English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Purpose. Patients with advanced cancer may undergo multiple lines oftreatment, switching therapies as their disease progresses. Motivated by astudy of metastatic prostate cancer, we develop a microsimulation framework tostudy therapy sequence. Methods. We propose a discrete-time state transitionmodel to study two lines of anti-cancer therapy. Based on digitized publishedprogression-free survival (PFS) and overall survival (OS) curves, we inferevent types (progression or death), and estimate transition probabilities usingcumulative incidence functions with competing risks. Our model incorporateswithin-patient dependence over time, such that response to first-line therapyinforms subsequent event probabilities. Parameters governing the degree ofwithin-patient dependence can be used to calibrate the model-based results tothose of a target trial. We demonstrate these methods in a study of two therapysequences for metastatic prostate cancer, where Docetaxel (DCT) and AbirateroneAcetate (AA) are both appropriate for use in either first or second linetreatment. We assess costs, Quality-Adjusted Life Years (QALYs) and IncrementalCost Effectiveness Ratio (ICER) for two treatment strategies: DCT then AA vs AAthen DCT. Results. Using digitized survival curves from relevant clinicaltrials, we identified 8.6-13.9% of PFS times that should be categorized asdeaths, allowing for estimation of cumulative incidence functions. Modelsassuming within-patient independence overestimated OS time, corrected with ourcalibration approach. Correction resulted in meaningful changes in thedifference in QALYs between treatment strategies (0.07 vs 0.15) and the ICER(-\$76,836/QALY vs -\$21,030/QALY). Conclusions. Microsimulation models can besuccessfully used to study cost-effectiveness of therapy sequences, taking careto account correctly for within-patient dependence.

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