Longitudinal mediation analysis suffers from at least two challenges.First,the mediators and outcome at a given time point affect those at later time points and they act as post-treatment confounders(or time-varying confounders).Second,Missing Not At Random(MNAR)data are commonly seen in longitudinal studies,which can lead to substantial systematic bias in the estimation of model parameters if not properly justified.To our knowledge,there is no literature simultaneously justifying time-varying confounders and MNAR data in longitudinal mediation analysis and we aimed to fill this gap.Based on potential outcome framework,we identified some regularity conditions for the identifiability of causal effects in longitudinal mediation analysis with MNAR data,and proposed a new statistical procedure based on estimating equations,multiple imputation and natural effect model to infer the causal effects.Large sample properties were established for the proposed method,and its finite sample performance was evaluated through simulations and real data analysis.
关键词
纵向中介分析/非随机缺失/时变混淆因子/多重插补/自然效应模型
Key words
longitudinal mediation analysis/missing not at random/time-varying confounders/multiple imputation/natural effect model
维普
万方数据