Causal inference methods to assess safety upper bounds in randomized trials with noncompliance

Causal inference methods to assess safety upper bounds in randomized trials with noncompliance

BACKGROUND Premature discontinuation and other forms of noncompliance with treatment assignment can complicate causal inference of treatment effects in randomized trials. The intent-to-treat analysis gives unbiased estimates for causal effects of treatment assignment on outcome, but may understate potential benefit or harm of actual treatment. The corresponding upper confidence limit can also b...

Causal Inference in Randomized Trials with Noncompliance

Intention-to-treat analysis in cluster randomized trials with noncompliance.

In cluster randomized trials (CRTs), individuals belonging to the same cluster are very likely to resemble one another, not only in terms of outcomes but also in terms of treatment compliance behavior. Although the impact of resemblance in outcomes is well acknowledged, little attention has been given to the possible impact of resemblance in compliance behavior. This study defines compliance in...

A comparison of methods for estimating the causal effect of a treatment in randomized clinical trials subject to noncompliance.

SUMMARY We consider the analysis of clinical trials that involve randomization to an active treatment (T = 1) or a control treatment (T = 0), when the active treatment is subject to all-or-nothing compliance. We compare three approaches to estimating treatment efficacy in this situation: as-treated analysis, per-protocol analysis, and instrumental variable (IV) estimation, where the treatment e...

Bayesian inference for causal effects in randomized experiments with noncompliance: The role of multivariate outcomes

Principal Stratification (PS) is a principled framework for addressing noncompliance issues. Due to the latent nature of principal strata, model-based PS analysis usually involves weakly identified models and identification of causal effects relies on untestable structural assumptions, such as exclusion restriction. This article develops a Bayesian approach to exploit multivariate outcomes to s...