Continuous(ly) missing outcome data in network meta-analysis: A one-stage pattern-mixture model approach.

ABSTRACT: Appropriate handling of aggregate missing outcome data is necessary to minimise bias in the conclusions of systematic reviews. The two-stage pattern-mixture model has been already proposed to address aggregate missing continuous outcome data. While this approach is more proper compared with the exclusion of missing continuous outcome data and simple imputation methods, it does not offer flexible modelling of missing continuous outcome data to investigate their implications on the conclusions thoroughly. Therefore, we propose a one-stage pattern-mixture model approach under the Bayesian framework to address missing continuous outcome data in a network of interventions and gain knowledge about the missingness process in different trials and interventions. We extend the hierarchical network m

SUBMITTER: Spineli LM 

PROVIDER: S-EPMC8209314 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature