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Causal mediation and sensitivity analysis for mixed-scale data.

Lexi Rene¹, Antonio R Linero², Elizabeth Slate¹

¹Department of Statistics, Florida State University, Tallahassee, FL, USA.

Statistical Methods in Medical Research

|May 17, 2023

Summary

This summary is machine-generated.

This study introduces a flexible parametric model for causal mediation analysis with mixed-scale outcomes. It addresses limitations of normal/Bernoulli models, enabling analysis of complex data structures.

Keywords:

Bayesian methods causal inference identification ignorability zero inflated sata

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Area of Science:

Statistics
Causal Inference
Biostatistics

Background:

Causal mediation analysis decomposes exposure effects into pathways.
Existing methods often assume normal/Bernoulli models for outcomes and mediators.
Mixed-scale, ordinal, or non-normal data present challenges for standard mediation analysis.

Purpose of the Study:

Develop a flexible parametric modeling framework for causal mediation analysis with mixed continuous and binary responses.
Extend mediation analysis to accommodate non-normal and boundary-censored data.
Apply the framework to real-world data to demonstrate its utility.

Main Methods:

Proposed a parametric modeling framework for mixed-scale outcomes and mediators.
Utilized a zero-one inflated beta model for the outcome and mediator.
Applied methods to the JOBS II dataset for estimation and sensitivity analysis.

Main Results:

Demonstrated the necessity of non-normal models for certain datasets.
Provided methods for estimating average and quantile mediation effects for boundary-censored data.
Showcased a sensitivity analysis approach using scientifically meaningful parameters.

Conclusions:

The developed framework effectively handles mixed-scale and non-normal data in causal mediation analysis.
The approach allows for robust estimation of mediation effects and sensitivity analysis.
This work expands the applicability of causal mediation analysis to a wider range of data types.