A Conservative Approach for Analysis of Noninferiority Trials With Missing Data and Subject Noncompliance

Brooke A. Rabe, Melanie L. Bell

Research output: Contribution to journalArticle

Abstract

Noninferiority clinical trials aim to show an experimental treatment is therapeutically no worse than standard of care, particularly if the new treatment is preferred for reasons such as cost, convenience, safety, and so on. Noninferiority trials are by nature less conservative than superiority studies: protocol violations may increase bias toward the alternative hypothesis of noninferiority. Our objective was to compare multiple imputation, a linear mixed model, and other methods for analyzing a longitudinal trial with missing data in intention-to-treat and per-protocol populations. We simulated trials with missing data and noncompliance due to treatment inefficacy under varying trial conditions (e.g., trajectory of treatment effects, correlation between repeated measures, and missing data mechanism), assessing each approach by estimating bias, Type I error, and power. We found that multiple imputation using auxiliary data on noncompliance in the imputation model performed best. A hybrid intention-to-treat/per-protocol multiple imputation approach with a missing not at random imputation model produced low Type I error, was unbiased and maintained reasonable power to detect noninferiority. We conclude that the anti-conservatism of noninferiority trial estimands conforming with the intention-to-treat principle may be offset by imputation models that include variables on intercurrent events. Supplementary materials for this article are available online.

Original languageEnglish (US)
JournalStatistics in Biopharmaceutical Research
DOIs
StateAccepted/In press - Jan 1 2019

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Non-inferiority
Noncompliance
Missing Data
Multiple Imputation
Imputation
Type I error
Politics
Standard of Care
Linear Models
Missing Data Mechanism
Measure Data
Linear Mixed Model
Missing at Random
Clinical Trials
Repeated Measures
Safety
Costs and Cost Analysis
Treatment Effects
Population
Model

Keywords

  • Intention-to-treat
  • Missing not at random
  • Multiple imputation
  • Per-protocol

ASJC Scopus subject areas

  • Statistics and Probability
  • Pharmaceutical Science

Cite this

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