A fully Bayesian mixture model approach for identifying noncompliance in a regulatory tobacco clinical trial

Alexander M. Kaizer, Joseph S. Koopmeiners

Research output: Contribution to journalArticlepeer-review

Abstract

Identifying noncompliance in a randomized trial is challenging, but could be improved by leveraging biomarker data to identify participants that did not comply with their assigned treatment. For randomized trials of very low nicotine content (VLNC) cigarettes, the biomarker of total nicotine equivalents (TNE) could be used to identify noncompliance. Compliant participants should have lower levels of TNEs than participants that did not comply and smoked normal nicotine content cigarettes, resulting in a mixture of compliant and noncompliant participants at each dose level. Thresholds of TNE could then be identified from the compliant groups at each dose level and used to determine which study participants were compliant. Furthermore, proposed biological relationships of TNE with nicotine dose could be incorporated into improve the efficiency of estimation, but may introduce bias if misspecified. To account for multiple modeling assumptions across dose levels, we explore model averaging via reversible jump markov chain monte carlo (MCMC) within each dose level to take advantage of improvements in efficiency when the proposed relationship is true and to downweight the biological model when it is misspecified. In simulation studies, we demonstrate that model averaging in the presence of a correct biological relationship results in a decrease in the mean square error (MSE) of up to 85%, but downweights the model in dose levels where the relationship is not appropriate. We apply our approach to data from a randomized trial of VLNC cigarettes to estimate TNE thresholds and probability of compliance curves as a function of TNEs for each nicotine dose used in the trial.

Original languageEnglish (US)
Pages (from-to)1328-1342
Number of pages15
JournalStatistics in Medicine
Volume39
Issue number9
DOIs
StatePublished - Apr 30 2020

Bibliographical note

Funding Information:
The authors would like to thank their collaborator, Dr. Eric Donny, for providing the data used in Section 4. This research was partially funded by NIH grants P30‐CA077598 from the National Cancer Institute and R01‐DA046320, 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.

Keywords

  • Bayesian model averaging
  • clinical trial
  • noncompliance
  • regulatory science
  • reversible jump MCMC

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural

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