Statistical methods for cost-effectiveness analysis of left-truncated censored survival data with treatment delays

TL;DR

This paper introduces new statistical methods for cost-effectiveness analysis of survival data with treatment delays, using stratified Cox models to improve accuracy in public health evaluations.

Contribution

It develops estimation and inference techniques for ICER and INB that incorporate treatment delays and risk factor adjustments in survival analysis.

Findings

Proposed estimators show excellent finite sample properties in simulations.

Method effectively accounts for treatment delays in cost-effectiveness analysis.

Applied to AIDS treatment data in Tanzania, demonstrating practical utility.

Abstract

The incremental cost-effectiveness ratio (ICER) and incremental net benefit (INB) are widely used for cost-effectiveness analysis. We develop methods for estimation and inference for the ICER and INB which use the semiparametric stratified Cox proportional hazard model, allowing for adjustment for risk factors. Since in public health settings, patients often begin treatment after they become eligible, we account for delay times in treatment initiation. Excellent finite sample properties of the proposed estimator are demonstrated in an extensive simulation study under different delay scenarios. We apply the proposed method to evaluate the cost-effectiveness of switching treatments among AIDS patients in Tanzania.