How to write a scientific paper—Writing the methods section

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Azevedo, F. Canário-Almeida, J. Almeida Fonseca, A. Costa-Pereira, J.C. Winck, V. Hespanhol" "autores" => array:6 [ 0 => array:4 [ "nombre" => "L.F." "apellidos" => "Azevedo" "email" => array:1 [ 0 => "lazevedo@med.up.pt" ] "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "*" "identificador" => "cor0005" ] ] ] 1 => array:3 [ "nombre" => "F." "apellidos" => "Canário-Almeida" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] ] ] 2 => array:3 [ "nombre" => "J." "apellidos" => "Almeida Fonseca" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] ] ] 3 => array:3 [ "nombre" => "A." "apellidos" => "Costa-Pereira" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] ] ] 4 => array:3 [ "nombre" => "J.C." "apellidos" => "Winck" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "b" "identificador" => "aff0010" ] 1 => array:2 [ "etiqueta" => "c" "identificador" => "aff0015" ] ] ] 5 => array:3 [ "nombre" => "V." "apellidos" => "Hespanhol" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "b" "identificador" => "aff0010" ] 1 => array:2 [ "etiqueta" => "c" "identificador" => "aff0015" ] ] ] ] "afiliaciones" => array:3 [ 0 => array:3 [ "entidad" => "Department of Health Information and Decision Sciences – HINDS (CIDES), Faculdade de Medicina, Universidade do Porto e Centro de Investigação em Tecnologias e Sistemas de Informação em Saúde, CINTESIS, Porto, Portugal" "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:3 [ "entidad" => "Department of Medicine, Faculdade de Medicina, Universidade do Porto, Hospital de São João, Porto, Portugal" "etiqueta" => "b" "identificador" => "aff0010" ] 2 => array:3 [ "entidad" => "Pulmonology Service, Hospital de São João, Porto, Portugal" "etiqueta" => "c" "identificador" => "aff0015" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "Corresponding author." ] ] ] ] "titulosAlternativos" => array:1 [ "pt" => array:1 [ "titulo" => "Como escrever um artigo científico – Estruturação e redacção da secção de métodos" ] ] "textoCompleto" => "IntroductionThe methods section is the most important part of a scientific paper because it provides the essential information that allows the reader to judge the validity of the results and conclusions of the study reported. Therefore, in this section the authors should provide a clear and precise description of how the study was performed and the rationale for the methodological choices and characteristics of the study design. This section should be written in a clear and concise manner, but should always present enough information so that: (1) the study could be replicated by other researchers, in order to evaluate the reproducibility of results (it should not be a step-by-step tutorial but should be a systematic and complete description of what was done), and (2) the readers are able to judge the validity of results and conclusions presented. This will typically be the first section to be written in a paper (although many times the last to be finalized after corrections and reviews of authors and reviewers), mainly because it should be already thought of and written as a part of the research protocol/proposal, prepared at the initial phase of the research work, and because it sets the stage for the results and conclusions presented in a paper. From a journalistic point of view this section should answer questions like “who”, “what”, “where”, “when”, “why” and “how”; and should do it having into account the difficult balance between completeness (sufficient details to allow replication and validity verification) and brevity (the impossibility of describing every technical detail and the need to strictly follow the guidelines/instructions for authors provided by journals and recommendations regarding word count limits). In this article, we describe and discuss some general recommendations that should help preparing the methods section of our manuscripts; and we propose a general structure and recommended content for this section. Because this section is so intimately related to the foundations of science, the scientific method and the study design, we begin by reviewing some general concepts and principles and then follow with the presentation of a proposal for its structure and content.Basics of the scientific method and study designAlthough many authors and schools of thought have different definitions and understandings regarding this matter, it is fair and generally consensual to say that science is a systematic endeavor aiming at the acquisition, development and updating of knowledge; and knowledge could be defined as a set of models that aim to describe, understand, explain, control and eventually modify the real world.<a class="elsevierStyleCrossRefs" href="#bib0005">1–4</a> The practice of science, generically named scientific research, aims to define questions and find answers that may contribute to the building of knowledge using the so called scientific method. The scientific method, particularly in the natural sciences, mainly relies on the empirical observation of the world, as objective and unbiased as possible, and the adequate use of processes as deduction, induction<a class="elsevierStyleCrossRefs" href="#bib0005">1–3</a> and abduction,<a class="elsevierStyleCrossRef" href="#bib0020">4</a> in order to generate and test hypothesis and subsequently models that allow us to describe, explain and modify the real world. In general, scientific research questions look for the estimation of population parameters or the confirmation or discovery of relationships, eventually of a causal nature, among objects (physical matter, processes, interventions or concepts). In a quantitative research paradigm the objects are regarded as variables that researchers may measure and/or control, and a variable is simply defined as a characteristic that may vary among the subjects or units of observation under study. When estimating parameters or assessing relationships, that quantitatively translate the answer to the research question, researchers are interested in minimizing random errors and systematic errors. Random errors are associated with usual sources of variability, generally measurement and sampling variability, that may affect the estimation<a class="elsevierStyleCrossRefs" href="#bib0025">5,6</a>; and they directly affect the precision of the parameter estimates presented. Systematic errors or bias are associated with phenomena that may affect the validity of the estimation and conclusions.<a class="elsevierStyleCrossRefs" href="#bib0025">5–9</a> Validity simply refers to the ability that a method or a study has to measure or estimate what it really intends to measure or estimate. Thus, validity refers to the credibility of the study design and results and the degree to which these results can be applied to the general population of interest.<a class="elsevierStyleCrossRefs" href="#bib0025">5,6</a> Internal validity refers to the credibility of the study itself and is determined by the degree to which study conclusions correctly depict the truth in the study.<a class="elsevierStyleCrossRefs" href="#bib0025">5,6</a> External validity refers to whether the results of a study can be generalized to a larger population of interest.<a class="elsevierStyleCrossRefs" href="#bib0025">5,6</a>Random errors are controlled or dealt with mainly by an adequate choice of measurement methods and instruments, an adequate choice of sampling methods and sample size and an adequate use of statistical methods for data analysis and presentation.<a class="elsevierStyleCrossRef" href="#bib0025">5</a>Systematic errors or bias are the main target when selecting a study design and defining the methodological characteristics of the study. The methods and strategies to control and minimize systematic errors are the main factors affecting the validity of the study results and conclusions.<a class="elsevierStyleCrossRefs" href="#bib0025">5,7–11</a> There are many different sources of systematic errors that should be considered when designing, implementing and reporting a research study and many authors have presented different proposals for their definition and systematization.<a class="elsevierStyleCrossRefs" href="#bib0045">9–11</a> It is classical though, particularly in the field of clinical or epidemiological research, to classify systematic errors or bias in three main categories<a class="elsevierStyleCrossRefs" href="#bib0025">5,6,9</a>: selection bias, information bias and confounding.<ul class="elsevierStyleList" id="lis0005"><li class="elsevierStyleListItem" id="lsti0005">1.Selection bias refers to systematic errors associated with the selection of study participants or units of observation.<a class="elsevierStyleCrossRefs" href="#bib0025">5,6,12</a></li><li class="elsevierStyleListItem" id="lsti0010">2.Information bias refers to systematic errors associated with the measurement or classification of study variables (typically classified into three main groups: dependent variables – outcome or response variables; independent variables – predictive, exposure or intervention variables and confounding variables – confounders or extraneous factors) and the methods and instruments used for that purpose.<a class="elsevierStyleCrossRefs" href="#bib0025">5,6</a></li><li class="elsevierStyleListItem" id="lsti0015">3.Confounding refers to a phenomenon where certain variables (confounding variables) that are associated simultaneously with the outcome and predictors under study interfere with the valid estimation of the true predictor's effect on the outcome.<a class="elsevierStyleCrossRefs" href="#bib0025">5–8</a> Confounding is, of course, of particular importance in causal research. Sadly, the real world is much more complex than we would like, so simple, unambiguous, direct relationships between objects can be difficult to ascertain. Thus, in causal research, the validity of a study is judged by the degree to which its outcomes can be attributed to manipulation of independent variables and not to the effects of confounding variables. It is important to emphasize that confounding variables are hardly ever fully controlled; and in many instances the influence of those variables is not fully appreciated by researchers. Therefore, the study design must be defined so as to control as many extraneous factors as possible, so that any potential cause-and-effect relationship between two objects can be judged validly.</li></ul>The study design is the overall plan for addressing the aims or purpose of the study and answering the research questions or testing the study hypotheses.<a class="elsevierStyleCrossRef" href="#bib0025">5</a> It generally defines the way researchers should look at the world, when seeking empirical evidence regarding the research question, in order to avoid fallacies and systematic errors usually associated with the unstructured or unscientific empirical observation of the world. The selection of a research design should be driven first by the research purpose (questions) and second by feasibility issues. Questions to consider when selecting a study design include<a class="elsevierStyleCrossRef" href="#bib0025">5</a>:<ul class="elsevierStyleList" id="lis0010"><li class="elsevierStyleListItem" id="lsti0020">1.How much do we known about the topic under study?</li><li class="elsevierStyleListItem" id="lsti0025">2.Will there be an intervention? Will all subjects get it? Do we control who gets the intervention? Is it feasible to randomly assign subjects to the intervention?</li><li class="elsevierStyleListItem" id="lsti0030">3.How often and when will data be collected from subjects?</li><li class="elsevierStyleListItem" id="lsti0035">4.How can factors that may potentially interfere in the relationship between predictors and outcomes be minimized or controlled?</li></ul>The answer to these questions comprises the justification for the study design selected and should be always succinctly explained. The different study designs and methodological characteristics will affect the validity of the study results. Thus, although a more thorough description of the various types of study designs is beyond the focus of this article, it is very important that researchers know the basics regarding study design and are able to adequately describe it.<a class="elsevierStyleCrossRef" href="#bib0025">5</a>In conclusion, the choice of the most appropriate study design and the adequate planning and implementation of the research methods are the foundations of good research work; and their main purpose is exactly to minimize random and systematic errors that may affect the answer to the research question. Thus, the methods section in a paper should essentially report in a concise but complete manner how well random and systematic errors were considered and controlled by researchers, so that the validity and precision of the estimates that quantitatively translate the answer to the research question may be judged by the readers.Structure and content of the methods sectionIn most journals the “Methods” section is designated as “Materials and Methods” or “Participants and Methods” emphasizing the two main areas that should be addressed. First, “Materials” refers to what was observed (e.g.: humans, animals, tissues, cells, etc.) and the interventions (e.g.: drugs, devices, etc.) and instruments (e.g.: measurement technologies) used in the study. Second, “Methods” refers to how subjects or objects were selected, manipulated or observed to answer the research question, how measurements were performed and how the data were analyzed.<a class="elsevierStyleCrossRefs" href="#bib0065">13–15</a>The writing of the Methods section should be clear and orderly to avoid confusion and ambiguity. The methods section should ideally be structured in a set of subsections describing its main content.<a class="elsevierStyleCrossRefs" href="#bib0065">13–15</a> A possible structure is proposed along this paper including the following subsections<a class="elsevierStyleCrossRefs" href="#bib0065">13–15</a>:<ul class="elsevierStyleList" id="lis0015"><li class="elsevierStyleListItem" id="lsti0040">1.Study design;</li><li class="elsevierStyleListItem" id="lsti0045">2.Selection of participants – selection criteria and selection methods;</li><li class="elsevierStyleListItem" id="lsti0050">3.Data collection – variables, methods and instruments and</li><li class="elsevierStyleListItem" id="lsti0055">4.Data analysis.</li></ul>Each one of these subsections could have additional subheadings as appropriate. It should be stressed that the proposal that follows is deemed to be broad and general in scope, and should always be completed with some other specific indications in the context of the particular type of study reported. To master the writing of the methods section it is important (1) to look at many other examples of methods sections in articles with similar scopes and aims as ours and (2) to use some of the many reporting guidelines that are available for the most common study types<a class="elsevierStyleCrossRefs" href="#bib0080">16,17</a> (e.g.: CONSORT for clinical trials<a class="elsevierStyleCrossRef" href="#bib0090">18</a>; STROBE for observational studies<a class="elsevierStyleCrossRef" href="#bib0095">19</a>; STARD for diagnostic research<a class="elsevierStyleCrossRef" href="#bib0100">20</a>; PRISMA for systematic reviews and meta-analysis<a class="elsevierStyleCrossRef" href="#bib0105">21</a>; etc.).The writing of Methods section should be direct, precise and in the past tense. Complex sentence structures should be avoided, as well as descriptions of unimportant aspects or too much details. In general the description of procedures and measurements should be organized chronologically; and, in each subsection, content should be organized from the most to the least important.<a class="elsevierStyleCrossRefs" href="#bib0065">13–15</a>Study designTypically the Methods section begins with a general paragraph describing the study design and the main methodological characteristics of the study, establishing the setting for the description of participants selection and data collection. In the context of clinical and epidemiological research, the classical classifications and characteristics most frequently considered when describing and systematizing the study design are<a class="elsevierStyleCrossRefs" href="#bib0025">5,6</a>:<ul class="elsevierStyleList" id="lis0020"><li class="elsevierStyleListItem" id="lsti0060">1.The definition of the descriptive vs. analytical nature of the study. Descriptive studies aim to describe population parameters or associations (hypothesis generating studies) and analytical studies try to answer causal questions (hypothesis testing studies).</li><li class="elsevierStyleListItem" id="lsti0065">2.Reporting the comparative vs. non-comparative nature of the study (is there a group comparison?).</li><li class="elsevierStyleListItem" id="lsti0070">3.Reporting the interventional vs. non-interventional nature of the study (is there an intervention to be evaluated?).</li><li class="elsevierStyleListItem" id="lsti0075">4.Reporting the existence of control over the interventions or factors under study and the existence of randomization. These two criteria allow the classification of studies into three main groups: experimental, quasi-experimental and observational studies. In experimental studies the researchers have direct control over the interventions or factors under study and allocate them to the subjects using a random process – randomization (e.g.: randomized controlled trials). In quasi-experimental studies researchers control the interventions or factors under study but they do not implement randomization procedures (e.g.: non-randomized clinical trials). In observational studies researchers are unable to directly control the interventions or factors under study and do not implement randomization procedures (e.g.: cohort studies, case–control studies, etc.).</li><li class="elsevierStyleListItem" id="lsti0080">5.Reporting the type of randomization procedures when those are implemented (e.g.: parallel groups vs. cross-over, balanced vs. unbalanced groups, complete vs. incomplete designs, factorial designs, etc.).</li><li class="elsevierStyleListItem" id="lsti0085">6.Reporting, in observational studies, if the participant selection was based on the predictor variables (cohort studies) or the outcomes (case–control studies) under assessment.</li><li class="elsevierStyleListItem" id="lsti0090">7.Reporting the cross-sectional vs. longitudinal nature of the study (having into account the existence of an assumed or factual follow-up period).</li><li class="elsevierStyleListItem" id="lsti0095">8.Reporting the prospective vs. retrospective nature of the study (having into account the point in time where the predictors are measured in relation to the outcomes or the point in time where recruitment of participants starts).</li></ul>The different study designs and methodological characteristics will affect the validity of the study results. Thus, although a more thorough description of the various types of study designs is beyond the focus of this article, it is very important that researchers know the basics regarding study design and are able to adequately describe it.<a class="elsevierStyleCrossRefs" href="#bib0025">5,6</a>Ethical considerationsA clear presentation of the ethical considerations is mandatory in all animal or human studies. Although it may not be a subsection by itself, as an alternative it could be a part of the subsection “Selection of participants”, this presentation is important and should take into account the international guidelines on good clinical and research practices.<a class="elsevierStyleCrossRefs" href="#bib0110">22–25</a> In general, references regarding the informed consent obtained on human subjects and the approval of the research protocol by an ethical committee or an institutional review board should be presented. In Portugal, submission of the research protocol to the national data protection agency (Comissão Nacional de Protecção de Dados – CNPD) could also be necessary.<a class="elsevierStyleCrossRef" href="#bib0130">26</a> In the case of experimental studies on human subjects (clinical trials) the approval by a national ethical committee is legally mandatory.<a class="elsevierStyleCrossRef" href="#bib0135">27</a> If the study raises any additional specific ethical concern this should be adequately described (e.g.: studies on especially vulnerable subgroups).<a class="elsevierStyleCrossRef" href="#bib0140">28</a> Although not directly linked with the ethical considerations, it is important to stress that for experimental studies on human subjects (clinical trials) authors should give an appropriate reference to the registration of the study protocol on a clinical trials registration database.<a class="elsevierStyleCrossRef" href="#bib0145">29</a> Most journals today only accept for publication clinical trials previously registered.Selection of participants – selection criteria and selection methodsThe participants selected for inclusion in a study and the methods of selection will ultimately determine the limits that are placed on the generalizations that can be made regarding the study results. Judging the external validity of a study (i.e.: assessing to whom the study results may be applied) requires that a comprehensive description of the selection criteria and selection methods and descriptive data regarding the study sample be provided.<a class="elsevierStyleCrossRef" href="#bib0025">5</a>This subsection could begin with a brief presentation of the study setting, in order to contextualize the study presented. This should include the setting, location(s) and relevant dates of the study, indicating, for example, periods of recruitment, exposures or interventions, follow-up and data collection.After presentation of the study setting, there are three major topics that should be addressed in this subsection<a class="elsevierStyleCrossRef" href="#bib0025">5</a>:<ul class="elsevierStyleList" id="lis0025"><li class="elsevierStyleListItem" id="lsti0100">1.Specification criteria (selection criteria),</li><li class="elsevierStyleListItem" id="lsti0105">2.Methods for selection of participants (sampling) and</li><li class="elsevierStyleListItem" id="lsti0110">3.Recruitment process.</li></ul>First, a full and thorough description of the criteria for selection of participants – inclusion and exclusion criteria – should be presented and its rationale explained.<a class="elsevierStyleCrossRef" href="#bib0025">5</a> Authors should clearly indicate the target population and the accessible population in the study.A general description of the characteristics of participants is also important and could also be added, but this is a matter of discussion because many authors and journals believe this should be a part of the results section. For human subjects it is important to describe general demographic and clinical characteristics. For animal subjects it is important to adequately describe the species, weight, strain, sex, age and eventually details regarding special characteristics or living conditions.In studies involving animal models or mechanical models, a detailed description must be provided regarding the preparations made prior to beginning the experimental protocol. In addition, all aspects of animal or tissue preparation required prior to initiation of the research protocol must be described in detail. With any animal preparation or mechanical model there must be enough detail provided so that the reader can duplicate it or evaluate its relevance.Second, the methods for selection of participants should be carefully explained. This should include an account of how the subjects were identified and how they were sampled from the target population under study (sampling methods).<a class="elsevierStyleCrossRef" href="#bib0025">5</a> When selecting subjects from a target population, probabilistic sampling methods (random samples) are preferred because they more appropriately guarantee representativeness of the sample. When reporting probabilistic sampling methods authors should describe the sampling frame, the instruments used for the random selection process and, if appropriate, the use of complex sampling methods with stratification or clustering and weighting procedures. Although probabilistic methods are preferred, for practical reasons, non-probabilistic sampling methods (non-random samples) are much more common (e.g.: consecutive samples, convenience samples, systematic samples, etc.). Although non-random sampling methods do not guarantee the representativeness of the sample, they do not necessarily prevent us from validly answering the research question. It should be stressed that non-probabilistic sampling methods are in many instances appropriate. For example, most of randomized clinical trials do not select participants through a random sampling process, and they still are able to appropriately answer the causal question regarding efficacy of therapeutic interventions, relying on the random allocation of alternative interventions (randomization), even when the participants selection was non-random. When answering causal questions the crucial point is to be able to generate comparable study groups and make fair comparisons (equipoise) between groups and, at least in this case, representativeness of the sample, although also important, is regarded as secondary.The third topic to be addressed in the “Selection of participants” subsection is the recruitment process.<a class="elsevierStyleCrossRef" href="#bib0025">5</a> Authors should describe in detail how recruitment was undertaken and particularly how effective it was. They should present a complete account of the subjects selected from the sampling frame, those that accepted and those that refused to participate, ideally with a summary of reasons for refusal and a brief characterization of the subjects refusing to participate. Methods implemented to reduce refusal rate should also be described.In addition to the three main topics described above, in comparative studies it is also important to describe some particular methods of group allocation and/or participant selection that aim to improve their comparability. In experimental studies (randomized controlled trials) a thorough account of randomization procedures should be presented including<a class="elsevierStyleCrossRef" href="#bib0090">18</a>: methods used to generate the random allocation sequence, details on any restrictions to randomization (stratification or blocking), methods for allocation concealment and implementation details of the randomization process. In this type of studies this is often an independent subsection of the methods section. In observational studies (e.g.: cohort studies, case–control studies, etc.) authors should describe and give details regarding the implementation of methods such as stratification and matching, whenever those are used.<a class="elsevierStyleCrossRef" href="#bib0095">19</a>Finally, in longitudinal studies a full description should be presented of the follow-up procedures implemented, often as a separate subsection. This should include a description of the completeness and quality of participants follow-up (number and reasons for losses of follow-up, drop-outs, drop-ins, etc.) and, in comparative studies, methods implemented to guarantee equality of follow-up conditions, for example, blinding of researchers or healthcare professionals responsible for the follow-up and the adequate control of co-interventions.<a class="elsevierStyleCrossRef" href="#bib0025">5</a>Data collection – variables, methods and instrumentsThe next step in the methods section is to describe the data collection process, including the variables measured and the methods and instruments used for their measurement. In a quantitative research paradigm the adequate and unbiased empirical observation and measurement of variables is the cornerstone of the scientific method; thus this subsection deserves careful and thorough consideration.Variables are observable objects that are measured, manipulated, or controlled in a study. Variables can be concrete concepts, such as height, weight, and blood pressure, or abstract concepts, such as stress, coping or quality-of-life. Variables should be operationally defined by indicating how the variable will be observed and measured in the study. Abstract variables (constructs), such as quality-of-life or stress, should be defined both conceptually and operationally. The conceptual definition explains the theoretical meaning of the variable, while the operational definition specifies how it will be measured. For example, when measuring quality-of-life, researchers could present a brief conceptual definition of the construct, but should always add details regarding its operational definition, by indicating the model and instrument applied to measure quality-of-life, for example, by using the SF-36 health questionnaire.In general, the variables in a study could be classified in one of four major groups<a class="elsevierStyleCrossRef" href="#bib0025">5</a>:<ul class="elsevierStyleList" id="lis0030"><li class="elsevierStyleListItem" id="lsti0115">1.Predictor (independent, exposure or intervention) variables,</li><li class="elsevierStyleListItem" id="lsti0120">2.Outcome (dependent) variables,</li><li class="elsevierStyleListItem" id="lsti0125">3.Confounding (extraneous) variables or</li><li class="elsevierStyleListItem" id="lsti0130">4.Interaction (effect modifier) variables.</li></ul>When describing the variables in a study the authors do not need to give a full and complete description of all variables measured, however the main predictors and all outcome variables should be described with sufficient detail as to allow replication and assessment of the quality of the measurement or classification. For these variables a full account of their conceptual definition, operational definition, classification or diagnostic criteria applied (if appropriate), methods of measurement, instruments used and a brief description of the evidence regarding their validity and reproducibility should be presented. This detailed presentation should be extended to any other variable of particular importance for the study or with uncommon measurement procedures or instruments.<a class="elsevierStyleCrossRef" href="#bib0025">5</a>For those variables where it is deemed necessary, the description of the measurement methods and instruments should include the manufacturer and model, calibration procedures, evidence regarding the validity and reproducibility of instruments and how measurements were made. The instruments used to measure variables must be reliable and valid. Validity is the extent to which an instrument measures what it reports to measure. Reliability refers to the consistency with which an instrument measures a study variable. Internal consistency (e.g.: Cronbach's alpha), test–retest reliability, and inter-rater reliability are examples of methods used to assess the reliability of an instrument, particularly in the context of abstract concepts (constructs) measurement. These psychometric or clinimetric properties of instruments determine the overall study validity. It is important to select and describe instruments that have established reliability and validity in the population that the investigator plans to study (e.g.: older adults or children) and use instruments that are properly translated, adapted and validated for the study population. Although not formally prohibited, the use of instruments that were not previously submitted to an adequate translation, adaptation and validation process impose important limitations to the credibility and validity of the study results and its use should be always indicated.Finally, particular methods to control bias associated with the measurement or classification of study variables should be described. For example, implementation of blinding procedures for participants and for researchers collecting data (especially outcomes measurement) should be indicated and explained.Data analysisIn the last part of the methods section authors should describe with sufficient detail the statistical methods used for the study data analysis, including descriptive statistics and methods for statistical inference.<a class="elsevierStyleCrossRef" href="#bib0025">5</a> This presentation should have a close link to the aims of the study and should precisely establish what will be presented in the results section.This subsection should include an initial general sentence regarding the descriptive statistics used, having into account the main types of variables analyzed (e.g.: means or medians, standard deviations or quantile ranges, absolute frequencies and proportions, etc.). Next, a brief description of inferential methods used should follow, including the indication of confidence intervals calculated, an account of the statistical hypothesis tests applied and the indication of any uni- or multi-variable regression or modeling procedures employed. A special note should be added regarding the use of confidence intervals as the best method to express the precision of parameter estimates presented in a study. Their presentation is increasingly deemed essential and they are more informative than the classical p-values of hypothesis testing.Some additional aspects regarding the statistical analysis should be addressed whenever it is deemed necessary, taking into account the main study aims:<ul class="elsevierStyleList" id="lis0035"><li class="elsevierStyleListItem" id="lsti0135">1.Describing the specifics regarding methods used to account for confounding in observational studies (e.g.: multi-variable regression methods for effect measures adjustment, propensity scores,<a class="elsevierStyleCrossRef" href="#bib0150">30</a> causality modeling using directed acyclic graphs and structural models,<a class="elsevierStyleCrossRefs" href="#bib0155">31–33</a> etc.).</li><li class="elsevierStyleListItem" id="lsti0140">2.Describing methods to examine subgroups, interactions and effect modification in experimental and observational studies.<a class="elsevierStyleCrossRef" href="#bib0170">34</a></li><li class="elsevierStyleListItem" id="lsti0145">3.Describing any interim analysis, stopping rules and adjustments that may be used, particularly in experimental studies.<a class="elsevierStyleCrossRef" href="#bib0090">18</a></li><li class="elsevierStyleListItem" id="lsti0150">4.Describing any particular adjustments made taking into account the sampling methods and weighting procedures used.</li><li class="elsevierStyleListItem" id="lsti0155">5.Describing methods used to account for missing data.</li><li class="elsevierStyleListItem" id="lsti0160">6.Describing methods used for sensitivity analysis.</li></ul>Also important in this section is to describe the estimates and explanation of methods for the sample size and power determination.<a class="elsevierStyleCrossRef" href="#bib0025">5</a> The determination of the sample size before the beginning of the study is crucial to ensure the appropriate power of hypothesis testing and the precision of parameter estimates. In many instances, particularly in observational studies, a formal sample size calculation is not possible for practical reasons (for example, the study sample is assembled retrospectively or is already fixed before the beginning of the study). Even in these situations it is advisable to present results of a formal power analysis, in order to give an indication of the power of hypothesis tests and the magnitude of differences that researchers are able to detect in those settings. Some authors prefer to incorporate the paragraph regarding sample size determination as a part of the “Selection of participants” subsection.Finally, an indication of the level of type I errors (alpha level) assumed in all statistical hypothesis testing (usually, a 5% alpha level is assumed) and an indication of the statistical software package used for analysis (with a reference) should be presented in this subsection.ConclusionThe methods section is the most important part of a scientific paper because it provides the crucial information that allows the reader to judge the validity of the results and conclusions of the study reported. Therefore, in this section, the authors should provide a clear and precise description of how the study was performed and the rationale for the methodological choices and characteristics of the study design. A clear and precise account of how a study was performed, and the rationale for specific study methods are the crucial aspects of scientific writing. A proposal for the structure and content of the methods has been presented and explored giving a general guidance for the writing and assessment of the quality of this section and of the study reported. We hope that somehow this paper may comprise a useful tool for authors, reviewers and readers of scientific papers, and in particular those of the Portuguese Journal of Pulmonology (Revista Portuguesa de Pneumologia)." 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Thematic series

How to write a scientific paper—Writing the methods section

Como escrever um artigo científico – Estruturação e redacção da secção de métodos

L.F. Azevedoa,

Corresponding author

lazevedo@med.up.pt

Corresponding author.

, F. Canário-Almeidaa, J. Almeida Fonsecaa, A. Costa-Pereiraa, J.C. Winckb,c, V. Hespanholb,c

a Department of Health Information and Decision Sciences – HINDS (CIDES), Faculdade de Medicina, Universidade do Porto e Centro de Investigação em Tecnologias e Sistemas de Informação em Saúde, CINTESIS, Porto, Portugal

b Department of Medicine, Faculdade de Medicina, Universidade do Porto, Hospital de São João, Porto, Portugal

c Pulmonology Service, Hospital de São João, Porto, Portugal

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            "entidad" => "Department of Health Information and Decision Sciences &#8211; HINDS &#40;CIDES&#41;&#44; Faculdade de Medicina&#44; Universidade do Porto e Centro de Investiga&#231;&#227;o em Tecnologias e Sistemas de Informa&#231;&#227;o em Sa&#250;de&#44; CINTESIS&#44; Porto&#44; Portugal"
            "etiqueta" => "a"
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          1 => array:3 [
            "entidad" => "Department of Medicine&#44; Faculdade de Medicina&#44; Universidade do Porto&#44; Hospital de S&#227;o Jo&#227;o&#44; Porto&#44; Portugal"
            "etiqueta" => "b"
            "identificador" => "aff0010"
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            "entidad" => "Pulmonology Service&#44; Hospital de S&#227;o Jo&#227;o&#44; Porto&#44; Portugal"
            "etiqueta" => "c"
            "identificador" => "aff0015"
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        "correspondencia" => array:1 [
          0 => array:3 [
            "identificador" => "cor0005"
            "etiqueta" => "&#8270;"
            "correspondencia" => "Corresponding author&#46;"
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    "titulosAlternativos" => array:1 [
      "pt" => array:1 [
        "titulo" => "Como escrever um artigo cient&#237;fico &#8211; Estrutura&#231;&#227;o e redac&#231;&#227;o da sec&#231;&#227;o de m&#233;todos"
      ]
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    "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">The methods section is the most important part of a scientific paper because it provides the essential information that allows the reader to judge the validity of the results and conclusions of the study reported&#46; Therefore&#44; in this section the authors should provide a clear and precise description of how the study was performed and the rationale for the methodological choices and characteristics of the study design&#46; This section should be written in a clear and concise manner&#44; but should always present enough information so that&#58; &#40;1&#41; the study could be replicated by other researchers&#44; in order to evaluate the reproducibility of results &#40;it should not be a step-by-step tutorial but should be a systematic and complete description of what was done&#41;&#44; and &#40;2&#41; the readers are able to judge the validity of results and conclusions presented&#46; This will typically be the first section to be written in a paper &#40;although many times the last to be finalized after corrections and reviews of authors and reviewers&#41;&#44; mainly because it should be already thought of and written as a part of the research protocol&#47;proposal&#44; prepared at the initial phase of the research work&#44; and because it sets the stage for the results and conclusions presented in a paper&#46; From a journalistic point of view this section should answer questions like &#8220;who&#8221;&#44; &#8220;what&#8221;&#44; &#8220;where&#8221;&#44; &#8220;when&#8221;&#44; &#8220;why&#8221; and &#8220;how&#8221;&#59; and should do it having into account the difficult balance between completeness &#40;sufficient details to allow replication and validity verification&#41; and brevity &#40;the impossibility of describing every technical detail and the need to strictly follow the guidelines&#47;instructions for authors provided by journals and recommendations regarding word count limits&#41;&#46; In this article&#44; we describe and discuss some general recommendations that should help preparing the methods section of our manuscripts&#59; and we propose a general structure and recommended content for this section&#46; Because this section is so intimately related to the foundations of science&#44; the scientific method and the study design&#44; we begin by reviewing some general concepts and principles and then follow with the presentation of a proposal for its structure and content&#46;</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle">Basics of the scientific method and study design</span><p id="par0010" class="elsevierStylePara elsevierViewall">Although many authors and schools of thought have different definitions and understandings regarding this matter&#44; it is fair and generally consensual to say that science is a systematic endeavor aiming at the acquisition&#44; development and updating of knowledge&#59; and knowledge could be defined as a set of models that aim to describe&#44; understand&#44; explain&#44; control and eventually modify the real world&#46;<a class="elsevierStyleCrossRefs" href="#bib0005"><span class="elsevierStyleSup">1&#8211;4</span></a> The practice of science&#44; generically named scientific research&#44; aims to define questions and find answers that may contribute to the building of knowledge using the so called scientific method&#46; The scientific method&#44; particularly in the natural sciences&#44; mainly relies on the empirical observation of the world&#44; as objective and unbiased as possible&#44; and the adequate use of processes as deduction&#44; induction<a class="elsevierStyleCrossRefs" href="#bib0005"><span class="elsevierStyleSup">1&#8211;3</span></a> and abduction&#44;<a class="elsevierStyleCrossRef" href="#bib0020"><span class="elsevierStyleSup">4</span></a> in order to generate and test hypothesis and subsequently models that allow us to describe&#44; explain and modify the real world&#46; In general&#44; scientific research questions look for the estimation of population parameters or the confirmation or discovery of relationships&#44; eventually of a causal nature&#44; among objects &#40;physical matter&#44; processes&#44; interventions or concepts&#41;&#46; In a quantitative research paradigm the objects are regarded as variables that researchers may measure and&#47;or control&#44; and a variable is simply defined as a characteristic that may vary among the subjects or units of observation under study&#46; When estimating parameters or assessing relationships&#44; that quantitatively translate the answer to the research question&#44; researchers are interested in minimizing random errors and systematic errors&#46; Random errors are associated with usual sources of variability&#44; generally measurement and sampling variability&#44; that may affect the estimation<a class="elsevierStyleCrossRefs" href="#bib0025"><span class="elsevierStyleSup">5&#44;6</span></a>&#59; and they directly affect the precision of the parameter estimates presented&#46; Systematic errors or bias are associated with phenomena that may affect the validity of the estimation and conclusions&#46;<a class="elsevierStyleCrossRefs" href="#bib0025"><span class="elsevierStyleSup">5&#8211;9</span></a> Validity simply refers to the ability that a method or a study has to measure or estimate what it really intends to measure or estimate&#46; Thus&#44; validity refers to the credibility of the study design and results and the degree to which these results can be applied to the general population of interest&#46;<a class="elsevierStyleCrossRefs" href="#bib0025"><span class="elsevierStyleSup">5&#44;6</span></a> Internal validity refers to the credibility of the study itself and is determined by the degree to which study conclusions correctly depict the truth in the study&#46;<a class="elsevierStyleCrossRefs" href="#bib0025"><span class="elsevierStyleSup">5&#44;6</span></a> External validity refers to whether the results of a study can be generalized to a larger population of interest&#46;<a class="elsevierStyleCrossRefs" href="#bib0025"><span class="elsevierStyleSup">5&#44;6</span></a></p><p id="par0015" class="elsevierStylePara elsevierViewall">Random errors are controlled or dealt with mainly by an adequate choice of measurement methods and instruments&#44; an adequate choice of sampling methods and sample size and an adequate use of statistical methods for data analysis and presentation&#46;<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a></p><p id="par0020" class="elsevierStylePara elsevierViewall">Systematic errors or bias are the main target when selecting a study design and defining the methodological characteristics of the study&#46; The methods and strategies to control and minimize systematic errors are the main factors affecting the validity of the study results and conclusions&#46;<a class="elsevierStyleCrossRefs" href="#bib0025"><span class="elsevierStyleSup">5&#44;7&#8211;11</span></a> There are many different sources of systematic errors that should be considered when designing&#44; implementing and reporting a research study and many authors have presented different proposals for their definition and systematization&#46;<a class="elsevierStyleCrossRefs" href="#bib0045"><span class="elsevierStyleSup">9&#8211;11</span></a> It is classical though&#44; particularly in the field of clinical or epidemiological research&#44; to classify systematic errors or bias in three main categories<a class="elsevierStyleCrossRefs" href="#bib0025"><span class="elsevierStyleSup">5&#44;6&#44;9</span></a>&#58; selection bias&#44; information bias and confounding&#46;<ul class="elsevierStyleList" id="lis0005"><li class="elsevierStyleListItem" id="lsti0005"><span class="elsevierStyleLabel">1&#46;</span><p id="par0025" class="elsevierStylePara elsevierViewall">Selection bias refers to systematic errors associated with the selection of study participants or units of observation&#46;<a class="elsevierStyleCrossRefs" href="#bib0025"><span class="elsevierStyleSup">5&#44;6&#44;12</span></a></p></li><li class="elsevierStyleListItem" id="lsti0010"><span class="elsevierStyleLabel">2&#46;</span><p id="par0030" class="elsevierStylePara elsevierViewall">Information bias refers to systematic errors associated with the measurement or classification of study variables &#40;typically classified into three main groups&#58; dependent variables &#8211; outcome or response variables&#59; independent variables &#8211; predictive&#44; exposure or intervention variables and confounding variables &#8211; confounders or extraneous factors&#41; and the methods and instruments used for that purpose&#46;<a class="elsevierStyleCrossRefs" href="#bib0025"><span class="elsevierStyleSup">5&#44;6</span></a></p></li><li class="elsevierStyleListItem" id="lsti0015"><span class="elsevierStyleLabel">3&#46;</span><p id="par0035" class="elsevierStylePara elsevierViewall">Confounding refers to a phenomenon where certain variables &#40;confounding variables&#41; that are associated simultaneously with the outcome and predictors under study interfere with the valid estimation of the true predictor&#39;s effect on the outcome&#46;<a class="elsevierStyleCrossRefs" href="#bib0025"><span class="elsevierStyleSup">5&#8211;8</span></a> Confounding is&#44; of course&#44; of particular importance in causal research&#46; Sadly&#44; the real world is much more complex than we would like&#44; so simple&#44; unambiguous&#44; direct relationships between objects can be difficult to ascertain&#46; Thus&#44; in causal research&#44; the validity of a study is judged by the degree to which its outcomes can be attributed to manipulation of independent variables and not to the effects of confounding variables&#46; It is important to emphasize that confounding variables are hardly ever fully controlled&#59; and in many instances the influence of those variables is not fully appreciated by researchers&#46; Therefore&#44; the study design must be defined so as to control as many extraneous factors as possible&#44; so that any potential cause-and-effect relationship between two objects can be judged validly&#46;</p></li></ul></p><p id="par0040" class="elsevierStylePara elsevierViewall">The study design is the overall plan for addressing the aims or purpose of the study and answering the research questions or testing the study hypotheses&#46;<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a> It generally defines the way researchers should look at the world&#44; when seeking empirical evidence regarding the research question&#44; in order to avoid fallacies and systematic errors usually associated with the unstructured or unscientific empirical observation of the world&#46; The selection of a research design should be driven first by the research purpose &#40;questions&#41; and second by feasibility issues&#46; Questions to consider when selecting a study design include<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a>&#58;<ul class="elsevierStyleList" id="lis0010"><li class="elsevierStyleListItem" id="lsti0020"><span class="elsevierStyleLabel">1&#46;</span><p id="par0045" class="elsevierStylePara elsevierViewall">How much do we known about the topic under study&#63;</p></li><li class="elsevierStyleListItem" id="lsti0025"><span class="elsevierStyleLabel">2&#46;</span><p id="par0050" class="elsevierStylePara elsevierViewall">Will there be an intervention&#63; Will all subjects get it&#63; Do we control who gets the intervention&#63; Is it feasible to randomly assign subjects to the intervention&#63;</p></li><li class="elsevierStyleListItem" id="lsti0030"><span class="elsevierStyleLabel">3&#46;</span><p id="par0055" class="elsevierStylePara elsevierViewall">How often and when will data be collected from subjects&#63;</p></li><li class="elsevierStyleListItem" id="lsti0035"><span class="elsevierStyleLabel">4&#46;</span><p id="par0060" class="elsevierStylePara elsevierViewall">How can factors that may potentially interfere in the relationship between predictors and outcomes be minimized or controlled&#63;</p></li></ul></p><p id="par0065" class="elsevierStylePara elsevierViewall">The answer to these questions comprises the justification for the study design selected and should be always succinctly explained&#46; The different study designs and methodological characteristics will affect the validity of the study results&#46; Thus&#44; although a more thorough description of the various types of study designs is beyond the focus of this article&#44; it is very important that researchers know the basics regarding study design and are able to adequately describe it&#46;<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a></p><p id="par0070" class="elsevierStylePara elsevierViewall">In conclusion&#44; the choice of the most appropriate study design and the adequate planning and implementation of the research methods are the foundations of good research work&#59; and their main purpose is exactly to minimize random and systematic errors that may affect the answer to the research question&#46; Thus&#44; the methods section in a paper should essentially report in a concise but complete manner how well random and systematic errors were considered and controlled by researchers&#44; so that the validity and precision of the estimates that quantitatively translate the answer to the research question may be judged by the readers&#46;</p></span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle">Structure and content of the methods section</span><p id="par0075" class="elsevierStylePara elsevierViewall">In most journals the &#8220;Methods&#8221; section is designated as &#8220;Materials and Methods&#8221; or &#8220;Participants and Methods&#8221; emphasizing the two main areas that should be addressed&#46; First&#44; &#8220;Materials&#8221; refers to what was observed &#40;e&#46;g&#46;&#58; humans&#44; animals&#44; tissues&#44; cells&#44; etc&#46;&#41; and the interventions &#40;e&#46;g&#46;&#58; drugs&#44; devices&#44; etc&#46;&#41; and instruments &#40;e&#46;g&#46;&#58; measurement technologies&#41; used in the study&#46; Second&#44; &#8220;Methods&#8221; refers to how subjects or objects were selected&#44; manipulated or observed to answer the research question&#44; how measurements were performed and how the data were analyzed&#46;<a class="elsevierStyleCrossRefs" href="#bib0065"><span class="elsevierStyleSup">13&#8211;15</span></a></p><p id="par0080" class="elsevierStylePara elsevierViewall">The writing of the Methods section should be clear and orderly to avoid confusion and ambiguity&#46; The methods section should ideally be structured in a set of subsections describing its main content&#46;<a class="elsevierStyleCrossRefs" href="#bib0065"><span class="elsevierStyleSup">13&#8211;15</span></a> A possible structure is proposed along this paper including the following subsections<a class="elsevierStyleCrossRefs" href="#bib0065"><span class="elsevierStyleSup">13&#8211;15</span></a>&#58;<ul class="elsevierStyleList" id="lis0015"><li class="elsevierStyleListItem" id="lsti0040"><span class="elsevierStyleLabel">1&#46;</span><p id="par0085" class="elsevierStylePara elsevierViewall">Study design&#59;</p></li><li class="elsevierStyleListItem" id="lsti0045"><span class="elsevierStyleLabel">2&#46;</span><p id="par0090" class="elsevierStylePara elsevierViewall">Selection of participants &#8211; selection criteria and selection methods&#59;</p></li><li class="elsevierStyleListItem" id="lsti0050"><span class="elsevierStyleLabel">3&#46;</span><p id="par0095" class="elsevierStylePara elsevierViewall">Data collection &#8211; variables&#44; methods and instruments and</p></li><li class="elsevierStyleListItem" id="lsti0055"><span class="elsevierStyleLabel">4&#46;</span><p id="par0100" class="elsevierStylePara elsevierViewall">Data analysis&#46;</p></li></ul></p><p id="par0105" class="elsevierStylePara elsevierViewall">Each one of these subsections could have additional subheadings as appropriate&#46; It should be stressed that the proposal that follows is deemed to be broad and general in scope&#44; and should always be completed with some other specific indications in the context of the particular type of study reported&#46; To master the writing of the methods section it is important &#40;1&#41; to look at many other examples of methods sections in articles with similar scopes and aims as ours and &#40;2&#41; to use some of the many reporting guidelines that are available for the most common study types<a class="elsevierStyleCrossRefs" href="#bib0080"><span class="elsevierStyleSup">16&#44;17</span></a> &#40;e&#46;g&#46;&#58; CONSORT for clinical trials<a class="elsevierStyleCrossRef" href="#bib0090"><span class="elsevierStyleSup">18</span></a>&#59; STROBE for observational studies<a class="elsevierStyleCrossRef" href="#bib0095"><span class="elsevierStyleSup">19</span></a>&#59; STARD for diagnostic research<a class="elsevierStyleCrossRef" href="#bib0100"><span class="elsevierStyleSup">20</span></a>&#59; PRISMA for systematic reviews and meta-analysis<a class="elsevierStyleCrossRef" href="#bib0105"><span class="elsevierStyleSup">21</span></a>&#59; etc&#46;&#41;&#46;</p><p id="par0110" class="elsevierStylePara elsevierViewall">The writing of Methods section should be direct&#44; precise and in the past tense&#46; Complex sentence structures should be avoided&#44; as well as descriptions of unimportant aspects or too much details&#46; In general the description of procedures and measurements should be organized chronologically&#59; and&#44; in each subsection&#44; content should be organized from the most to the least important&#46;<a class="elsevierStyleCrossRefs" href="#bib0065"><span class="elsevierStyleSup">13&#8211;15</span></a></p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle">Study design</span><p id="par0115" class="elsevierStylePara elsevierViewall">Typically the Methods section begins with a general paragraph describing the study design and the main methodological characteristics of the study&#44; establishing the setting for the description of participants selection and data collection&#46; In the context of clinical and epidemiological research&#44; the classical classifications and characteristics most frequently considered when describing and systematizing the study design are<a class="elsevierStyleCrossRefs" href="#bib0025"><span class="elsevierStyleSup">5&#44;6</span></a>&#58;<ul class="elsevierStyleList" id="lis0020"><li class="elsevierStyleListItem" id="lsti0060"><span class="elsevierStyleLabel">1&#46;</span><p id="par0120" class="elsevierStylePara elsevierViewall">The definition of the descriptive vs&#46; analytical nature of the study&#46; Descriptive studies aim to describe population parameters or associations &#40;hypothesis generating studies&#41; and analytical studies try to answer causal questions &#40;hypothesis testing studies&#41;&#46;</p></li><li class="elsevierStyleListItem" id="lsti0065"><span class="elsevierStyleLabel">2&#46;</span><p id="par0125" class="elsevierStylePara elsevierViewall">Reporting the comparative vs&#46; non-comparative nature of the study &#40;is there a group comparison&#63;&#41;&#46;</p></li><li class="elsevierStyleListItem" id="lsti0070"><span class="elsevierStyleLabel">3&#46;</span><p id="par0130" class="elsevierStylePara elsevierViewall">Reporting the interventional vs&#46; non-interventional nature of the study &#40;is there an intervention to be evaluated&#63;&#41;&#46;</p></li><li class="elsevierStyleListItem" id="lsti0075"><span class="elsevierStyleLabel">4&#46;</span><p id="par0135" class="elsevierStylePara elsevierViewall">Reporting the existence of control over the interventions or factors under study and the existence of randomization&#46; These two criteria allow the classification of studies into three main groups&#58; experimental&#44; quasi-experimental and observational studies&#46; In experimental studies the researchers have direct control over the interventions or factors under study and allocate them to the subjects using a random process &#8211; randomization &#40;e&#46;g&#46;&#58; randomized controlled trials&#41;&#46; In quasi-experimental studies researchers control the interventions or factors under study but they do not implement randomization procedures &#40;e&#46;g&#46;&#58; non-randomized clinical trials&#41;&#46; In observational studies researchers are unable to directly control the interventions or factors under study and do not implement randomization procedures &#40;e&#46;g&#46;&#58; cohort studies&#44; case&#8211;control studies&#44; etc&#46;&#41;&#46;</p></li><li class="elsevierStyleListItem" id="lsti0080"><span class="elsevierStyleLabel">5&#46;</span><p id="par0140" class="elsevierStylePara elsevierViewall">Reporting the type of randomization procedures when those are implemented &#40;e&#46;g&#46;&#58; parallel groups vs&#46; cross-over&#44; balanced vs&#46; unbalanced groups&#44; complete vs&#46; incomplete designs&#44; factorial designs&#44; etc&#46;&#41;&#46;</p></li><li class="elsevierStyleListItem" id="lsti0085"><span class="elsevierStyleLabel">6&#46;</span><p id="par0145" class="elsevierStylePara elsevierViewall">Reporting&#44; in observational studies&#44; if the participant selection was based on the predictor variables &#40;cohort studies&#41; or the outcomes &#40;case&#8211;control studies&#41; under assessment&#46;</p></li><li class="elsevierStyleListItem" id="lsti0090"><span class="elsevierStyleLabel">7&#46;</span><p id="par0150" class="elsevierStylePara elsevierViewall">Reporting the cross-sectional vs&#46; longitudinal nature of the study &#40;having into account the existence of an assumed or factual follow-up period&#41;&#46;</p></li><li class="elsevierStyleListItem" id="lsti0095"><span class="elsevierStyleLabel">8&#46;</span><p id="par0155" class="elsevierStylePara elsevierViewall">Reporting the prospective vs&#46; retrospective nature of the study &#40;having into account the point in time where the predictors are measured in relation to the outcomes or the point in time where recruitment of participants starts&#41;&#46;</p></li></ul></p><p id="par0160" class="elsevierStylePara elsevierViewall">The different study designs and methodological characteristics will affect the validity of the study results&#46; Thus&#44; although a more thorough description of the various types of study designs is beyond the focus of this article&#44; it is very important that researchers know the basics regarding study design and are able to adequately describe it&#46;<a class="elsevierStyleCrossRefs" href="#bib0025"><span class="elsevierStyleSup">5&#44;6</span></a></p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle">Ethical considerations</span><p id="par0165" class="elsevierStylePara elsevierViewall">A clear presentation of the ethical considerations is mandatory in all animal or human studies&#46; Although it may not be a subsection by itself&#44; as an alternative it could be a part of the subsection &#8220;Selection of participants&#8221;&#44; this presentation is important and should take into account the international guidelines on good clinical and research practices&#46;<a class="elsevierStyleCrossRefs" href="#bib0110"><span class="elsevierStyleSup">22&#8211;25</span></a> In general&#44; references regarding the informed consent obtained on human subjects and the approval of the research protocol by an ethical committee or an institutional review board should be presented&#46; In Portugal&#44; submission of the research protocol to the national data protection agency &#40;Comiss&#227;o Nacional de Protec&#231;&#227;o de Dados &#8211; CNPD&#41; could also be necessary&#46;<a class="elsevierStyleCrossRef" href="#bib0130"><span class="elsevierStyleSup">26</span></a> In the case of experimental studies on human subjects &#40;clinical trials&#41; the approval by a national ethical committee is legally mandatory&#46;<a class="elsevierStyleCrossRef" href="#bib0135"><span class="elsevierStyleSup">27</span></a> If the study raises any additional specific ethical concern this should be adequately described &#40;e&#46;g&#46;&#58; studies on especially vulnerable subgroups&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0140"><span class="elsevierStyleSup">28</span></a> Although not directly linked with the ethical considerations&#44; it is important to stress that for experimental studies on human subjects &#40;clinical trials&#41; authors should give an appropriate reference to the registration of the study protocol on a clinical trials registration database&#46;<a class="elsevierStyleCrossRef" href="#bib0145"><span class="elsevierStyleSup">29</span></a> Most journals today only accept for publication clinical trials previously registered&#46;</p></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle">Selection of participants &#8211; selection criteria and selection methods</span><p id="par0170" class="elsevierStylePara elsevierViewall">The participants selected for inclusion in a study and the methods of selection will ultimately determine the limits that are placed on the generalizations that can be made regarding the study results&#46; Judging the external validity of a study &#40;i&#46;e&#46;&#58; assessing to whom the study results may be applied&#41; requires that a comprehensive description of the selection criteria and selection methods and descriptive data regarding the study sample be provided&#46;<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a></p><p id="par0175" class="elsevierStylePara elsevierViewall">This subsection could begin with a brief presentation of the study setting&#44; in order to contextualize the study presented&#46; This should include the setting&#44; location&#40;s&#41; and relevant dates of the study&#44; indicating&#44; for example&#44; periods of recruitment&#44; exposures or interventions&#44; follow-up and data collection&#46;</p><p id="par0180" class="elsevierStylePara elsevierViewall">After presentation of the study setting&#44; there are three major topics that should be addressed in this subsection<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a>&#58;<ul class="elsevierStyleList" id="lis0025"><li class="elsevierStyleListItem" id="lsti0100"><span class="elsevierStyleLabel">1&#46;</span><p id="par0185" class="elsevierStylePara elsevierViewall">Specification criteria &#40;selection criteria&#41;&#44;</p></li><li class="elsevierStyleListItem" id="lsti0105"><span class="elsevierStyleLabel">2&#46;</span><p id="par0190" class="elsevierStylePara elsevierViewall">Methods for selection of participants &#40;sampling&#41; and</p></li><li class="elsevierStyleListItem" id="lsti0110"><span class="elsevierStyleLabel">3&#46;</span><p id="par0195" class="elsevierStylePara elsevierViewall">Recruitment process&#46;</p></li></ul></p><p id="par0200" class="elsevierStylePara elsevierViewall">First&#44; a full and thorough description of the criteria for selection of participants &#8211; inclusion and exclusion criteria &#8211; should be presented and its rationale explained&#46;<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a> Authors should clearly indicate the target population and the accessible population in the study&#46;</p><p id="par0205" class="elsevierStylePara elsevierViewall">A general description of the characteristics of participants is also important and could also be added&#44; but this is a matter of discussion because many authors and journals believe this should be a part of the results section&#46; For human subjects it is important to describe general demographic and clinical characteristics&#46; For animal subjects it is important to adequately describe the species&#44; weight&#44; strain&#44; sex&#44; age and eventually details regarding special characteristics or living conditions&#46;</p><p id="par0210" class="elsevierStylePara elsevierViewall">In studies involving animal models or mechanical models&#44; a detailed description must be provided regarding the preparations made prior to beginning the experimental protocol&#46; In addition&#44; all aspects of animal or tissue preparation required prior to initiation of the research protocol must be described in detail&#46; With any animal preparation or mechanical model there must be enough detail provided so that the reader can duplicate it or evaluate its relevance&#46;</p><p id="par0215" class="elsevierStylePara elsevierViewall">Second&#44; the methods for selection of participants should be carefully explained&#46; This should include an account of how the subjects were identified and how they were sampled from the target population under study &#40;sampling methods&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a> When selecting subjects from a target population&#44; probabilistic sampling methods &#40;random samples&#41; are preferred because they more appropriately guarantee representativeness of the sample&#46; When reporting probabilistic sampling methods authors should describe the sampling frame&#44; the instruments used for the random selection process and&#44; if appropriate&#44; the use of complex sampling methods with stratification or clustering and weighting procedures&#46; Although probabilistic methods are preferred&#44; for practical reasons&#44; non-probabilistic sampling methods &#40;non-random samples&#41; are much more common &#40;e&#46;g&#46;&#58; consecutive samples&#44; convenience samples&#44; systematic samples&#44; etc&#46;&#41;&#46; Although non-random sampling methods do not guarantee the representativeness of the sample&#44; they do not necessarily prevent us from validly answering the research question&#46; It should be stressed that non-probabilistic sampling methods are in many instances appropriate&#46; For example&#44; most of randomized clinical trials do not select participants through a random sampling process&#44; and they still are able to appropriately answer the causal question regarding efficacy of therapeutic interventions&#44; relying on the random allocation of alternative interventions &#40;randomization&#41;&#44; even when the participants selection was non-random&#46; When answering causal questions the crucial point is to be able to generate comparable study groups and make fair comparisons &#40;equipoise&#41; between groups and&#44; at least in this case&#44; representativeness of the sample&#44; although also important&#44; is regarded as secondary&#46;</p><p id="par0220" class="elsevierStylePara elsevierViewall">The third topic to be addressed in the &#8220;Selection of participants&#8221; subsection is the recruitment process&#46;<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a> Authors should describe in detail how recruitment was undertaken and particularly how effective it was&#46; They should present a complete account of the subjects selected from the sampling frame&#44; those that accepted and those that refused to participate&#44; ideally with a summary of reasons for refusal and a brief characterization of the subjects refusing to participate&#46; Methods implemented to reduce refusal rate should also be described&#46;</p><p id="par0225" class="elsevierStylePara elsevierViewall">In addition to the three main topics described above&#44; in comparative studies it is also important to describe some particular methods of group allocation and&#47;or participant selection that aim to improve their comparability&#46; In experimental studies &#40;randomized controlled trials&#41; a thorough account of randomization procedures should be presented including<a class="elsevierStyleCrossRef" href="#bib0090"><span class="elsevierStyleSup">18</span></a>&#58; methods used to generate the random allocation sequence&#44; details on any restrictions to randomization &#40;stratification or blocking&#41;&#44; methods for allocation concealment and implementation details of the randomization process&#46; In this type of studies this is often an independent subsection of the methods section&#46; In observational studies &#40;e&#46;g&#46;&#58; cohort studies&#44; case&#8211;control studies&#44; etc&#46;&#41; authors should describe and give details regarding the implementation of methods such as stratification and matching&#44; whenever those are used&#46;<a class="elsevierStyleCrossRef" href="#bib0095"><span class="elsevierStyleSup">19</span></a></p><p id="par0230" class="elsevierStylePara elsevierViewall">Finally&#44; in longitudinal studies a full description should be presented of the follow-up procedures implemented&#44; often as a separate subsection&#46; This should include a description of the completeness and quality of participants follow-up &#40;number and reasons for losses of follow-up&#44; drop-outs&#44; drop-ins&#44; etc&#46;&#41; and&#44; in comparative studies&#44; methods implemented to guarantee equality of follow-up conditions&#44; for example&#44; blinding of researchers or healthcare professionals responsible for the follow-up and the adequate control of co-interventions&#46;<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a></p></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle">Data collection &#8211; variables&#44; methods and instruments</span><p id="par0235" class="elsevierStylePara elsevierViewall">The next step in the methods section is to describe the data collection process&#44; including the variables measured and the methods and instruments used for their measurement&#46; In a quantitative research paradigm the adequate and unbiased empirical observation and measurement of variables is the cornerstone of the scientific method&#59; thus this subsection deserves careful and thorough consideration&#46;</p><p id="par0240" class="elsevierStylePara elsevierViewall">Variables are observable objects that are measured&#44; manipulated&#44; or controlled in a study&#46; Variables can be concrete concepts&#44; such as height&#44; weight&#44; and blood pressure&#44; or abstract concepts&#44; such as stress&#44; coping or quality-of-life&#46; Variables should be operationally defined by indicating how the variable will be observed and measured in the study&#46; Abstract variables &#40;constructs&#41;&#44; such as quality-of-life or stress&#44; should be defined both conceptually and operationally&#46; The conceptual definition explains the theoretical meaning of the variable&#44; while the operational definition specifies how it will be measured&#46; For example&#44; when measuring quality-of-life&#44; researchers could present a brief conceptual definition of the construct&#44; but should always add details regarding its operational definition&#44; by indicating the model and instrument applied to measure quality-of-life&#44; for example&#44; by using the SF-36 health questionnaire&#46;</p><p id="par0245" class="elsevierStylePara elsevierViewall">In general&#44; the variables in a study could be classified in one of four major groups<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a>&#58;<ul class="elsevierStyleList" id="lis0030"><li class="elsevierStyleListItem" id="lsti0115"><span class="elsevierStyleLabel">1&#46;</span><p id="par0250" class="elsevierStylePara elsevierViewall">Predictor &#40;independent&#44; exposure or intervention&#41; variables&#44;</p></li><li class="elsevierStyleListItem" id="lsti0120"><span class="elsevierStyleLabel">2&#46;</span><p id="par0255" class="elsevierStylePara elsevierViewall">Outcome &#40;dependent&#41; variables&#44;</p></li><li class="elsevierStyleListItem" id="lsti0125"><span class="elsevierStyleLabel">3&#46;</span><p id="par0260" class="elsevierStylePara elsevierViewall">Confounding &#40;extraneous&#41; variables or</p></li><li class="elsevierStyleListItem" id="lsti0130"><span class="elsevierStyleLabel">4&#46;</span><p id="par0265" class="elsevierStylePara elsevierViewall">Interaction &#40;effect modifier&#41; variables&#46;</p></li></ul></p><p id="par0270" class="elsevierStylePara elsevierViewall">When describing the variables in a study the authors do not need to give a full and complete description of all variables measured&#44; however the main predictors and all outcome variables should be described with sufficient detail as to allow replication and assessment of the quality of the measurement or classification&#46; For these variables a full account of their conceptual definition&#44; operational definition&#44; classification or diagnostic criteria applied &#40;if appropriate&#41;&#44; methods of measurement&#44; instruments used and a brief description of the evidence regarding their validity and reproducibility should be presented&#46; This detailed presentation should be extended to any other variable of particular importance for the study or with uncommon measurement procedures or instruments&#46;<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a></p><p id="par0275" class="elsevierStylePara elsevierViewall">For those variables where it is deemed necessary&#44; the description of the measurement methods and instruments should include the manufacturer and model&#44; calibration procedures&#44; evidence regarding the validity and reproducibility of instruments and how measurements were made&#46; The instruments used to measure variables must be reliable and valid&#46; Validity is the extent to which an instrument measures what it reports to measure&#46; Reliability refers to the consistency with which an instrument measures a study variable&#46; Internal consistency &#40;e&#46;g&#46;&#58; Cronbach&#39;s alpha&#41;&#44; test&#8211;retest reliability&#44; and inter-rater reliability are examples of methods used to assess the reliability of an instrument&#44; particularly in the context of abstract concepts &#40;constructs&#41; measurement&#46; These psychometric or clinimetric properties of instruments determine the overall study validity&#46; It is important to select and describe instruments that have established reliability and validity in the population that the investigator plans to study &#40;e&#46;g&#46;&#58; older adults or children&#41; and use instruments that are properly translated&#44; adapted and validated for the study population&#46; Although not formally prohibited&#44; the use of instruments that were not previously submitted to an adequate translation&#44; adaptation and validation process impose important limitations to the credibility and validity of the study results and its use should be always indicated&#46;</p><p id="par0280" class="elsevierStylePara elsevierViewall">Finally&#44; particular methods to control bias associated with the measurement or classification of study variables should be described&#46; For example&#44; implementation of blinding procedures for participants and for researchers collecting data &#40;especially outcomes measurement&#41; should be indicated and explained&#46;</p></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle">Data analysis</span><p id="par0285" class="elsevierStylePara elsevierViewall">In the last part of the methods section authors should describe with sufficient detail the statistical methods used for the study data analysis&#44; including descriptive statistics and methods for statistical inference&#46;<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a> This presentation should have a close link to the aims of the study and should precisely establish what will be presented in the results section&#46;</p><p id="par0290" class="elsevierStylePara elsevierViewall">This subsection should include an initial general sentence regarding the descriptive statistics used&#44; having into account the main types of variables analyzed &#40;e&#46;g&#46;&#58; means or medians&#44; standard deviations or quantile ranges&#44; absolute frequencies and proportions&#44; etc&#46;&#41;&#46; Next&#44; a brief description of inferential methods used should follow&#44; including the indication of confidence intervals calculated&#44; an account of the statistical hypothesis tests applied and the indication of any uni- or multi-variable regression or modeling procedures employed&#46; A special note should be added regarding the use of confidence intervals as the best method to express the precision of parameter estimates presented in a study&#46; Their presentation is increasingly deemed essential and they are more informative than the classical <span class="elsevierStyleItalic">p</span>-values of hypothesis testing&#46;</p><p id="par0295" class="elsevierStylePara elsevierViewall">Some additional aspects regarding the statistical analysis should be addressed whenever it is deemed necessary&#44; taking into account the main study aims&#58;<ul class="elsevierStyleList" id="lis0035"><li class="elsevierStyleListItem" id="lsti0135"><span class="elsevierStyleLabel">1&#46;</span><p id="par0300" class="elsevierStylePara elsevierViewall">Describing the specifics regarding methods used to account for confounding in observational studies &#40;e&#46;g&#46;&#58; multi-variable regression methods for effect measures adjustment&#44; propensity scores&#44;<a class="elsevierStyleCrossRef" href="#bib0150"><span class="elsevierStyleSup">30</span></a> causality modeling using directed acyclic graphs and structural models&#44;<a class="elsevierStyleCrossRefs" href="#bib0155"><span class="elsevierStyleSup">31&#8211;33</span></a> etc&#46;&#41;&#46;</p></li><li class="elsevierStyleListItem" id="lsti0140"><span class="elsevierStyleLabel">2&#46;</span><p id="par0305" class="elsevierStylePara elsevierViewall">Describing methods to examine subgroups&#44; interactions and effect modification in experimental and observational studies&#46;<a class="elsevierStyleCrossRef" href="#bib0170"><span class="elsevierStyleSup">34</span></a></p></li><li class="elsevierStyleListItem" id="lsti0145"><span class="elsevierStyleLabel">3&#46;</span><p id="par0310" class="elsevierStylePara elsevierViewall">Describing any interim analysis&#44; stopping rules and adjustments that may be used&#44; particularly in experimental studies&#46;<a class="elsevierStyleCrossRef" href="#bib0090"><span class="elsevierStyleSup">18</span></a></p></li><li class="elsevierStyleListItem" id="lsti0150"><span class="elsevierStyleLabel">4&#46;</span><p id="par0315" class="elsevierStylePara elsevierViewall">Describing any particular adjustments made taking into account the sampling methods and weighting procedures used&#46;</p></li><li class="elsevierStyleListItem" id="lsti0155"><span class="elsevierStyleLabel">5&#46;</span><p id="par0320" class="elsevierStylePara elsevierViewall">Describing methods used to account for missing data&#46;</p></li><li class="elsevierStyleListItem" id="lsti0160"><span class="elsevierStyleLabel">6&#46;</span><p id="par0325" class="elsevierStylePara elsevierViewall">Describing methods used for sensitivity analysis&#46;</p></li></ul></p><p id="par0330" class="elsevierStylePara elsevierViewall">Also important in this section is to describe the estimates and explanation of methods for the sample size and power determination&#46;<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a> The determination of the sample size before the beginning of the study is crucial to ensure the appropriate power of hypothesis testing and the precision of parameter estimates&#46; In many instances&#44; particularly in observational studies&#44; a formal sample size calculation is not possible for practical reasons &#40;for example&#44; the study sample is assembled retrospectively or is already fixed before the beginning of the study&#41;&#46; Even in these situations it is advisable to present results of a formal power analysis&#44; in order to give an indication of the power of hypothesis tests and the magnitude of differences that researchers are able to detect in those settings&#46; Some authors prefer to incorporate the paragraph regarding sample size determination as a part of the &#8220;Selection of participants&#8221; subsection&#46;</p><p id="par0335" class="elsevierStylePara elsevierViewall">Finally&#44; an indication of the level of type I errors &#40;alpha level&#41; assumed in all statistical hypothesis testing &#40;usually&#44; a 5&#37; alpha level is assumed&#41; and an indication of the statistical software package used for analysis &#40;with a reference&#41; should be presented in this subsection&#46;</p></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle">Conclusion</span><p id="par0340" class="elsevierStylePara elsevierViewall">The methods section is the most important part of a scientific paper because it provides the crucial information that allows the reader to judge the validity of the results and conclusions of the study reported&#46; Therefore&#44; in this section&#44; the authors should provide a clear and precise description of how the study was performed and the rationale for the methodological choices and characteristics of the study design&#46; A clear and precise account of how a study was performed&#44; and the rationale for specific study methods are the crucial aspects of scientific writing&#46; A proposal for the structure and content of the methods has been presented and explored giving a general guidance for the writing and assessment of the quality of this section and of the study reported&#46; We hope that somehow this paper may comprise a useful tool for authors&#44; reviewers and readers of scientific papers&#44; and in particular those of the <span class="elsevierStyleItalic">Portuguese Journal of Pulmonology &#40;Revista Portuguesa de Pneumologia&#41;</span>&#46;</p></span></span>"
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                          "autores" => array:2 [
                            0 => "T&#46;J&#46; VanderWeele"
                            1 => "J&#46;M&#46; Robins"
                          ]
                        ]
                      ]
                    ]
                  ]
                  "host" => array:1 [
                    0 => array:2 [
                      "doi" => "10.1097/EDE.0b013e318127181b"
                      "Revista" => array:6 [
                        "tituloSerie" => "Epidemiology"
                        "fecha" => "2007"
                        "volumen" => "18"
                        "paginaInicial" => "561"
                        "paginaFinal" => "568"
                        "link" => array:1 [
                          0 => array:2 [
                            "url" => "https://www.ncbi.nlm.nih.gov/pubmed/17700242"
                            "web" => "Medline"
                          ]
                        ]
                      ]
                    ]
                  ]
                ]
              ]
            ]
          ]
        ]
      ]
    ]
  ]
  "idiomaDefecto" => "en"
  "url" => "/08732159/0000001700000005/v2_201509041523/S0873215911000973/v2_201509041523/en/main.assets"
  "Apartado" => array:4 [
    "identificador" => "9587"
    "tipo" => "SECCION"
    "en" => array:2 [
      "titulo" => "Review"
      "idiomaDefecto" => true
    ]
    "idiomaDefecto" => "en"
  ]
  "PDF" => "https://static.elsevier.es/multimedia/08732159/0000001700000005/v2_201509041523/S0873215911000973/v2_201509041523/en/main.pdf?idApp=UINPBA00004E&text.app=https://journalpulmonology.org/"
  "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S0873215911000973?idApp=UINPBA00004E"
]

Article information

ISSN: 08732159
Original language: English

DOI:10.1016/j.rppneu.2011.06.014

The statistics are updated each day

Year/Month	Html	Pdf	Total

2024 November	44	10	54
2024 October	391	57	448
2024 September	300	68	368
2024 August	345	85	430
2024 July	266	44	310
2024 June	361	52	413
2024 May	333	59	392
2024 April	286	44	330
2024 March	367	49	416
2024 February	247	41	288
2024 January	231	30	261
2023 December	253	18	271
2023 November	256	47	303
2023 October	288	52	340
2023 September	232	53	285
2023 August	235	27	262
2023 July	313	69	382
2023 June	590	43	633
2023 May	525	38	563
2023 April	343	35	378
2023 March	465	66	531
2023 February	380	41	421
2023 January	288	35	323
2022 December	318	65	383
2022 November	429	68	497
2022 October	425	71	496
2022 September	377	56	433
2022 August	312	59	371
2022 July	274	81	355
2022 June	259	56	315
2022 May	316	66	382
2022 April	310	71	381
2022 March	330	75	405
2022 February	320	55	375
2022 January	352	75	427
2021 December	280	85	365
2021 November	373	80	453
2021 October	448	95	543
2021 September	380	62	442
2021 August	332	61	393
2021 July	327	56	383
2021 June	492	117	609
2021 May	947	112	1059
2021 April	2666	328	2994
2021 March	2728	95	2823
2021 February	1195	105	1300
2021 January	910	113	1023
2020 December	903	78	981
2020 November	729	84	813
2020 October	774	78	852
2020 September	1035	89	1124
2020 August	530	68	598
2020 July	793	82	875
2020 June	679	77	756
2020 May	1078	116	1194
2020 April	1661	128	1789
2020 March	1044	92	1136
2020 February	934	48	982
2020 January	958	96	1054
2019 December	556	49	605
2019 November	658	53	711
2019 October	1072	66	1138
2019 September	948	37	985
2019 August	536	59	595
2019 July	588	54	642
2019 June	431	29	460
2019 May	488	34	522
2019 April	568	64	632
2019 March	784	39	823
2019 February	1618	16	1634
2019 January	687	36	723
2018 December	392	6	398
2018 November	273	1	274
2018 October	111	9	120
2018 September	118	16	134
2018 August	78	49	127
2018 July	64	63	127
2018 June	85	46	131
2018 May	93	44	137
2018 April	146	44	190
2018 March	100	33	133
2018 February	74	18	92
2018 January	65	32	97
2017 December	75	40	115
2017 November	62	23	85
2017 October	59	13	72
2017 September	62	15	77
2017 August	73	29	102
2017 July	65	70	135
2017 June	87	32	119
2017 May	85	34	119
2017 April	63	21	84
2017 March	54	17	71
2017 February	32	11	43
2017 January	62	31	93
2016 December	80	16	96
2016 November	61	20	81
2016 October	101	64	165
2016 September	130	109	239
2016 August	141	89	230
2016 July	83	124	207
2016 June	0	83	83
2016 May	1	0	1
2016 April	2667	19	2686
2016 March	3718	167	3885
2016 February	3566	140	3706
2016 January	2315	122	2437
2015 December	2398	107	2505
2015 November	3259	128	3387
2015 October	3155	164	3319
2015 September	2555	109	2664
2015 August	2053	117	2170
2015 July	1889	125	2014
2015 June	1553	57	1610
2015 May	2651	105	2756
2015 April	2637	129	2766
2015 March	3580	30	3610
2015 February	2823	36	2859
2015 January	1730	30	1760
2014 December	1764	49	1813
2014 November	2694	33	2727
2014 October	2812	50	2862
2014 September	2144	37	2181
2014 August	1528	30	1558
2014 July	1367	37	1404
2014 June	1384	36	1420
2014 May	1497	35	1532
2014 April	1933	40	1973
2014 March	1958	42	2000
2014 February	1643	34	1677
2014 January	1163	31	1194
2013 December	1016	27	1043
2013 November	1272	26	1298
2013 October	1000	27	1027
2013 September	511	42	553
2013 August	298	46	344
2013 July	227	37	264
2013 June	230	35	265
2013 May	246	31	277
2013 April	402	43	445
2013 March	370	34	404
2013 February	142	24	166
2013 January	113	35	148
2012 December	73	28	101
2012 November	78	25	103
2012 October	34	26	60
2012 September	15	14	29
2012 January	586	0	586

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