Constructing optimal treatment length strategies to maximize quality-adjusted lifetimes

TL;DR

This paper develops new statistical methods to determine optimal treatment durations that maximize quality-adjusted lifetimes, accounting for complex censoring and confounding in clinical survival data.

Contribution

It introduces a weighted estimating equation and a nonparametric estimator for optimal treatment length strategies in quality-adjusted survival analysis, addressing limitations of existing methods.

Findings

The proposed estimator is asymptotically linear.

Application to ALS patients identified optimal gastrostomy timing.

Method effectively adjusts for informative censoring and confounding.

Abstract

Real-world clinical decision making is a complex process that involves balancing the risks and benefits of treatments. Quality-adjusted lifetime is a composite outcome that combines patient quantity and quality of life, making it an attractive outcome in clinical research. We propose methods for constructing optimal treatment length strategies to maximize this outcome. Existing methods for estimating optimal treatment strategies for survival outcomes cannot be applied to a quality-adjusted lifetime due to induced informative censoring. We propose a weighted estimating equation that adjusts for both confounding and informative censoring. We also propose a nonparametric estimator of the mean counterfactual quality-adjusted lifetime survival curve under a given treatment length strategy, where the weights are estimated using an undersmoothed sieve-based estimator. We show that the estimator is asymptotically linear and provide a data-dependent undersmoothing criterion. We apply our method to obtain the optimal time for percutaneous endoscopic gastrostomy insertion in patients with amyotrophic lateral sclerosis.