Related Experiment Video
Updated: Aug 19, 2025

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
Published on: September 11, 2021
Causal Mediation Analysis with Multiple Time-varying Mediators
An-Shun Tai1,2, Sheng-Hsuan Lin2, Yu-Cheng Chu2
1From the Department of Statistics, National Cheng Kung University, Tainan, Taiwan.
None:
In longitudinal studies with time-varying exposures and mediators, the mediational g-formula is an important method for the assessment of direct and indirect effects. However, current methodologies based on the mediational g-formula can deal with only one mediator. This limitation makes these methodologies inapplicable to many scenarios. Hence, we develop a novel methodology by extending the mediational g-formula to cover cases with multiple time-varying mediators. We formulate two variants of our approach that are each suited to a distinct set of assumptions and effect definitions and present nonparametric identification results of each variant. We further show how complex causal mechanisms (whose complexity derives from the presence of multiple time-varying mediators) can be untangled. We implemented a parametric method, along with a user-friendly algorithm, in R software. We illustrate our method by investigating the complex causal mechanism underlying the progression of chronic obstructive pulmonary disease. We found that the effects of lung function impairment mediated by dyspnea symptoms accounted for 14.6% of the total effect and that mediated by physical activity accounted for 11.9%. Our analyses thus illustrate the power of this approach, providing evidence for the mediating role of dyspnea and physical activity on the causal pathway from lung function impairment to health status. See video abstract at, http://links.lww.com/EDE/B988 .
More Related Videos
Related Concept Videos
Censoring Survival Data
Causality in Epidemiology
Comparing the Survival Analysis of Two or More Groups
Criteria for Causality: Bradford Hill Criteria - II
Multiple Regression
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Friedman Two-way Analysis of Variance by Ranks

