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Main Author:	Sakshi Nigal
Format:	Recurso digital
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Published:	Zenodo 2025
Online Access:	https://doi.org/10.5281/zenodo.15284366
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Research methods and methodology in EBM.  An article by SAKSHI NIGAL under the guidance of MADRAIMOVA V.S. ma'am.    Abstract  Evidence Based Medicine (EBM) is a concept to integrate the best available research evidence with clinical expertise and patient values in making healthcare decisions. Sound methods, rigorous research methods, and the methods and methods of sound research are required to produce evidence that is reliable and valid for transferring to practice. Particularly, some key research methods and methodological considerations for EBM are described in this article, including study designs, data analysis techniques and critical appraisal strategies to appraise the quality and applicability of the research evidence.   Introduction Evidence based medicine in healthcare has revolutionized the practice by incorporating the employment of high quality research evidence to shape clinical decision making. The EBM process entails having a clinical question, searching for evidence pertinent to a question, critically appraising the evidence, applying the evidence to a patient’s care, and then evaluating the outcomes. This process is reliant on research methods and methodology to determine the quality and suitability of evidence, which in turn can drive change with regard to clinical practice. According to this article, the essential research methods and methodological considerations in EBM are crucial and will be discussed in this article. Content 1. Study Designs in EBM Based on its design, the strength and applicability of research findings are substantial. The types of clinical questions that can be asked are different and study designs that are suited to each type. The hierarchy of evidence is an approach to describing the relative effectiveness of different study designs to minimize bias and establish causality.   •  1.1. Systematic Reviews and Meta-Analyses:   A review of the literature on a specific clinical question using a systematic and reproducible method to identify, select, and critically appraise all relevant studies. Statistical techniques such as meta-analyses combine the results of multiple studies to come up with a summary estimate of the effect of an intervention.• Strengths: Provide the highest caliber of evidence, minimize bias, boost statistical power, and spot discrepancies between studies. • Restrictions: Subject to publication bias, heterogeneity (variability) among studies, and the caliber of included studies. • Methodological considerations include a clear research question, a thorough search strategy, clear inclusion and exclusion criteria, a thorough quality assessment, relevant statistical analysis, an evaluation of publication bias, and openness in reporting.   •  1.2. RCTs, or randomized controlled trials:  • Description: Randomized controlled trials (RCTs) are experimental studies where participants are randomized to receive a control (e.g., standard care or a placebo) or an intervention (e.g., a new drug). • Strengths: Allow for causal inferences, reduce bias through randomization, and offer concrete proof of an intervention's impact. • Drawbacks: May be costly, time-consuming, and challenging to carry out, particularly for intricate interventions or uncommon results. The kinds of interventions that can be researched may be restricted by ethical considerations. • Methodological considerations include intention-to-treat analysis, standardized outcome measures, blinding (masking) of participants and investigators, random assignment, and a sufficient sample size.    • 1.3. Cohort Studies: • Synopsis: Cohort studies track a group of people (a cohort) over time to investigate the connection between exposure (e.g., treatment or risk factor) and outcome (e.g., illness or death). • Strengths: Able to estimate disease incidence, evaluate the temporal relationship between exposure and outcome, and analyze multiple outcomes. • Drawbacks: May not be appropriate for uncommon outcomes, can be costly and time-consuming, and is subject to selection bias and attrition bias (participant loss over time). • Methodological considerations include a well-defined cohort, precise exposure and outcome measurements, confounding variable control, and sufficient follow-up duration.   •  1.4 Case-Control Studies: • Synopsis: Case-control studies look for factors that might be linked to an outcome by comparing people who have a disease or outcome (cases) to people who don't have the disease or outcome (controls). • Advantages: Less costly and time-consuming than cohort studies, effective for researching uncommon diseases or outcomes. • Recall bias (cases may recall exposures differently than controls), selection bias, and difficulty establishing temporality are among the limitations. • Methodological considerations include precise exposure measurements, confounding variable matching or statistical adjustment, and a clear definition of cases and controls.    • 1.5. Cross-Sectional Studies: • Synopsis: Cross-sectional studies look at data from a population at one particular moment in time. • Strengths: Helps generate hypotheses by giving a quick overview of the prevalence of a disease or outcome in a population.   • 1.6. Case Reports and Case Series: • Description: Detailed reports of individual patients or a small group of patients with a particular condition; • Strengths: May be useful for identifying new diseases or adverse events, generating hypotheses, and illustrating clinical management; • Limitations: Cannot establish causality, susceptible to prevalence-incidence bias, and limited generalizability;   2.Data Analysis Techniques in EBM Appropriate data analysis techniques are crucial for accurately interpreting research findings and drawing valid conclusions.   • 2.1. Statistical Significance: The p-value is a measure of the probability of observing a result as extreme as, or more extreme than, the one observed if the null hypothesis (no effect) is true. Although statistical significance does not always imply clinical significance, a p-value of less than 0.05 is generally regarded as statistically significant.   • 2.2. Confidence Intervals: These intervals give a range of values that are likely to contain the true population parameter. Greater uncertainty regarding the actual effect is indicated by wider confidence intervals.    • 2.3. Effect Size Measures: These metrics quantify the extent to which an intervention has an impact. Typical metrics for effect size include: The ratio of the exposed group's risk of an outcome to the unexposed group's risk is known as relative risk, or RR. The odds of an outcome in the exposed group divided by the odds in the unexposed group is known as the odds ratio, or OR. • Hazard Ratio (HR): The ratio of the exposed group's hazard rate (the immediate risk of an event) to the unexposed group's hazard rate. The number of patients who require treatment with an intervention in order to stop one more negative outcome is known as the Number Needed to Treat (NNT).    • 2.4. Heterogeneity: This describes the differences between the studies that are part of a meta-analysis or systematic review. Heterogeneity is evaluated using statistical tests (such as the Q test and I2 statistic). It might not be appropriate to combine the study results if there is a lot of heterogeneity.    • 2.5. Regression Analysis: This statistical method looks at how one or more independent variables (predictors) relate to a dependent variable (outcome).    3. Evaluation of Research Evidence Critically The process of methodically evaluating the reliability, validity, and applicability of research evidence is known as critical appraisal. Because they enable clinicians to recognize high-quality evidence that can guide clinical practice, critical appraisal skills are crucial for EBM.    • 3.1. Evaluating Validity: The accuracy of the research findings is referred to as validity. When evaluating validity, the following are important questions to think about: • Did the research question fit the study design? • Did the study follow strict guidelines and take precautions against bias? • Were the findings statistically significant? • Did the narrow confidence intervals show accurate effect estimates?   • 3.2. Evaluating Reliability: The consistency and reproducibility of the research findings are referred to as reliability. When evaluating reliability, it's important to ask: • Was the sample size sufficient? • Were the outcome measures consistently measured and well-defined? • Did the outcomes hold true for various participant subgroups?    • 3.3. Evaluating Applicability: The degree to which the research findings are applicable to the clinician's own patient population is known as applicability. When evaluating applicability, it's important to ask: • Do study participants resemble the clinician's own patients? • In the practice setting of the clinician, was the intervention both feasible and acceptable? • Is it likely that the intervention's advantages will exceed its disadvantages?   • 3.4. Critical Appraisal Tools: Clinicians can evaluate research evidence critically with the help of a number of tools. The GRADE framework, the CASP checklists, and the Cochrane Risk of Bias tool are a few examples.    Conclusion  In conclusion Sound methodologies and rigorous research techniques are necessary to produce legitimate and trustworthy evidence that can guide EBM and be put into practice. Clinicians must be able to interpret statistical analyses, critically evaluate research evidence, and comprehend the advantages and disadvantages of various study designs. Clinicians can enhance patient outcomes and deliver optimal care by utilizing high-quality evidence to inform clinical decision-making.   Reference  [1] Sackett DL, Straus SE, Richardson WS, Rosenberg W, Haynes RB. Evidence-based medicine: how to practice and teach EBM. 2nd ed. Edinburgh: Churchill Livingstone; 2000.   [2] Guyatt GH, Rennie D, Meade MO, Cook DJ. Users' guides to the medical literature: a manual for evidence-based clinical practice. 2nd ed. New York: McGraw-Hill; 2002.   [3] Higgins JPT, Green S, editors. Cochrane handbook for systematic reviews of interventions. Chichester: John Wiley & Sons; 2008.   [4] Schulz KF, Grimes DA. Allocation concealment in randomised trials: defending against deciphering. Lancet. 2002;359(9306):614-8.

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