Disentangling treatment and placebo effects in randomized experiments using principal stratification—An introduction

Abstract

Although randomized controlled trials (RCTs) are generally considered the gold standard for estimating causal effects, for example of pharmaceutical treatments, the valid analysis of RCTs is more complicated with human units than with plants and other such objects. One potential complication that arises with human subjects is the possible existence of placebo effects in RCTs with placebo controls, where a treatment, suppose a new drug, is compared to a placebo, and for approval, the treatment must demonstrate better outcomes than the placebo. In such trials, the causal estimand of interest is the medical effect of the drug compared to placebo. But in practice, when a drug is prescribed by a doctor and the patient is aware of the prescription received, the patient can be expected to receive both a placebo effect and the active effect of the drug. An important issue for practice concerns how to disentangle the medical effect of the drug from the placebo effect of being treated using data arising in a placebo-controlled RCT. Our proposal uses principal stratification as the key statistical tool. The method is applied to initial data from an actual experiment to illustrate important ideas.