Missing data handling in non-inferiority and equivalence trials: A systematic review.

Autor: Rabe BA; Interdisciplinary Program in Statistics, The University of Arizona, Tucson, AZ, USA., Day S; Clinical Trials Consulting & Training Limited, UK., Fiero MH; Office of Biostatistics, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA., Bell ML; Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, The University of Arizona, Tucson, AZ, USA.
Jazyk: angličtina
Zdroj: Pharmaceutical statistics [Pharm Stat] 2018 Sep; Vol. 17 (5), pp. 477-488. Date of Electronic Publication: 2018 May 25.
DOI: 10.1002/pst.1867
Abstrakt: Background: Non-inferiority (NI) and equivalence clinical trials test whether a new treatment is therapeutically no worse than, or equivalent to, an existing standard of care. Missing data in clinical trials have been shown to reduce statistical power and potentially bias estimates of effect size; however, in NI and equivalence trials, they present additional issues. For instance, they may decrease sensitivity to differences between treatment groups and bias toward the alternative hypothesis of NI (or equivalence).
Aims: Our primary aim was to review the extent of and methods for handling missing data (model-based methods, single imputation, multiple imputation, complete case), the analysis sets used (Intention-To-Treat, Per-Protocol, or both), and whether sensitivity analyses were used to explore departures from assumptions about the missing data.
Methods: We conducted a systematic review of NI and equivalence trials published between May 2015 and April 2016 by searching the PubMed database. Articles were reviewed primarily by 2 reviewers, with 6 articles reviewed by both reviewers to establish consensus.
Results: Of 109 selected articles, 93% reported some missing data in the primary outcome. Among those, 50% reported complete case analysis, and 28% reported single imputation approaches for handling missing data. Only 32% reported conducting analyses of both intention-to-treat and per-protocol populations. Only 11% conducted any sensitivity analyses to test assumptions with respect to missing data.
Conclusion: Missing data are common in NI and equivalence trials, and they are often handled by methods which may bias estimates and lead to incorrect conclusions.
(Copyright © 2018 John Wiley & Sons, Ltd.)
Databáze: MEDLINE
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