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Causal mediation analysis with mediator values below an assay limit.

Ariel Chernofsky¹, Ronald J Bosch², Judith J Lok³

¹Department of Biostatistics, Boston University, Boston, Massachusetts, USA.

Statistics in Medicine

|April 1, 2024

Summary

This study introduces novel methods for estimating causal effects when a biomarker mediator has assay detection limits. These approaches improve mediation analysis accuracy for HIV research and other clinical trials.

Keywords:

HIV/AIDS assay lower limit causal inference causal mediation analysis indirect and direct effects

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Area of Science:

Biostatistics
Epidemiology
Clinical Trials

Background:

Causal inference decomposes total effects into direct and indirect pathways.
Biomarkers as mediators in clinical trials can be subject to assay lower limits.
Assay limits complicate causal mediation analysis.

Purpose of the Study:

To propose and evaluate methods for estimating causal indirect and direct effects with assay-limited mediators.
To address the compounded problem of assay limits in mediation analysis.
To apply these methods to HIV research.

Main Methods:

Two novel estimation approaches: extrapolation and numerical optimization/integration.
Methods rely on the Mediation Formula, applicable to various causal mediation analyses.
A simulation study compared proposed methods against imputation with half the assay limit.

Main Results:

The proposed methods provide accurate estimation of indirect and direct effects with assay-limited mediators.
Simulation results demonstrate the performance of extrapolation and numerical optimization.
Comparison highlights advantages over simple imputation methods.

Conclusions:

The developed methods effectively handle assay lower limits in causal mediation analysis.
These techniques enhance the interpretability of treatment effects in clinical trials.
Application to HIV data demonstrates utility in estimating treatment effects on viral suppression via HIV RNA measures.