|
Abstract:
|
Estimating the causal effect of a treatment in a randomized clinical trial is often complicated due to noncompliance. Existing methods to estimate the causal effect assume that participants' compliance status is reported without error, but this is an untenable assumption when noncompliance is based on self-report. Biomarkers may provide more reliable indicators of compliance but cannot perfectly discriminate between compliers and non-compliers. However, by modeling the distribution of the biomarker as a mixture distribution and writing the probability of compliance as a function of the mixture components, the probability of compliance can be directly estimated from the data even when compliance status is unknown. We develop a novel approach to estimate the causal effect that re-weights participants by the product of their probability of compliance given the biomarkers and the inverse probability of compliance given confounders. Our proposed estimator is consistent, asymptotically normal, and simulation shows that the proposed estimator has smaller bias and greater efficiency than an ad hoc approach. We apply our method to data from a recently completed randomized trial.
|
