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Semi-parametric model approach to causal mediation analysis for longitudinal data

Youjun Li & Jeffrey M. Albert

Statistical Modelling2025https://doi.org/10.1177/1471082x241306911article
ABDC B
Weight
0.37

What the paper says

There has been a lack of causal mediation analysis implementation on complicated longitudinal data. Most existing work focuses on extensions of parametric models that have been well developed for causal mediation analysis. To better handle more complex data patterns, our approach takes advantage of the flexibility of penalized splines and performs the causal mediation analysis under the structural equation model framework. We also provide the formula for identifying the natural direct and indirect effects based on our semi-parametric models, whose inference is carried out by the delta method and Monte Carlo approximation. Our approach is first evaluated by conducting simulation studies, where the two methods for inference are compared. Finally, we apply the method to data from a longitudinal cohort study to examine the effect of a training programme for healthcare providers on improving their patients' type 2 diabetes condition.

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https://doi.org/https://doi.org/10.1177/1471082x241306911

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@article{youjun2025,
  title        = {{Semi-parametric model approach to causal mediation analysis for longitudinal data}},
  author       = {Youjun Li & Jeffrey M. Albert},
  journal      = {Statistical Modelling},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1177/1471082x241306911},
}

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Semi-parametric model approach to causal mediation analysis for longitudinal data

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.