# On Prediction and Tolerance Intervals for Dynamic Treatment Regimes

## TL;DR

This paper develops and evaluates methods for constructing prediction and tolerance intervals in dynamic treatment regimes, providing more detailed prognostic information for patients following estimated optimal treatments.

## Contribution

It introduces adaptation of interval estimation methods to DTRs, addressing challenges due to data limitations and offering practical evaluation and application insights.

## Key findings

- Effective tolerance interval methods for DTRs are proposed.
- Empirical evaluation demonstrates the methods' practical utility.
- Application to clinical trial data illustrates real-world relevance.

## Abstract

We develop and evaluate tolerance interval methods for dynamic treatment regimes (DTRs) that can provide more detailed prognostic information to patients who will follow an estimated optimal regime. Although the problem of constructing confidence intervals for DTRs has been extensively studied, prediction and tolerance intervals have received little attention. We begin by reviewing in detail different interval estimation and prediction methods and then adapting them to the DTR setting. We illustrate some of the challenges associated with tolerance interval estimation stemming from the fact that we do not typically have data that were generated from the estimated optimal regime. We give an extensive empirical evaluation of the methods and discussed several practical aspects of method choice, and we present an example application using data from a clinical trial. Finally, we discuss future directions within this important emerging area of DTR research.