Causal Mediation Analysis for Multivariate Longitudinal Data and Survival Outcomes
Bibliographic Data
| ID | 21641956 |
|---|---|
| Authors | Xiaoxiao Zhou (0000-0001-5208-673X, Chinese University of Hong Kong), Xinyuan Song (0000-0002-4877-3200, Chinese University of Hong Kong, corresponding author) |
| Year | 2023 |
| Volume | 30 |
| Issue | 5 |
| Pages | 749-760 |
| Publication date | 2023-09-03 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Structural Equation Modeling: A Multidisciplinary Journal (JOURNAL) |
| Journal identifiers | ISSN: 1070-5511 • E-ISSN: 1532-8007 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/10705511.2022.2162406 |
| OpenAlex | W4319793906 |
| Language | EN |
| Citations received | 2 |
| References cited | 30 |
This study proposes a joint modeling approach to conduct causal mediation analysis that accommodates multivariate longitudinal data, dynamic latent mediator, and survival outcome. First, we introduce a confirmatory factor analysis model to characterize a time-varying latent mediator through multivariate longitudinal observable variables. Then, we establish a growth curve model to describe the linear trajectory of the dynamic latent mediator and simultaneously explore the relationship between the exposure and the mediating process. Finally, we link the mediating process to the survival outcome through a proportional hazards model. In addition, we use the mediation formula approach to assess the natural direct and indirect effects and prove the identifiability of the causal effects under sequential ignorability assumptions. A Bayesian approach incorporating the Markov chain Monte Carlo algorithm is developed to estimate the causal effects efficiently. Simulation studies are conducted to evaluate the empirical performance of the proposed method. An application to the Alzheimer’s Disease Neuroimaging Initiative study further confirms the utility of the proposed method
Bayesian probability · Covariate · Econometrics · Identifiability · Latent variable · Machine learning · Markov chain Monte Carlo · Mediation · Multivariate analysis · Multivariate statistics · Structural equation modeling · Advanced Causal Inference Techniques · Computer Science · Mathematics · Statistical Methods and Bayesian Inference · Statistical Methods and Inference · Artificial Intelligence
Causal Inference for Statistics, Social, and Biomedical Sciences
Latent Curve Analysis
Investigation of Mediational Processes Using Parallel Process Latent Growth Curve Modeling
Advances in Mediation Analysis
Mediation Models for Longitudinal Data in Developmental Research
General longitudinal modeling of individual differences in experimental designs
A general approach to causal mediation analysis.
Hierarchical linear models
Bayesian Causal Mediation Analysis with Latent Mediators and Survival Outcome
| Citations per year | 1 |
|---|---|
| Citation span | 2024 - 2026 (3) |
| Highly cited | No |