Estimation of causal effects in clinical endpoint bioequivalence studies in the presence of intercurrent events: noncompliance and missing data

Yiyue Lou; Michael P Jones; Wanjie Sun

doi:10.1080/10543406.2018.1489408

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Estimation of causal effects in clinical endpoint bioequivalence studies in the presence of intercurrent events: noncompliance and missing data

Journal article

Peer reviewed

Estimation of causal effects in clinical endpoint bioequivalence studies in the presence of intercurrent events: noncompliance and missing data

Yiyue Lou, Michael P Jones and Wanjie Sun

Journal of Biopharmaceutical Statistics, Vol.29(1), pp.151-173

01/02/2019

DOI: 10.1080/10543406.2018.1489408

PMID: 29995564

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https://figshare.com/articles/journal_contribution/Estimation_of_causal_effects_in_clinical_endpoint_bioequivalence_studies_in_the_presence_of_intercurrent_events_noncompliance_and_missing_data/6804644View

Open Access

Abstract

In clinical endpoint bioequivalence (BE) studies, the primary analysis for assessing equivalence between a generic and an innovator product is based on the observed per-protocol (PP) population (usually completers and compliers). However, missing data and noncompliance are post-randomization intercurrent events and may introduce selection bias. Therefore, PP analysis is generally not causal. The FDA Missing Data Working Group recommended using "causal estimands of primary interest." In this paper, we propose a principal stratification causal framework and co-primary causal estimands to test equivalence, which was also recommended by the recently published ICH E9 (R1) addendum to address intercurrent events. We identify three conditions under which the current PP estimator is unbiased for one of the proposed co-primary causal estimands - the "Survivor Average Causal Effect" (SACE) estimand. Simulation shows that when these three conditions are not met, the PP estimator is biased and may inflate Type 1 error and/or change power. We also propose a tipping point sensitivity analysis to evaluate the robustness of the current PP estimator in testing equivalence when the sensitivity parameters deviate from the three identified conditions, but stay within a clinically meaningful range. Our work is the first causal equivalence assessment in equivalence studies with intercurrent events.

noncompliance data

principal stratification

Bioequivalence

causal inference

missing data

Details

Title: Subtitle: Estimation of causal effects in clinical endpoint bioequivalence studies in the presence of intercurrent events: noncompliance and missing data
Creators: Yiyue Lou - Department of Biostatistics, University of Iowa College of Public Health
Michael P Jones - Department of Biostatistics, University of Iowa College of Public Health
Wanjie Sun - Office of Biostatistics, Center for Drug Evaluation and Research, Food and Drug Administration (CDER/FDA)
Resource Type: Journal article
Publication Details: Journal of Biopharmaceutical Statistics, Vol.29(1), pp.151-173
DOI: 10.1080/10543406.2018.1489408
PMID: 29995564
NLM abbreviation: J Biopharm Stat
ISSN: 1054-3406
eISSN: 1520-5711
Publisher: Taylor & Francis
Language: English
Date published: 01/02/2019
Academic Unit: Statistics and Actuarial Science; Biostatistics; Public Policy Center (Archive)
Record Identifier: 9983985811002771

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