Cost-effectiveness analysis for therapy sequence in advanced cancer: A microsimulation approach with application to metastatic prostate cancer

TL;DR

This study develops a microsimulation framework to evaluate the cost-effectiveness of therapy sequences in advanced cancer, specifically metastatic prostate cancer, by modeling patient transitions and incorporating within-patient dependence.

Contribution

The paper introduces a novel microsimulation approach that accounts for within-patient dependence and calibrates to target trials, improving cost-effectiveness analysis of therapy sequences.

Findings

Calibration significantly alters QALY differences.

Within-patient dependence affects survival estimates.

Cost-effectiveness varies with therapy sequence.

Abstract

Purpose. Patients with advanced cancer may undergo multiple lines of treatment, switching therapies as their disease progresses. Motivated by a study of metastatic prostate cancer, we develop a microsimulation framework to study therapy sequence. Methods. We propose a discrete-time state transition model to study two lines of anti-cancer therapy. Based on digitized published progression-free survival (PFS) and overall survival (OS) curves, we infer event types (progression or death), and estimate transition probabilities using cumulative incidence functions with competing risks. Our model incorporates within-patient dependence over time, such that response to first-line therapy informs subsequent event probabilities. Parameters governing the degree of within-patient dependence can be used to calibrate the model-based results to those of a target trial. We demonstrate these methods in a study of two therapy sequences for metastatic prostate cancer, where Docetaxel (DCT) and Abiraterone Acetate (AA) are both appropriate for use in either first or second line treatment. We assess costs, Quality-Adjusted Life Years (QALYs) and Incremental Cost Effectiveness Ratio (ICER) for two treatment strategies: DCT then AA vs AA then DCT. Results. Using digitized survival curves from relevant clinical trials, we identified 8.6-13.9% of PFS times that should be categorized as deaths, allowing for estimation of cumulative incidence functions. Models assuming within-patient independence overestimated OS time, corrected with our calibration approach. Correction resulted in meaningful changes in the difference in QALYs between treatment strategies (0.07 vs 0.15) and the ICER (-\$76,836/QALY vs -\$21,030/QALY). Conclusions. Microsimulation models can be successfully used to study cost-effectiveness of therapy sequences, taking care to account correctly for within-patient dependence.