Inference for the effect of treatment on survival probability in randomized trials with noncompliance and administrative censoring.

Web-based Supplementary Materials for “Inference for the Effect of Treatment on Survival Probability in Randomized Trials with Noncompliance and Administrative Censoring” by Nie, Cheng and Small

Causal Inference Through Potential Outcomes and Principal Stratification: Application to Studies with ``Censoring'' Due to Death

Causal inference is best understood using potential outcomes. This use is particularly important in more complex settings, that is, observational studies or randomized experiments with complications such as noncompliance. The topic of this lecture, the issue of estimating the causal effect of a treatment on a primary outcome that is “censored” by death, is another such complication. For example...

Performance of statistical methods for analysing survival data in the presence of non-random compliance.

Noncompliance often complicates estimation of treatment efficacy from randomized trials. Under random noncompliance, per protocol analyses or even simple regression adjustments for noncompliance, could be adequate for causal inference, but special methods are needed when noncompliance is related to risk. For survival data, Robins and Tsiatis introduced the semi-parametric structural Causal Acce...

Multiple imputation methods for treatment noncompliance and nonresponse in randomized clinical trials.

Randomized clinical trials are a powerful tool for investigating causal treatment effects, but in human trials there are oftentimes problems of noncompliance which standard analyses, such as the intention-to-treat or as-treated analysis, either ignore or incorporate in such a way that the resulting estimand is no longer a causal effect. One alternative to these analyses is the complier average ...

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...