The top of the evidence pyramid

Abstract

Randomized clinical trials are the most rigorous way to establish a cause-effect relationship between an intervention's efficacy and the outcomes with the least level of uncertainty. Its key features are that the study subjects are randomly assigned to the intervention, the researcher and the subjects are blinded to the treatment they are receiving, and all groups receive the same care except for the experimental intervention. The intention-to-treat analysis involves analyzing the samples in the same group to which they were randomly assigned. The main goal of this analysis is to find out how much the predetermined outcomes of the intervention groups differed from one another.

In order to assess the efficacy of interventions, observational studies need to account for confounding variables that influence the association because they are related to both the exposure and the intervention; but it is usually only correlation that is shown, not causality.  Researchers and healthcare professionals often interpret this incorrectly. The concept of utilizing meta-analysis to create a mathematical synthesis of the body of evidence is logical and appealing enough to be ranked at the top; however, even though the studies are well-developed and executed, the results are contingent upon the methodological aspects, inherent clinical heterogeneity, and the design of each individual study.

Assessing the magnitude of the difference between the interventions and the outcomes is the goal of the statistical analysis of a randomized clinical trial. Initially, a point estimate that reflects the observed difference is obtained. Next, the degree of data uncertainty needs to be determined, typically using 95% confidence intervals (95% CI). There are essentially three kinds of estimates that can be utilized, depending on the sort of outcome of interest: binary, time to event, and quantitative outcome.