Using observational data for personalized medicine when clinical trial evidence is limited

Abstract

Randomized clinical trials are considered the preferred approach for comparing the effects of treatments, yet data from high-quality clinical trials are often unavailable and many clinical decisions are made on the basis of evidence from observational studies. Using clinical examples about the management of infertility, we discuss how we can use observational data from large and information-rich health-care databases combined with modern epidemiological and statistical methods to learn about the effects of interventions when clinical trial evidence is unavailable or not applicable to the clinically relevant target population. When trial evidence is unavailable, we can conduct observational analyses emulating the hypothetical pragmatic target trials that would address the clinical questions of interest. When trial evidence is available but not applicable to the clinically relevant target population, we can transport inferences from trial participants to the target population using the trial data and a sample of observational data from the target population. Clinical trial emulations and transportability analyses can be coupled with methods for examining heterogeneity of treatment effects, providing a path toward personalized medicine.

Keywords: Causal inference; emulating clinical trials using observational data; heterogeneity of treatment effects; study design; transportability.