Model selection with uncertainty in estimating optimal dynamic treatment regimes

TL;DR

This paper develops a finite-sample model selection method for contrast functions in dynamic treatment regimes, using counterfactual cross-validation and variance estimation to handle uncertainty and improve clinical decision-making.

Contribution

It introduces a novel approach combining cross-validation and variance estimation to select models for DTRs, focusing on finite-sample performance rather than asymptotic properties.

Findings

The proposed method effectively quantifies uncertainty in model selection.

Simulation studies demonstrate improved model choice accuracy.

The approach aids in selecting interpretable models for clinical applications.

Abstract

Optimal dynamic treatment regimes (DTRs), as a key part of precision medicine, have progressively gained more attention recently. To inform clinical decision making, interpretable and parsimonious models for contrast functions are preferred, raising concerns about undue misspecification. It is therefore important to properly evaluate the performance of candidate interpretable models and select the one that best approximates the unknown contrast function. Moreover, since a DTR usually involves multiple decision points, an inaccurate approximation at a later decision point affects its estimation at an earlier decision point when a backward induction algorithm is applied. This paper aims to perform model selection for contrast functions in the context of learning optimal DTRs from observed data. Note that the relative performance of candidate models may heavily depend on the sample size when, for example, the comparison is made between parametric and tree-based models. Therefore, instead of investigating the limiting behavior of each candidate model and developing methods to select asymptotically the `correct' one, we focus on the finite sample performance of each model and attempt to perform model selection under a given sample size. To this end, we adopt the counterfactual cross-validation metric and propose a novel method to estimate the variance of the metric. Supplementing the cross-validation metric with its estimated variance allows us to characterize the uncertainty in model selection under a given sample size and facilitates hypothesis testing associated with a preferred model structure.