Handling missing data when estimating causal effects with targeted maximum likelihood estimation
Year: 2024
Type: article
Abstract: Targeted maximum likelihood estimation (TMLE) is increasingly used for doubly robust causal inference, but how missing data should be handled when using TMLE with data-adaptive approaches is unclear. Based on data (1992-1998) from the Victorian Adolescent Health Cohort Study, we conducted a simulation study to evaluate 8 missing-data methods in this context: complete-case analysis, extended TMLE incorporating an outcome-missingness model, the missing covariate missing indicator method, and 5 multiple imputation (MI) approaches using parametric or machine-learning models. We considered 6 scenarios that varied in terms of exposure/outcome generation models (presence of confounder-confounder interactions) and missingness mechanisms (whether outcome influenced missingness in other variables and presence of interaction/nonlinear terms in missingness models). Complete-case analysis and extended TMLE had small biases when outcome did not influence missingness in other variables. Parametric MI without interactions had large bias when exposure/outcome generation models included interactions. Parametric MI including interactions performed best in bias and variance reduction across all settings, except when missingness models included a nonlinear term. When choosing a method for handling missing data in the context of TMLE, researchers must consider the missingness mechanism and, for MI, compatibility with the analysis method. In many settings, a parametric MI approach that incorporates interactions and nonlinearities is expected to perform well.
Source: American Journal of Epidemiology
DOI: 10.1093/aje/kwae012
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $5,099
Awards: New statistical methods to maximise the impact of longitudinal cohort studies for investigating causal pathways to disease, Novel methods for analysing causal pathways to disease in longitudinal studies, Method - Analysis, Addressing new challenges with missing data in complex epidemiological studies: methods, guidance and software, A program of substantive and methodological research and capacity building in biostatistics, G4704302700, Optimising treatment and prevention strategies to accelerate malaria elimination, G9000012104
OpenAlex ID: W4392147501