Comparison of Joint and Landmark Modeling for Predicting Cancer Progression in Men With Castration-Resistant Prostate Cancer : A Secondary Post Hoc Analysis of the PREVAIL Randomized Clinical Trial

Key Points

Question

Can dynamic prediction models estimate individual radiographic progression risk in metastatic castration-resistant prostate cancer better than baseline variables alone?

Findings

This prognostic study using data from a phase 3 enzalutamide trial compared the predictive accuracy of joint and landmark models for radiographic progression-free survival among men with metastatic castration resistant prostate cancer. Models including baseline and longitudinal prostate-specific antigen changes generally produced smaller prediction errors and improved discriminative capability at follow-up.

Meaning

These findings suggest that dynamic predictions using landmark or joint models improved identification of patients with metastatic castration-resistant prostate cancer at risk of progression.

Abstract

This prognostic study assessed the ability of dynamic prediction models to aid prognosis of radiographic progression risk among men with metastatic castration-resistant prostate cancer.

Abstract

Importance

Dynamic prediction models may help predict radiographic disease progression in advanced prostate cancer.

Objective

To assess whether dynamic prediction models aid prognosis of radiographic progression risk, using ongoing longitudinal prostate-specific antigen (PSA) assessments.

Design, Setting, and Participants

This prognostic study used data from the PREVAIL study to compare dynamic models for predicting disease progression. The PREVAIL study was a phase 3, multinational, double-blind, placebo-controlled randomized clinical trial of enzalutamide for prostate cancer conducted from September 2010 to September 2012. A total of 773 men with metastatic castration-resistant prostate cancer (CRPC) who had never received chemotherapy and had no baseline visceral disease were treated with enzalutamide. For illustration, 4 patients were selected based on PSA kinetics or PSA response in case studies. Data were analyzed from July 2018 to September 2019.

Main Outcomes and Measures

Landmark and joint models were applied to dynamically predict radiographic progression–free survival (PFS) using longitudinal PSA profile, baseline PSA, lactate dehydrogenase, and hemoglobin levels. The main outcome was radiographic PFS as predicted using landmark and joint models. Current PSA and PSA change were considered longitudinal biomarkers possibly associated with radiographic PFS. Predictive performance was evaluated using Brier score for overall prediction errors (PEs) and area under the curve (AUC) for model discriminative capability. Case studies were illustrated using dynamic prediction plots.

Results

A total of 763 men with metastatic CRPC treated with enzalutamide (mean [SD] age, 71.2 [8.5] years; mean [SD] body mass index [calculated as weight in kilograms divided by height in meters squared], 28.4 [4.6]) were included in the analysis. Current PSA and PSA change were associated with radiographic PFS in all models. Adding the PSA slope, compared with the landmark models using current PSA alone, improved the prediction of 5-month prospect of radiographic progression, with relative gains of 5.7% in prediction (PE [SE], 0.132 [0.008] vs 0.140 [0.008]) and 7.7% in discrimination (AUC [SE], 0.800 [0.018] vs 0.743 [0.018]) at month 10. In joint models with linear vs nonlinear PSA, prediction of 5-month risk of radiographic progression was improved when PSA trajectories were not assumed to be linear, with 8.0% relative gain in prediction (PE [SE], 0.150 [0.006] vs 0.138 [0.005]) and 19.4% relative gain in discrimination (AUC [SE], 0.653 [0.022] vs 0.780 [0.016]) at month 10. Predictions were affected by amount of marker information accumulated and prespecified assumptions. PSA changes affected progression risk more strongly at later vs earlier follow-up.

Conclusions and Relevance

This prognostic study found that prediction of radiographic PFS was improved when longitudinal PSA information was added to baseline variables. In a population of patients with metastatic CRPC, dynamic predictions using landmark or joint models may help identify patients at risk of progression.