Missing data handling in non-inferiority and equivalence trials: A systematic review.
Pharm Stat
; 17(5): 477-488, 2018 09.
Article
em En
| MEDLINE
| ID: mdl-29797777
ABSTRACT
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Projetos de Pesquisa
/
Ensaios Clínicos como Assunto
/
Interpretação Estatística de Dados
Tipo de estudo:
Guideline
/
Prognostic_studies
/
Systematic_reviews
Limite:
Humans
Idioma:
En
Revista:
Pharm Stat
Assunto da revista:
FARMACOLOGIA
Ano de publicação:
2018
Tipo de documento:
Article
País de afiliação:
Estados Unidos