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Activity Number: 403 - SPAAC Poster Competition
Type: Topic Contributed
Date/Time: Tuesday, July 30, 2019 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #305154
Title: Detecting Participant Noncompliance Across Multiple Time Points: The CATCH ‘EM Method
Author(s): Ross Peterson* and David Michael Vock and Joseph Koopmeiners
Companies: and University of Minnesota and University of Minnesota
Keywords: clinical trials; participant noncompliance; detection; EM algorithm; random effects model

Participant noncompliance, in which participants do not follow their assigned treatment protocol, has long complicated the interpretation of RCTs. Because participant noncompliance is often unobserved, no gold standard exists for ascertainment. In some trials, biomarker data may have a relationship with participant noncompliance. We present a method that uses longitudinal biomarker data to model compliance across multiple time points when compliance is unobserved. We use random effects models to express the biomarker distribution as a longitudinal mixture density with parameters estimated by an EM algorithm. Conditional on biomarker data, we extract three probability estimators from the mixture density: 1) the probability of compliance at the final time point of the trial; 2) the probability of compliance at all time points; and 3) the prediction probability of compliance at the final time point. We evaluate our method by simulating a trial of n = 1000 participants followed over 5 time points. We compare probability estimators 1) and 2) to those that ignore the within-participant correlation in the data. As 3) does not have a naive comparator, we plotted its calibration lines.

Authors who are presenting talks have a * after their name.

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