Assessing efficacy in non‐inferiority trials with non‐adherence to interventions: Are intention‐to‐treat and per‐protocol analyses fit for purpose?
Open Access
- 1 April 2024
- journal article
- research article
- Published by Wiley in Statistics in Medicine
- Vol. 43 (12) , 2314-2331
Abstract
Background: Non‐inferiority trials comparing different active drugs are often subject to treatment non‐adherence. Intention‐to‐treat (ITT) and per‐protocol (PP) analyses have been advocated in such studies but are not guaranteed to be unbiased in the presence of differential non‐adherence.Methods: The REMoxTB trial evaluated two 4‐month experimental regimens compared with a 6‐month control regimen for newly diagnosed drug‐susceptible TB. The primary endpoint was a composite unfavorable outcome of treatment failure or recurrence within 18 months post‐randomization. We conducted a simulation study based on REMoxTB to assess the performance of statistical methods for handling non‐adherence in non‐inferiority trials, including: ITT and PP analyses, adjustment for observed adherence, multiple imputation (MI) of outcomes, inverse‐probability‐of‐treatment weighting (IPTW), and a doubly‐robust (DR) estimator.Results: When non‐adherence differed between trial arms, ITT, and PP analyses often resulted in non‐trivial bias in the estimated treatment effect, which consequently under‐ or over‐inflated the type I error rate. Adjustment for observed adherence led to similar issues, whereas the MI, IPTW and DR approaches were able to correct bias under most non‐adherence scenarios; they could not always eliminate bias entirely in the presence of unobserved confounding. The IPTW and DR methods were generally unbiased and maintained desired type I error rates and statistical power.Conclusions: When non‐adherence differs between trial arms, ITT and PP analyses can produce biased estimates of efficacy, potentially leading to the acceptance of inferior treatments or efficacious regimens being missed. IPTW and the DR estimator are relatively straightforward methods to supplement ITT and PP approaches.Keywords
Funding Information
- Wellcome Trust (224485/Z/21/Z)
This publication has 34 references indexed in Scilit:
- Digital adherence technologies to improve tuberculosis treatment outcomes in China: a cluster-randomised superiority trialThe Lancet Global Health, 2023
- Using simulation studies to evaluate statistical methodsStatistics in Medicine, 2019
- A patient-level pooled analysis of treatment-shortening regimens for drug-susceptible pulmonary tuberculosisNature Medicine, 2018
- Assessing the performance of the generalized propensity score for estimating the effect of quantitative or continuous exposures on binary outcomesStatistics in Medicine, 2018
- Correcting for non‐compliance in randomized non‐inferiority trials with active and placebo control using structural modelsStatistics in Medicine, 2014
- Reporting of Noninferiority and Equivalence Randomized TrialsJAMA, 2012
- A comparison of the results of intent-to-treat, per-protocol, and g-estimation in the presence of non-random treatment changes in a time-to-event non-inferiority trialStatistics in Medicine, 2010
- Reporting of Noninferiority and Equivalence Randomized TrialsJAMA, 2006
- Choosing the analysis population in non-inferiority studies: per protocol or intent-to-treatStatistics in Medicine, 2006
- A comparison of intent-to-treat and per-protocol results in antibiotic non-inferiority trialsStatistics in Medicine, 2004