BACKGROUND: In the PROSELICA, a randomized controlled trial (RCT) comparing cabazitaxel 20 mg/m2 (C20) versus 25 mg/m2 (C25) in metastatic castration-resistant prostate cancer (mCRPC), one-variable-at-a-time subgroup analysis suggested possible heterogeneity in treatment effect (HTE) of C25 versus C20 among study participants. Novel predictive HTE analysis approaches may provide an in-depth understanding of such results. METHODS: We analyzed patient-level data from 1200 patients with mCRPC who were randomized in the PROSELICA trial. Outcomes included overall survival (OS) and progression-free survival (PFS). Using baseline characteristics, patients were stratified into quartiles based on either quantitative baseline risk of poor outcome (risk modeling) or predicted individualized treatment effect (ITE) using a causal survival forest algorithm (effect modeling). Treatment effects were measured as differences in restricted mean survival time (RMST). RESULTS: For risk modeling, the OS effect of C25 increased with risk quartiles: -0.07 months (95% CI, -1.60 to 1.46) in the lowest risk quartile and 1.67 months (95% CI, 0.25 to 3.10) in the highest risk quartile. For effect modeling, the OS effect ranged from -0.17 months (95% CI, -3.01 to 2.68) in the lowest ITE quartile to 0.57 months (95% CI, -2.27 to 3.41) in the highest ITE quartile. Both approaches demonstrated greater C25 benefit in patients with extensive previous treatment and baseline disease burden. PFS effects remained consistent across all quartiles. CONCLUSIONS: The OS effect of C25 versus C20 may vary based on baseline characteristics in post-docetaxel mCRPC. Patients with extensive treatment history and disease burden may benefit more from C25.

Department of Clinical Biostatistics Institute of Science Tokyo Tokyo Japan

Department of Urology 2nd Faculty of Medicine Charles University Prague Czech Republic

Department of Urology Comprehensive Cancer Center Medical University of Vienna Vienna Austria

Department of Urology Medical University of Silesia Zabrze Poland

Department of Urology The Jikei University School of Medicine Tokyo Japan

Department of Urology University of Texas Southwestern Medical Center Dallas Texas USA

Department of Urology Weill Cornell Medical College New York New York USA

Division of Surgery and Interventional Science University College London London UK

Karl Landsteiner Institute of Urology and Andrology Vienna Austria

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Freedland S. J., Davis M., Epstein A. J., Arondekar B., and Ivanova J. I., “Real‐World Treatment Patterns and Overall Survival Among Men With Metastatic Castration‐Resistant Prostate Cancer (mCRPC) in the US Medicare Population,” Prostate Cancer and Prostatic Diseases 27, no. 2 (2024): 327–333. PubMed PMC

de Bono J. S., Oudard S., Ozguroglu M., et al., “Prednisone Plus Cabazitaxel or Mitoxantrone for Metastatic Castration‐Resistant Prostate Cancer Progressing After Docetaxel Treatment: A Randomised Open‐Label Trial,” Lancet 376, no. 9747 (2010): 1147–1154. PubMed

Eisenberger M., Hardy‐Bessard A. C., Kim C. S., et al., “Phase III Study Comparing a Reduced Dose of Cabazitaxel (20 Mg/m PubMed

Rothwell P. M., “Subgroup Analysis in Randomised Controlled Trials: Importance, Indications, and Interpretation,” Lancet 365, no. 9454 (2005): 176–186. PubMed

Assmann S. F., Pocock S. J., Enos L. E., and Kasten L. E., “Subgroup Analysis and Other (Mis)uses of Baseline Data in Clinical Trials,” Lancet 355, no. 9209 (2000): 1064–1069. PubMed

Brookes S. T., Whitley E., Peters T. J., Mulheran P. A., Egger M., and Davey Smith G., “Subgroup Analyses in Randomised Controlled Trials: Quantifying the Risks of False‐Positives and False‐Negatives,” Health Technology Assessment 5, no. 33 (2001): 1–56. PubMed

Brookes S. T., Whitely E., Egger M., Smith G. D., Mulheran P. A., and Peters T. J., “Subgroup Analyses in Randomized Trials: Risks of Subgroup‐Specific Analyses; Power and Sample Size for the Interaction Test,” Journal of Clinical Epidemiology 57, no. 3 (2004): 229–236. PubMed

Kent D. M., Rothwell P. M., Ioannidis J. P. A., Altman D. G., and Hayward R. A., “Assessing and Reporting Heterogeneity in Treatment Effects in Clinical Trials: A Proposal,” Trials 11, no. 1 (2010): 85. PubMed PMC

Kent D. M. and Hayward R. A., “Limitations of Applying Summary Results of Clinical Trials to Individual Patients: The Need for Risk Stratification,” JAMA 298, no. 10 (2007): 1209–1212. PubMed

Kent D. M., Steyerberg E., and van Klaveren D., “Personalized Evidence Based Medicine: Predictive Approaches to Heterogeneous Treatment Effects,” BMJ 363 (2018): k4245. PubMed PMC

Kent D. M., Paulus J. K., van Klaveren D., et al., “The Predictive Approaches to Treatment Effect Heterogeneity (PATH) Statement,” Annals of Internal Medicine 172, no. 1 (2020): 35–45. PubMed PMC

von Elm E., Altman D. G., Egger M., Pocock S. J., Gøtzsche P. C., and Vandenbroucke J. P., “The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: Guidelines for Reporting Observational Studies,” Journal of Clinical Epidemiology 61, no. 4 (2008): 344–349. PubMed

Kent D. M., van Klaveren D., Paulus J. K., et al., “The Predictive Approaches to Treatment Effect Heterogeneity (PATH) Statement: Explanation and Elaboration,” Annals of Internal Medicine 172, no. 1 (2020): W1–W25. PubMed PMC

Steyerberg E. W., Vickers A. J., Cook N. R., et al., “Assessing the Performance of Prediction Models: A Framework for Traditional and Novel Measures,” Epidemiology 21, no. 1 (2010): 128–138. PubMed PMC

Cui Y., Kosorok M. R., Sverdrup E., Wager S., and Zhu R., “Estimating Heterogeneous Treatment Effects With Right‐Censored Data via Causal Survival Forests,” Journal of the Royal Statistical Society. Series B, Statistical Methodology 85, no. 2 (2023): 179–211.

Wager S. and Athey S., “Estimation and Inference of Heterogeneous Treatment Effects Using Random Forests,” Journal of the American Statistical Association 113, no. 523 (2018): 1228–1242.

Chernozhukov V., Demirer M., Duflo E., and Fernández‐Val I., “Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, With an Application to Immunization in India. National Bureau of Economic Research,” (2018), 10.3386/w24678. DOI

Uroweb—European Association of Urology , “Prostate Cancer,” (2025), https://uroweb.org/guidelines/prostate‐cancer.

“Advanced Prostate Cancer: AUA/SUO Guideline—American Urological Association,” (2025), https://www.auanet.org/guidelines‐and‐quality/guidelines/advanced‐prostate‐cancer.

Singh H., Gong Y., Roy P., et al., “FDA Analysis of ECOG Performance Status and Safety Outcomes,” Journal of Clinical Oncology 38, no. 15 (2020): 12024.

Kent D. M., Nelson J., Dahabreh I. J., Rothwell P. M., Altman D. G., and Hayward R. A., “Risk and Treatment Effect Heterogeneity: Re‐Analysis of Individual Participant Data From 32 Large Clinical Trials,” International Journal of Epidemiology 45, no. 6 (2016): 2075–2088. PubMed PMC

de Wit R., de Bono J., Sternberg C. N., et al., “Cabazitaxel Versus Abiraterone or Enzalutamide in Metastatic Prostate Cancer,” New England Journal of Medicine 381, no. 26 (2019): 2506–2518. PubMed