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Updated: Jun 10, 2026

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

Targeted maximum likelihood estimation for mediation analysis with multiple time-varying mediators.

Yan-Lin Chen1, Yun-Hao Chang1, Sheng-Hsuan Lin1,2,3,4

  • 1Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan.

Biometrics
|June 8, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for analyzing how multiple time-varying factors influence health outcomes in chronic diseases. It precisely breaks down the total effect into specific pathways for better understanding complex medical feedback loops.

Keywords:
causal mediation analysismultiple time-varying mediatorspath-specific effectsrobust inferencetargeted maximum likelihood estimation

Related Experiment Videos

Last Updated: Jun 10, 2026

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction (PS-I): A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

Area of Science:

  • Epidemiological research
  • Medical research
  • Biostatistics

Background:

  • Causally ordered multiple time-varying mediation is crucial for understanding chronic diseases.
  • Existing methods struggle with reciprocal feedback loops and decomposing total effects.
  • Chronic obstructive pulmonary disease exemplifies complex mediation with dyspnea and inactivity.

Purpose of the Study:

  • To develop a novel framework for analyzing causally ordered multiple time-varying mediation.
  • To precisely decompose the total effect (TE) into path-specific effects (PSEs) for each mediator.
  • To address limitations of existing methods in capturing reciprocal feedback dynamics.

Main Methods:

  • Proposed a new framework for mediation analysis in longitudinal data.
  • Derived efficient influence functions for estimation.
  • Employed targeted maximum likelihood estimation (TMLE) for robust statistical properties.

Main Results:

  • The novel framework decomposes the total effect into path-specific effects.
  • The sum of PSEs accurately equals the total effect, resolving interpretative issues.
  • The TMLE approach ensures multiple robustness, asymptotic normality, and efficiency.

Conclusions:

  • The proposed framework provides a powerful solution for analyzing complex mediation mechanisms in longitudinal data.
  • Offers precise and clinically relevant insights into chronic disease progression.
  • Has substantial applications in medical and epidemiological research.