Evidence-based medicine-When observational studies are better than randomized controlled trials

Nephrology (Carlton). 2020 Oct;25(10):737-743. doi: 10.1111/nep.13742. Epub 2020 Jul 2.

Abstract

In evidence-based medicine, clinical research questions may be addressed by different study designs. This article describes when randomized controlled trials (RCT) are needed and when observational studies are more suitable. According to the Centre for Evidence-Based Medicine, study designs can be divided into analytic and non-analytic (descriptive) study designs. Analytic studies aim to quantify the association of an intervention (eg, treatment) or a naturally occurring exposure with an outcome. They can be subdivided into experimental (ie, RCT) and observational studies. The RCT is the best study design to evaluate the intended effect of an intervention, because the randomization procedure breaks the link between the allocation of the intervention and patient prognosis. If the randomization of the intervention or exposure is not possible, one needs to depend on observational analytic studies, but these studies usually suffer from bias and confounding. If the study focuses on unintended effects of interventions (ie, effects of an intervention that are not intended or foreseen), observational analytic studies are the most suitable study designs, provided that there is no link between the allocation of the intervention and the unintended effect. Furthermore, non-analytic studies (ie, descriptive studies) also rely on observational study designs. In summary, RCTs and observational study designs are inherently different, and depending on the study aim, they each have their own strengths and weaknesses.

Keywords: confounding; epidemiology; methodology; observational research; randomized controlled trial.

Publication types

  • Review

MeSH terms

  • Confounding Factors, Epidemiologic
  • Evidence-Based Medicine*
  • Humans
  • Observational Studies as Topic / methods*
  • Randomized Controlled Trials as Topic / methods*
  • Research Design