Participants' outcomes gone missing within a network of interventions: Bayesian modeling strategies

Abstract

Objectives: To investigate the implications of addressing informative missing binary outcome data (MOD) on network meta-analysis (NMA) estimates while applying the missing at random (MAR) assumption under different prior structures of the missingness parameter. Methods: In three motivating examples, we compared six different prior structures of the informative missingness odds ratio (IMOR) parameter in logarithmic scale under pattern-mixture and selection models. Then, we simulated 1000 triangle networks of two-arm trials assuming informative MOD related to interventions. We extended the Bayesian random-effects NMA model for binary outcomes and node-splitting approach to incorporate these 12 models in total. With interval plots, we illustrated the posterior distribution of log OR, common between-trial variance (τ2), inconsistency factor and probability of being best per intervention under each model. Results: All models gave similar point estimates for all NMA estimates regardless of simulation scenario. For moderate and large MOD, intervention-specific prior structure of log IMOR led to larger posterior standard deviation of log ORs compared to trial-specific and common-within-network prior structures. Hierarchical prior structure led to slightly more precise τ2 compared to identical prior structure, particularly for moderate inconsistency and large MOD. Pattern-mixture and selection models agreed for all NMA estimates. Conclusions: Analyzing informative MOD assuming MAR with different prior structures of log IMOR affected mainly the precision of NMA estimates. Reviewers should decide in advance on the prior structure of log IMOR that best aligns with the condition and interventions investigated.