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

Jeffrey A. Boatman, David M. Vock, Joseph S. Koopmeiners, Eric C. Donny

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

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 is based on self-report. Biomarkers (e.g., nicotine levels in the urine) 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, we show how the probability of compliance can be directly estimated from the data even when compliance status is unknown. To estimate the causal effect, we develop a new approach which re-weights participants by the product of their probability of compliance given the observed data and the inverse probability of compliance given confounders. We show that our proposed estimator is consistent and asymptotically normal and show that in some situations the proposed approach is more efficient than standard IPCW estimators. We demonstrate via simulation that the proposed estimator achieves smaller bias and greater efficiency than ad hoc approaches to estimating the causal effect when compliance is measured with error. We apply our method to data from a recently completed randomized trial of VLNC cigarettes.

Original languageEnglish (US)
Pages (from-to)103-118
Number of pages16
JournalBiostatistics
Volume19
Issue number1
DOIs
StatePublished - Jan 1 2018

Bibliographical note

Funding Information:
This research was partially funded by NIH grants R03-DA041870 and U54-DA031659 from the National Institute on Drug Abuse and FDA Center for Tobacco Products (CTP). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or FDA CTP.

Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.

Keywords

  • Causal inference
  • Clinical trials
  • Inverse probability weighting
  • Noncompliance
  • Regulatory science
  • Very low nicotine content cigarettes

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