Comparison of exclusion, imputation and modelling of missing binary outcome data in frequentist network meta-analysis.

ABSTRACT:

Background

Missing participant outcome data (MOD) are ubiquitous in systematic reviews with network meta-analysis (NMA) as they invade from the inclusion of clinical trials with reported participant losses. There are available strategies to address aggregate MOD, and in particular binary MOD, while considering the missing at random (MAR) assumption as a starting point. Little is known about their performance though regarding the meta-analytic parameters of a random-effects model for aggregate binary outcome data as obtained from trial-reports (i.e. the number of events and number of MOD out of the total randomised per arm).

Methods

We used four strategies to handle binary MOD under MAR and we classified these strategies to those modelling versus excluding/imputing MOD and to

SUBMITTER: Spineli LM 

PROVIDER: S-EPMC7049189 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature