Simulation Study and Case Validation on Causal Inference of g-computation-based Joint Mixed-effects Model for Controlling Unmeasured Confounders
10.11783/j.issn.1002-3674.2024.05.012
- VernacularTitle:基于g-computation联合混合效应模型控制未测混杂因素的因果推断方法模拟研究及实例验证
- Author:
Boran SUN
1
;
Wenli LU
;
Yongjie CHEN
Author Information
1. 天津医科大学流行病与卫生统计学系(300070)
- Keywords:
Longitudinal studies;
Unmeasured confounders;
g-computation;
Joint mixed-effects model
- From:
Chinese Journal of Health Statistics
2024;41(5):691-696
- CountryChina
- Language:Chinese
-
Abstract:
Objective A simulation study was conducted to explore the effect and performance of g-computation-based joint mixed-effects model(JMM)on causal inference for controlling unmeasured confounders in longitudinal studies.Methods Longitudinal data including baseline and two follow-up visits were generated by computer simulations.The simulation scenarios included different sample sizes,the presence or absence of unmeasured confounders,and effects of unmeasured confounders.Causal effects were estimated using g-computation-based JMM,linear mixed-effects model,fixed effects model,and longitudinal target maximum likelihood estimation,respectively.Indicators including mean absolute deviation(MAD),standard error,root mean square error(RMSE),and 95%confidence interval coverage(95%CI coverage)were used to evaluate and compare the causal inference performance.Based on the physical examination cohort data of the menopausal women,four models were used to estimate the causal association between serum follicle-stimulating hormone(FSH)levels and lumbar bone density in menopausal women respectively,verifying the causal inference performance of models in the real longitudinal data.Results JMM had a better accuracy of causal inference with controlling unmeasured confounders.But its estimation stability was slightly worse.When strong unmeasured confounders existed,only JMM can accurately estimate the causal effect,and its precision and authenticity were better in scenarios with large sample sizes.Conclusion JMM can effectively control the unmeasured confounders and perform approximately unbiased causal estimation in longitudinal studies.
