Writing the methods section.

Methods section is the easiest part of the scientific paper and you can start writing it down even when the research is unfinished. It has to be written in the past tense because you have already written the proposal and either you have started or have conducted the study. The basic elements of the methods section are study design, setting and subjects, data collection, data analysis, and ethical approval.

Methods section is the easiest part of the scientific paper and you can start writing it down even when the research is unfinished. It has to be written in the past tense because you have already written the proposal and either you have started or have conducted the study. The basic elements of the methods section are study design, setting and subjects, data collection, data analysis, and ethical approval.

Study Design

The study design is rated according to its clinical relevance. There are different types of study designs based on their relevance from high to low impact as follows.

Meta-analysis

It is a type of systematic review with statistical procedure for combining data from multiple studies. When the treatment effect (effect size) is consistent from one study to another, meta-analysis will be useful to identify the common effect. When the effect varies from one study to another, meta-analysis can be used to identify the reason of variation and consider the implications. PRISMA (preferred reporting items for systematic reviews and meta-analyses) guidelines could be used for validation of meta-analyses studies.[1]

Systematic review

It is a narrative approach (without statistical analysis), which summarizes the results of available carefully designed health-care studies (controlled trials) and provides a high level of evidence on the effectiveness of health-care interventions. Judgments may be made about the evidence and inform recommendations for health care. These reviews are complicated and depend largely on what clinical trials are available, how they were carried out (the quality of the trials), and the health outcomes that were measured. PRISMA guidelines could be also used for validation of the systematic reviews.

Randomized controlled trial

It is a trial with randomized and controlled design (e.g., a two-armed study with parallel groups); the effects of the study treatment (intervention) are compared with those of a control treatment and the patients are randomly assigned to the two groups. The patients in the control group receive either a placebo or another treatment. In randomized controlled trial (RCT), the patients are randomly assigned to the different study groups. This is intended to ensure that all potential confounding factors are divided equally among the groups that will later be compared (structural equivalence). These factors are characteristics that may affect the patients' response to treatment, for example, weight, age, and sex. Only if the groups are structurally equivalent, can any differences in the results be attributed to a treatment effect rather than the influence of confounders? If the confounders are known, structural equivalence of the patient groups can be attained by stratified randomization.[2] Consolidated standards of reporting trial flow chart should appear in the methods section for any RCT.[3]

Observational studies

Observational studies fall under the category of analytic study designs and are further subclassified as observational or experimental study designs. The goal of analytic studies is to identify and evaluate causes or risk factors of diseases or health-related events. The difference between observational and experimental study designs is that in an observational study, the investigator does not intervene and rather simply “observes” and assesses the strength of the relationship between an exposure and disease variable.[4] Three types of observational studies are known, which include cohort (an ancient Roman military word that means a group of people with a shared characteristic), case-control, and cross-sectional studies. In an observational cohort study, the investigator identifies a cohort of interest exposed to a risk factor or a treatment and chooses a control group with a different exposure. These groups are then followed prospectively while comparing the long-term consequences of the exposures. They are particularly relevant for evaluating risk factors of the disease, the prognosis, the incidence, and/or risk ratio. In a case-control study, the investigator first identifies patients affected by a disease compared with healthy controlled group. The exposures in each group are then compared retrospectively. They are relevant to identify potential risk factors of the disease and the odds ratio. In a cross-sectional study, also known as prevalence study, the investigator measures both the exposure and disease prevalence at a single time point. It is appropriate to generate hypotheses on the cause of the disease or to evaluate the odds ratio.[4] STrengthening the Reporting of OBservational studies in Epidemiology could be used for article validation.[5]

Case series

The investigator describes several (>3–4) patients with unique clinical presentation. A group or series of case reports involves patients who were given similar treatment. Reports of case series usually contain detailed information about the individual patients. This includes demographic information (e.g., age, gender, and ethnic origin) and information on diagnosis, treatment, response to treatment, and follow-up after treatment. CARE (CaseReport) guidelines can be used for case report article validation.[6]

Case report

The investigator describes 1–3 patients with a unique clinical presentation that has a high educational value. CARE guidelines can be used for case report article validation.

Setting and Subjects

This section describes the settings and relevant dates including periods of recruitment, exposure, follow-up, and data collection. Subjects in all types of clinical studies should be clearly identified with inclusion and exclusion eligibility criteria. The primary and secondary outcome measurements should be clearly described. The primary outcome measure is the outcome that an investigator considers to be the most important among other outcomes to be examined in the study. The primary outcome needs to be defined at the time the study is designed. There are two reasons for this: it reduces the risk of false-positive errors resulting from the statistical testing of many outcomes, and it reduces the risk of a false-negative error by providing the basis for the estimation of the sample size necessary for an adequately powered study. The secondary outcome is not usually used to determine the trial design and sample size. They are included as secondary or tertiary outcomes to be measured in the trial. These outcomes may not be statistically conclusive, since the trial may not have been designed with the power to evaluate them, but they can be very useful to generate further hypotheses and guide future trials. Due to their importance in justifying future studies, these additional outcomes also need careful definition and measurement and they should be fully specified in the protocol, as extra resources are often needed to measure and evaluate them.

Data Collection (Variables)

A study usually has three kinds of variables: independent, dependent, and controlled. The independent variable is the variable whose change is not affected by any other variable in the experiment. Two examples of common independent variables are age and time. There is nothing you or anyone else can do to speed up or slow down time or increase or decrease age. They are independent of everything. It is usually wise to have only one independent variable at a time. If you are new to doing science projects and want to know the effect of changing multiple variables, do multiple tests where you focus on one independent variable at a time. The dependent variables are the things that the researcher focuses his/her observations on to see how they respond to the change made to the independent variable. The dependent variable is what is being studied and measured in the experiment. It is what changes as a result of the changes to the independent variable. An example of a dependent variable is how tall you are at different ages. The dependent variable (height) depends on the independent variable (age). An easy way to think of independent and dependent variables is that when you are conducting an experiment, the independent variable is what you change, and the dependent variable is what changes because of that. You can also think of the independent variable as the cause and the dependent variable as the effect. The controlled variables are quantities that a researcher wants to remain constant, and she/he must observe them as carefully as the dependent variables. Controlled variables are variables that an experimenter keeps constant to prevent confounding with the independent variable. They are called controlled variables because the experimenter controls them. In general, all measurements should be clearly identified in the data collection of the methods section.

Data Analysis

Data analysis is the process of extracting information from data. It involves multiple stages including establishing a data set, preparing the data for processing, applying models, identifying key findings, and creating reports. The goal of data analysis is to find actionable insights that can inform decision-making. Data analysis can involve data mining, descriptive and predictive analysis, and statistical analysis. Power analysis should be performed in order to show how the study size was arrived which should be large enough at a point estimate with a reasonably narrow confidence interval. Performing power analysis and sample size estimation is an important aspect of experimental design, because without these calculations, sample size may be too high or too low. If sample size is too low, the experiment will lack the precision to provide reliable answers to the questions it is investigating. If sample size is too large, time and resources will be wasted, often for minimal gain. Not performing power analysis for sample size calculation is usually considered a good reason for article rejection. Statistical methods should be described in details, including type of the test used for either linear or nonlinear measurements. In addition, describe any other method used to examine subgroups variables. The software used should be stated with the version and source.

Ethical Approval

This is the most important part of the methods section of any study. Institutional Review Board (IRB) approval is a very important document to carry on any study. Failure to submit the original IRB document, if asked by journal editor, will lead to serious consequences. Any time during auditing of journal articles, the journal can ask you to provide it even many years after your article was published. For RCTs, an online registration number should be obtained and provided in the text. Failure to obtain it is a common reason for article rejection. The website for it is www.clinicaltrials.gov or any similar websites.[7]

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.