Treatment noncompliance in randomized experiments: Statistical approaches and design issues

Treatment noncompliance in randomized experiments threatens the validity of causal inference and the interpretability of treatment effects. This article provides a nontechnical review of 7 approaches: 3 traditional and 4 newer statistical analysis strategies. Traditional approaches include (a) intention-to-treat analysis (which estimates the effects of treatment assignment irrespective of treatment received), (b) as-treated analysis (which reassigns participants to groups reflecting the treatment they actually received), and (c) per-protocol analysis (which drops participants who did not comply with their assigned treatment). Newer approaches include (d) the complier average causal effect (which estimates the effect of treatment on the subpopulation of those who would comply with their assigned treatment), (e) dose-response estimation (which uses degree of compliance to stratify participants, producing an estimate of a dose-response relationship), (f) propensity score analysis (which uses covariates to estimate the probability that individual participants will comply, enabling estimates of treatment effects at different propensities), and (g) treatment effect bounding (which calculates a range of possible treatment effects applicable to both compliers and noncompliers). The discussion considers the areas of application, the quantity estimated, the underlying assumptions, and the strengths and weaknesses of each approach.