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Evaluating sensitivity to classification uncertainty in latent subgroup effect analyses.

Wen Wei Loh^1,2, Jee-Seon Kim³

¹Department of Data Analysis, Ghent University, Gent, Belgium. wen.wei.loh@emory.edu.

BMC Medical Research Methodology

|September 24, 2022

Summary

This summary is machine-generated.

This study introduces a new method to check how reliable subgroup analyses are when people

Keywords:

Causal inference Finite mixture models Latent class analysis Parametric bootstrap Perturbed confidence interval Sensitivity analysis Subgroup average treatment effect (ATE)

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Area of Science:

Causal inference
Machine learning
Statistical modeling

Background:

Assessing treatment effect heterogeneity across latent subgroups is crucial.
Latent subgroups require imputation, introducing classification uncertainty.
Ignoring this uncertainty can lead to biased results and incorrect conclusions.

Purpose of the Study:

To develop a sensitivity analysis strategy for classify-analyze subgroup effect studies.
To address the impact of classification uncertainty on subgroup-specific average causal effects.
To provide a method that avoids stringent causal assumptions.

Main Methods:

Propose a sensitivity analysis exploiting subgroup membership probabilities.
Utilize Monte Carlo confidence intervals with parametric bootstrap for imprecision.
Apply the strategy to real-world datasets with latent subgroups based on health history.

Main Results:

The proposed strategy was illustrated on two public datasets.
Latent subgroups were identified based on medical and health history.
Sensitivity analysis revealed subgroup-specific effects largely insensitive to misclassification.

Conclusions:

The sensitivity analysis is easy to implement and provides graphical/numerical summaries.
It can assess the sensitivity of machine learning causal effect estimators to classification uncertainty.
Recommends routine use of sensitivity analyses in latent subgroup effect studies.