Semiparametric estimation of structural nested mean models with irregularly spaced longitudinal observations.

ABSTRACT: Structural nested mean models (SNMMs) are useful for causal inference of treatment effects in longitudinal observational studies. Most existing works assume that the data are collected at prefixed time points for all subjects, which, however, may be restrictive in practice. To deal with irregularly spaced observations, we assume a class of continuous-time SNMMs and a martingale condition of no unmeasured confounding (NUC) to identify the causal parameters. We develop the semiparametric efficiency theory and locally efficient estimators for continuous-time SNMMs. This task is nontrivial due to the restrictions from the NUC assumption imposed on the SNMM parameter. In the presence of ignorable censoring, we show that the complete-case estimator is optimal among a class of weighting estimator

SUBMITTER: Yang S 

PROVIDER: S-EPMC8661109 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature