Estimating causal effects from a randomized clinical trial when noncompliance is measured with error.

ABSTRACT: Noncompliance or non-adherence to randomized treatment is a common challenge when interpreting data from randomized clinical trials. The effect of an intervention if all participants were forced to comply with the assigned treatment (i.e., the causal effect) is often of primary scientific interest. For example, in trials of very low nicotine content (VLNC) cigarettes, policymakers are interested in their effect on smoking behavior if their use were to be compelled by regulation. A variety of statistical methods to estimate the causal effect of an intervention have been proposed, but these methods, including inverse probability of compliance weighted (IPCW) estimators, assume that participants' compliance statuses are reported without error. This is an untenable assumption when compliance i

SUBMITTER: Boatman JA 

PROVIDER: S-EPMC6075374 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature