Pragmatic randomized clinical trials: best practices and statistical guidance

Abstract

Randomized clinical trials often serve the purpose of assessing the efficacy and safety of a compound. By combining real-world evidence and randomization, pragmatic randomized clinical trials (PrCTs) can be used to inform treatment effectiveness and healthcare decisions. PrCTs, referring to studies where several pragmatic elements are used (eligibility, endpoints, follow-up, etc.), pose unique challenges (Loudon et al. in BMJ 350:h2147, 2015). From a literature review, we propose a definition of PrCT and discuss strategies to overcome some PrCT challenges. Use of alternative data collection approaches may lead to uncertainties, and absence of blinding could potentially lead to non-random missing data at study endpoints such that randomization is no longer protected by an intent to treat. Therefore, more complex randomization strategies may be needed to minimize bias. Additional data sources could be used to synthesize information and create a more accurate endpoint definition, which may require tools such as natural language processing. The statistician must become familiar with the challenges and strengths of PrCTs, ranging from design to analysis to interpretation, in order to transform data into evidence (Califf in Clin Trials 13:471–477, 2016).