Comparison of Systemic Treatments for Metastatic Castration-Sensitive Prostate Cancer: A Systematic Review and Network Meta-analysis | Oncology | JAMA Oncology | JAMA Network
[Skip to Navigation]
Sign In
Figure 1.  Network Graph of Treatment Comparison
Network Graph of Treatment Comparison

Graph depicts underlying evidence base of this study. Nodes (circles) represent competing treatments added to androgen-deprivation therapy and edges (lines) show which treatments have been compared. Node size proportional to the number of trials evaluating each treatment, edge thickness proportional to precision (the inverse of the variance of hazard ratios of overall survival) of each direct comparison. The labels on the edges are randomized clinical trials of pairs of treatments. Edges with gray color represent multiarm Systemic Therapy in Advancing or Metastatic Prostate Cancer: Evaluation of Drug Efficacy (STAMPEDE) trial. Study names are expanded in the footnotes to Table 1.

Figure 2.  Treatment Ranking and Relative Effect
Treatment Ranking and Relative Effect

CI indicates credible interval; HR, hazard ratio; OR, odds ratio.

Figure 3.  Overall Survival and Radiographic Progression-Free Survival Based on Relative Treatment Effect Estimates
Overall Survival and Radiographic Progression-Free Survival Based on Relative Treatment Effect Estimates

Overall (A) and progression-free survival (B). Median indicated by solid lines; 95% credible intervals indicated by shaded areas.

Table 1.  Characteristics of Included Trials
Characteristics of Included Trials
Table 2.  Risk of Bias Within Trials
Risk of Bias Within Trials
1.
US Cancer Society. Prostate at a glance. Estimated new cases, 2020. Accessed May 28, 2020. https://cancerstatisticscenter.cancer.org/#!/cancer-site/Prostate
2.
Cancer Stat Facts SEER. Prostate cancer. National Cancer Institute. Accessed May 28, 2020. https://seer.cancer.gov/statfacts/html/prost.html
3.
Alpajaro  SIR, Harris  JAK, Evans  CP.  Non-metastatic castration resistant prostate cancer: a review of current and emerging medical therapies.   Prostate Cancer Prostatic Dis. 2019;22(1):16-23. doi:10.1038/s41391-018-0078-1 PubMedGoogle ScholarCrossref
4.
Sartor  O, de Bono  JS.  Metastatic prostate cancer.   N Engl J Med. 2018;378(7):645-657. doi:10.1056/NEJMra1701695 PubMedGoogle ScholarCrossref
5.
Sweeney  CJ, Chen  YH, Carducci  M,  et al.  Chemohormonal therapy in metastatic hormone–sensitive prostate cancer.   N Engl J Med. 2015;373(8):737-746. doi:10.1056/NEJMoa1503747 PubMedGoogle ScholarCrossref
6.
James  ND, Sydes  MR, Clarke  NW,  et al; STAMPEDE investigators.  Addition of docetaxel, zoledronic acid, or both to first-line long-term hormone therapy in prostate cancer (STAMPEDE): survival results from an adaptive, multiarm, multistage, platform randomised controlled trial.   Lancet. 2016;387(10024):1163-1177. doi:10.1016/S0140-6736(15)01037-5 PubMedGoogle ScholarCrossref
7.
Fizazi  K, Tran  N, Fein  L,  et al; LATITUDE Investigators.  Abiraterone plus prednisone in metastatic, castration-sensitive prostate cancer.   N Engl J Med. 2017;377(4):352-360. doi:10.1056/NEJMoa1704174 PubMedGoogle ScholarCrossref
8.
James  ND, de Bono  JS, Spears  MR,  et al; STAMPEDE Investigators.  Abiraterone for prostate cancer not previously treated with hormone therapy.   N Engl J Med. 2017;377(4):338-351. doi:10.1056/NEJMoa1702900 PubMedGoogle ScholarCrossref
9.
Fizazi  K, Tran  N, Fein  L,  et al.  Abiraterone acetate plus prednisone in patients with newly diagnosed high-risk metastatic castration-sensitive prostate cancer (LATITUDE): final overall survival analysis of a randomised, double-blind, phase 3 trial.   Lancet Oncol. 2019;20(5):686-700. doi:10.1016/S1470-2045(19)30082-8 PubMedGoogle ScholarCrossref
10.
Hoyle  AP, Ali  A, James  ND,  et al; STAMPEDE Investigators.  Abiraterone in “high-” and “low-risk” metastatic hormone-sensitive prostate cancer.   Eur Urol. 2019;76():719-728. doi:10.1016/j.eururo.2019.08.006 PubMedGoogle ScholarCrossref
11.
Armstrong  AJ, Szmulewitz  RZ, Petrylak  DP,  et al.  ARCHES: a randomized, phase III study of androgen deprivation therapy with enzalutamide or placebo in men with metastatic hormone-sensitive prostate cancer.   J Clin Oncol. 2019;37(32):2974-2986. doi:10.1200/JCO.19.00799 PubMedGoogle ScholarCrossref
12.
Chi  KN, Agarwal  N, Bjartell  A,  et al; TITAN Investigators.  Apalutamide for metastatic, castration-sensitive prostate cancer.   N Engl J Med. 2019;381(1):13-24. doi:10.1056/NEJMoa1903307 PubMedGoogle ScholarCrossref
13.
European Association of Oncology. Guidelines on prostate cancer. Full-text guidelines. Published 2020. Accessed May 28, 2020 https://uroweb.org/guidelin.e/prostate-cancer/
14.
National Comprehensive Cancer Network. Prostate cancer: version 2.2020. Accessed May 28, 2020. https://www.nccn.org/professionals/physician_gls/pdf/prostate.pdf
15.
US Department of Veterans Affairs National Acquisition Center (CCST). Published 2019. Accessed October 27, 2019. https://www.vendorportal.ecms.va.gov/nac/Pharma/List
16.
Moher  D, Liberati  A, Tetzlaff  J, Altman  DG; PRISMA Group.  Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement.   PLoS Med. 2009;6(7):e1000097. doi:10.1371/journal.pmed.1000097 PubMedGoogle Scholar
17.
Hutton  B, Salanti  G, Caldwell  DM,  et al.  The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations.   Ann Intern Med. 2015;162(11):777-784. doi:10.7326/M14-2385 PubMedGoogle ScholarCrossref
18.
Sterne  JAC, Savović  J, Page  MJ,  et al.  RoB 2: a revised tool for assessing risk of bias in randomised trials.   BMJ. 2019;366:l4898. doi:10.1136/bmj.l4898 PubMedGoogle ScholarCrossref
19.
Sofia Dias  AEA, Welton NJ, Jansen JP, Sutton AJ. Network Meta-Analysis for Decision-Making (Statistics in Practice). Wiley; 2018.
20.
Franchini  AJ, Dias  S, Ades  AE, Jansen  JP, Welton  NJ.  Accounting for correlation in network meta-analysis with multi-arm trials.   Res Synth Methods. 2012;3(2):142-160. doi:10.1002/jrsm.1049 PubMedGoogle ScholarCrossref
21.
Rohatigi  A. WebPlotDigitizer, version 4.3. Published 2020. Accessed October 23, 2020. https://automeris.io/WebPlotDigitizer
22.
Guyot  P, Ades  AE, Ouwens  MJ, Welton  NJ.  Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan-Meier survival curves.   BMC Med Res Methodol. 2012;12:9. doi:10.1186/1471-2288-12-9 PubMedGoogle ScholarCrossref
23.
Wei  Y, Royston  P.  Reconstructing time-to-event data from published Kaplan-Meier curves.   Stata J. 2017;17(4):786-802. doi:10.1177/1536867X1801700402 PubMedGoogle ScholarCrossref
24.
Jansen  JP.  Network meta-analysis of survival data with fractional polynomials.   BMC Med Res Methodol. 2011;11:61. doi:10.1186/1471-2288-11-61 PubMedGoogle ScholarCrossref
25.
Spiegelhalter  DJ, Best  N, Carlin  BP, Linde  A.  Bayesian measures of model complexity and fit (with discussion).   J Royal Stat Soc. 2002;64:583-639. doi:10.1111/1467-9868.00353 Google ScholarCrossref
26.
Van Valkenhoef  G, Kuiper  J. gemtc: network meta-analysis using bayesian methods. R package, version 0.8-4. Updated August 10, 2020. Accessed May 28, 2020. https://CRAN.R-project.org/package=gemtc
27.
R Foundation for Statistical Computing. R: a language and environment for statistical computing. Published 2019. Accessed May 28, 2020. https://www.R-project.org/
28.
Lunn  DJ, Thomas  A, Best  N, Spiegelhalter  D.  WinBUGS: a bayesian modelling framework: concepts, structure, and extensibility.   Stat Comput. 2000;10:325-337. doi:10.1023/A:1008929526011Google ScholarCrossref
29.
Dias  S, Welton  NJ, Caldwell  DM, Ades  AE.  Checking consistency in mixed treatment comparison meta-analysis.   Stat Med. 2010;29(7-8):932-944. doi:10.1002/sim.3767 PubMedGoogle ScholarCrossref
30.
Van Valkenhoef  G, Dias  S, Ades  AE, Welton  NJ.  Automated generation of node-splitting models for assessment of inconsistency in network meta-analysis.   Res Synth Methods. 2016;7(1):80-93. doi:10.1002/jrsm.1167 PubMedGoogle ScholarCrossref
31.
Gelman  A. Inference and monitoring convergence. In:  Markov Chain Monte Carlo in Practice. London: Chapman & Hall; 1996.
32.
Brooks  S, Gelman  A.  General methods for monitoring convergence of iterative simulations.   J Comput Graph Stat. 1998;7:434-455.Google Scholar
33.
Sydes  MR, Spears  MR, Mason  MD,  et al; STAMPEDE Investigators.  Adding abiraterone or docetaxel to long-term hormone therapy for prostate cancer: directly randomised data from the STAMPEDE multi-arm, multi-stage platform protocol.   Ann Oncol. 2018;29(5):1235-1248. doi:10.1093/annonc/mdy072 PubMedGoogle ScholarCrossref
34.
Gravis  G, Fizazi  K, Joly  F,  et al.  Androgen-deprivation therapy alone or with docetaxel in non-castrate metastatic prostate cancer (GETUG-AFU 15): a randomised, open-label, phase 3 trial.   Lancet Oncol. 2013;14(2):149-158. doi:10.1016/S1470-2045(12)70560-0 PubMedGoogle ScholarCrossref
35.
Davis  ID, Martin  AJ, Stockler  MR,  et al; ENZAMET Trial Investigators and the Australian and New Zealand Urogenital and Prostate Cancer Trials Group.  Enzalutamide with standard first-line therapy in metastatic prostate cancer.   N Engl J Med. 2019;381(2):121-131. doi:10.1056/NEJMoa1903835 PubMedGoogle ScholarCrossref
36.
Kyriakopoulos  CE, Chen  YH, Carducci  MA,  et al.  Chemohormonal therapy in metastatic hormone-sensitive prostate cancer: long-term survival analysis of the randomized phase III E3805 CHAARTED trial.   J Clin Oncol. 2018;36(11):1080-1087. doi:10.1200/JCO.2017.75.3657 PubMedGoogle ScholarCrossref
37.
Morgans  AK, Chen  YH, Sweeney  CJ,  et al.  Quality of life during treatment with chemohormonal therapy: analysis of E3805 chemohormonal androgen ablation randomized trial in prostate cancer.   J Clin Oncol. 2018;36(11):1088-1095. doi:10.1200/JCO.2017.75.3335 PubMedGoogle ScholarCrossref
38.
Gravis  G, Boher  JM, Joly  F,  et al; GETUG.  Androgen deprivation therapy (ADT) plus docetaxel versus ADT alone in metastatic non castrate prostate cancer: impact of metastatic burden and long-term survival analysis of the randomized phase 3 GETUG-AFU15 Trial.   Eur Urol. 2016;70(2):256-262. doi:10.1016/j.eururo.2015.11.005 PubMedGoogle ScholarCrossref
39.
Chi  KN, Protheroe  A, Rodríguez-Antolín  A,  et al.  Patient-reported outcomes following abiraterone acetate plus prednisone added to androgen deprivation therapy in patients with newly diagnosed metastatic castration-naive prostate cancer (LATITUDE): an international, randomised phase 3 trial.   Lancet Oncol. 2018;19(2):194-206. doi:10.1016/S1470-2045(17)30911-7 PubMedGoogle ScholarCrossref
40.
Fukasawa  S, Suzuki  H, Kawaguchi  K,  et al.  Efficacy and safety of abiraterone acetate plus prednisone in Japanese patients with newly diagnosed, metastatic hormone-naïve prostate cancer: a subgroup analysis of LATITUDE, a randomized, double-blind, placebo-controlled, Phase 3 study.   Jpn J Clin Oncol. 2018;48(11):1012-1021. doi:10.1093/jjco/hyy129 PubMedGoogle ScholarCrossref
41.
Agarwal  N, McQuarrie  K, Bjartell  A,  et al; TITAN investigators.  Health-related quality of life after apalutamide treatment in patients with metastatic castration-sensitive prostate cancer (TITAN): a randomised, placebo-controlled, phase 3 study.   Lancet Oncol. 2019;20(11):1518-1530. doi:10.1016/S1470-2045(19)30620-5 PubMedGoogle ScholarCrossref
42.
Clarke  NW, Ali  A, Ingleby  FC,  et al.  Addition of docetaxel to hormonal therapy in low- and high-burden metastatic hormone sensitive prostate cancer: long-term survival results from the STAMPEDE trial.   Ann Oncol. 2019;30(12):1992-2003. doi:10.1093/annonc/mdz396 PubMedGoogle ScholarCrossref
43.
Vale  CL, Fisher  DJ, White  IR,  et al.  What is the optimal systemic treatment of men with metastatic, hormone-naive prostate cancer? a STOPCAP systematic review and network meta-analysis.   Ann Oncol. 2018;29(5):1249-1257. doi:10.1093/annonc/mdy071 PubMedGoogle ScholarCrossref
44.
Sathianathen  NJ, Koschel  S, Thangasamy  IA,  et al.  Indirect comparisons of efficacy between combination approaches in metastatic hormone-sensitive prostate cancer: a systematic review and network meta-analysis.   Eur Urol. 2020;77(3):365-372. doi:10.1016/j.eururo.2019.09.004 PubMedGoogle ScholarCrossref
45.
Wang  L, Hong  H, Paller  C, Brawley  O, Li  T. I s current trial data sharing status conducive for evidence generation for personalized medicine: a failed attempt to conduct an individual pattient trial data network meta-analysis.   Drugs Generics Org Pract. 2020;23(suppl 1):S139-S140. doi:10.1016/j.jval.2020.04.345 Google Scholar
Limit 200 characters
Limit 25 characters
Conflicts of Interest Disclosure

Identify all potential conflicts of interest that might be relevant to your comment.

Conflicts of interest comprise financial interests, activities, and relationships within the past 3 years including but not limited to employment, affiliation, grants or funding, consultancies, honoraria or payment, speaker's bureaus, stock ownership or options, expert testimony, royalties, donation of medical equipment, or patents planned, pending, or issued.

Err on the side of full disclosure.

If you have no conflicts of interest, check "No potential conflicts of interest" in the box below. The information will be posted with your response.

Not all submitted comments are published. Please see our commenting policy for details.

Limit 140 characters
Limit 3600 characters or approximately 600 words
    Original Investigation
    January 14, 2021

    Comparison of Systemic Treatments for Metastatic Castration-Sensitive Prostate Cancer: A Systematic Review and Network Meta-analysis

    Author Affiliations
    • 1Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
    • 2Center for Drug Safety and Effectiveness, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
    • 3Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland
    • 4Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina
    • 5Duke Clinical Research Institute, Duke University, Durham, North Carolina
    JAMA Oncol. 2021;7(3):412-420. doi:10.1001/jamaoncol.2020.6973
    Key Points

    Question  What are the most effective systemic treatments for metastatic castration-sensitive prostate cancer?

    Findings  This network meta-analysis of 7 randomized clinical trials including 7287 patients noted that, combined with androgen-deprivation therapy, treatments associated with significantly improved overall survival included abiraterone acetate, apalutamide, and docetaxel; treatments associated with significantly improved radiographic progression-free survival included enzalutamide, abiraterone acetate, apalutamide, and docetaxel, ordered from the agent with the greatest to least effectiveness according to the results of clinical trials. Docetaxel was associated with substantially increased serious adverse events, abiraterone with slightly increased serious adverse events, and other treatments with no increase in serious adverse events.

    Meaning  This network meta-analysis suggests that abiraterone acetate and apalutamide may provide the largest and most consistent overall survival benefits with relatively low serious adverse event risks among metastatic castration-sensitive prostate cancer treatments.

    Abstract

    Importance  Multiple systemic treatments are available for metastatic castration-sensitive prostate cancer (mCSPC), with unclear comparative effectiveness and safety and widely varied costs.

    Objective  To compare the effectiveness and safety determined in randomized clinical trials of systemic treatments for mCSPC.

    Data Sources  Bibliographic databases (MEDLINE, Embase, and Cochrane Central), regulatory documents (US Food and Drug Administration and European Medicines Agency), and trial registries (ClinicalTrials.gov and European Union clinical trials register) were searched from inception through November 5, 2019.

    Study Selection, Data Extraction, and Synthesis  Eligible studies were randomized clinical trials evaluating the addition of docetaxel, abiraterone acetate, apalutamide, or enzalutamide to androgen-deprivation therapy (ADT) for treatment of mCSPC. Two investigators independently performed screening. Discrepancies were resolved through consensus. A Cochrane risk-of-bias tool was used to assess trial quality. Relative effects of competing treatments were assessed by bayesian network meta-analysis and survival models. The Preferred Reporting Items for Systematic Reviews and Meta-analyses guideline was used.

    Main Outcomes and Measures  Overall survival, radiographic progression-free survival, and serious adverse events (SAEs).

    Results  Seven trials with 7287 patients comparing 6 treatments (abiraterone acetate, apalutamide, docetaxel, enzalutamide, standard nonsteroidal antiandrogen, and placebo/no treatment) were identified. Ordered from the most to the least effective determined by results of clinical trials, treatments associated with improved overall survival when added to ADT included abiraterone acetate (hazard ratio [HR], 0.61; 95% credible interval [CI], 0.54-0.70), apalutamide (HR, 0.67; 95% CI, 0.51-0.89), and docetaxel (HR, 0.79; 95% CI, 0.71-0.89); treatments associated with improved radiographic progression-free survival when added to ADT included enzalutamide (HR, 0.39; 95% CI, 0.30-0.50), apalutamide (HR, 0.48; 95% CI, 0.39-0.60), abiraterone acetate (HR, 0.51; 95% CI, 0.45-0.58), and docetaxel (HR, 0.67; 95% CI 0.60-0.74). Docetaxel was associated with substantially increased SAEs (odds ratio, 23.72; 95% CI, 13.37-45.15), abiraterone acetate with slightly increased SAEs (odds ratio, 1.42; 95% CI, 1.10-1.83), and other treatments with no significant increase in SAEs. Risk of bias was noted for 4 trials with open-label design, 3 trials with missing data, and 2 trials with potential unprespecified analyses.

    Conclusions and Relevance  In this network meta-analysis, as add-on treatments to ADT, abiraterone acetate and apalutamide may provide the largest overall survival benefits with relatively low SAE risks. Although enzalutamide may improve radiographic progression-free survival to the greatest extent, longer follow-up is needed to examine the overall survival benefits associated with enzalutamide.

    Introduction

    Prostate cancer is the most common cancer and the second leading cause of cancer death among men in the US.1Quiz Ref ID Although 76% of the patients with prostate cancer were first diagnosed with localized cancer,2 approximately 30% of these patients experience disease recurrence after definitive treatments.3 Most recurrent prostate cancers initially respond to androgen-deprivation therapy (ADT) but eventually develop resistance, transforming from castration-sensitive to castration-resistant prostate cancer.4 Metastatic castration-resistant prostate cancer is associated with high mortality: a 5-year survival rate of 30%.2

    Progress in research has led to several promising treatments that, when added to ADT, delay disease progression to metastatic castration-resistant prostate cancer (mCRPC). These drugs include the taxane docetaxel,5,6 androgen synthesis inhibitor abiraterone acetate,7-10 and androgen receptor inhibitors apalutamide and enzalutamide.11,12 The availability of these drugs has improved prostate cancer survival. However, owing to the lack of head-to-head trials comparing active treatments, little is known about the optimal choice weighing effectiveness and safety. As a result, clinical guideline committees hesitate to recommend one drug over others.13,14

    In addition, drug costs vary widely. The range of drug acquisition costs for patients to complete all planned courses of treatment (18 weeks for docetaxel and a median treatment duration of 2 years for other drugs) is $627 for docetaxel, $62 714 for generic abiraterone acetate, $175 438 for enzalutamide (Xtandi), and $231 789 for apalutamide (Erleada).15 This study aimed to compare the effectiveness and safety of systemic treatments for mCSPC determined in randomized clinical trials (RCTs) to inform decision-making.

    Methods
    Eligibility Criteria

    The study protocol was registered in PROSPERO (the International Prospective Register of Systematic Reviews, CRD42020160839). We included RCTs of parallel design for mCSPC and excluded cluster and dose-escalation trials. The interventions of interest were docetaxel, abiraterone acetate, apalutamide, and enzalutamide; the comparator of interest was any active drug, placebo, or no treatment—all in addition to ADT. Androgen-deprivation therapy includes orchiectomy, luteinizing hormone-releasing hormone agonists and antagonists, and estrogen. We combined different dose regimens of the same drug, combined placebo with no treatment, and required a median follow-up of at least 12 months. Trial registrations without results, published trial protocols, and abstracts were excluded. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline and its extension for network meta-analysis.16,17

    Data Sources and Extraction

    We searched bibliographic databases (MEDLINE [PubMed interface], EMBASE [OVID interface]), the Cochrane Central Register of Controlled Trials (CENTRAL, Wiley interface), trial registries (ClinicalTrials.gov and the EU Clinical Trials Register), and regulatory documents (US Food and Drug Administration and European Medicines Agency review packets) from inception to November 5, 2019, with no language or date restrictions. Search strategies in eMethods 1 in the Supplement.

    We used Sysrev for title and abstract screening and Endnote X9 (Clarivate Analytics) for full-text screening. Two investigators (L.W. and A.D.F.) independently performed the screening. One investigator (L.W.) extracted data from included trials and the second investigator (C.J.P.) checked the extracted data. Discrepancies were resolved through consensus. Data extracted included trial design, interventions, outcomes, baseline characteristics, and results (eTable 1 in the Supplement). The efficacy outcomes of interest were overall survival (OS) and radiographic progression-free survival (rPFS), defined as time from randomization to radiographic progression or death from any cause, whichever occurred first. The safety outcome of interest was any serious adverse events (SAEs).

    Risk of Bias Assessment

    We assessed the risk of bias of individual trials for effectiveness outcomes using the Cochrane Collaboration’s tool (version 2.0).18 The overall bias of a trial was assessed from 5 domains: randomization process, deviation from intended interventions, missing outcome data, measurement of the outcome, and selection of reported results. The overall bias was judged to be low if all domains were at low risk of bias and high if at least 1 domain was at high risk of bias or multiple domains raised concerns. Judgments were made independently by 2 investigators (L.W. and G.C.A.). Disagreements were resolved by discussion. Risk of bias assessment was incorporated into our interpretation of results.

    Statistical Analysis

    Bayesian network meta-analysis was performed using generalized linear models.19 We used multivariate normal distribution to account for between-arm correlations in multiarm trials.19,20 We fitted fixed- and random-effects models, with the latter accounting for between-study heterogeneity. We report results from fixed-effects models because, for treatment comparisons examined in RCTs, 4 of the 6 comparisons were examined in only 1 trial. In addition, the results from fixed- and random-effects models were consistent, with only wider 95% credible intervals (CIs) noted for the random-effects models.

    As primary analysis for OS and rPFS, time-invariant hazard ratios (HRs) between treatment arms from individual trials were analyzed to estimate the overall HRs. For patient subgroups consistently examined across trials, we performed subgroup analyses to evaluate how comparative effectiveness varied by patient characteristics. For SAEs, the number of events in individual trial arms was analyzed to estimate the overall odds ratios (ORs) between treatments.

    As secondary analysis for OS and rPFS, we estimated time-varying HRs by bayesian parametric survival network meta-analysis and compared expected survival curves across treatments. Specifically, published Kaplan-Meier curves were digitalized using Web PlotDigitizer, version 4.2.21 The individual level time-to-event data were reconstructed using Guyot algorithms.22 and the Stata, version (StataCorp LLC) command ipdfc.23 We fit a series of first-order fractional polynomial models with power parameters −2, −1, −0.5, 0.5, 1, 2, and 3, which include common survival distributions, such as Weibull (power parameter = 0) and Gompertz (power parameter = 1).24 The deviance information criterion was used to assess model fit.25

    Bayesian models estimate treatment effects via Markov chain Monte Carlo algorithms. Noninformative priors were used to allow the observed trial data to explain effect estimates.19 For primary analysis, we used the gemtc package26 in R, version 3.6.2,27 with 4 parallel Markov chains consisting of 100 000 samples after a 5000-sample burn-in. For secondary analysis, we used WinBUGS, version 1.4.3,28 with 3 parallel Markov chains consisting of 50 000 samples after a 5000-sample burn-in. We checked the statistical consistency between direct (head-to-head RCTs) and indirect (treatments sharing common comparators) evidence by fitting node-splitting models via the R gemtc package and the z test.26,29,30 Convergence of Markov chains was checked by trace plots and Gelman-Rubin diagnostic statistics.31,32 The significance level was α = .05 for statistical tests. Statistical models and WinBUGS code are available in eMethods 2 in the Supplement).

    Results
    Study Selection and Network Geometry

    A total of 8424 unique study records were identified, including 7582 publication citations, 800 trial registrations, and 42 trial regulatory records. Full-text screening was done for 103 publication citations and all trial registrations and trial regulatory records (eFigure 1 in the Supplement).

    Seven trials comparing 6 treatments were analyzed (Table 1),5-12,33-42 including placebo/no treatment, a standard nonsteroidal antiandrogen (bicalutamide, nilutamide, or flutamide), docetaxel, abiraterone acetate, enzalutamide, and apalutamide. A network graph of treatment comparisons is presented in Figure 1, with nodes representing competing treatments and edges representing RCTs for pairs of treatments. The most studied treatments were docetaxel (3 trials), abiraterone acetate (2 trials), and enzalutamide (2 trials). Quiz Ref IDSix RCTs5,7,11,12,33,34 used placebo/no treatment as the comparator and 1 trial35 used standard nonsteroidal antiandrogen therapy. Active treatments have not been compared in head-to-head trials except for docetaxel and abiraterone acetate, which were compared in the only multiarm RCT: the Systemic Therapy in Advancing or Metastatic Prostate Cancer: Evaluation of Drug Efficacy (STAMPEDE) trial.33

    Characteristics of Included Trials

    The 7 included trials were multicenter phase 3 RCTs published between 2013 and 2019, involving a total of 7287 patients (Table 1). The median sample size was 1125 (range, 385-1586) patients; the median duration of follow-up was 52 months (range, 14-84). The main eligibility criteria entailed newly diagnosed prostate adenocarcinoma with radiologic evidence of metastatic disease and adequate performance status. Previous chemotherapy and hormone therapy in the metastatic setting were either prohibited or restricted. The STAMPEDE trial recruited a broader patient population; we used only mCSPC data in this analysis. The LATITUDE trial required at least 2 high prognostic risk factors for eligibility7,9; we assessed the association between these factors and outcomes by subgroup analysis.

    Treatment and Assessment of Outcomes

    Treatments were given until disease progression or prohibitive toxic effects occurred. Docetaxel, 75 mg/m2, was given every 3 weeks for 6 cycles with premedication or concurrent use of corticosteroids, with the exception of the GETUG-AFU15 trial, in which patients received a median of 8 cycles of docetaxel.34 Abiraterone acetate, 1000 mg/d, was given with concurrent corticosteroids; the other treatments were apalutamide, 240 mg/d, and enzalutamide, 160 mg/d.

    All included trials assessed OS and SAEs, 4 trials assessed rPFS, and 3 trials assessed a modified version of rPFS. Specifically, the STAMPEDE trial33 examined progression-free survival, defined as the time from randomization to radiographic progression or death from prostate cancer, whichever occurred first. The ChemoHormonal Therapy Versus Androgen Ablation Randomized Trial for Extensive Disease in Prostate Cancer (CHAARTED) and Enzalutamide in First Line Androgen Deprivation Therapy for Metastatic Prostate Cancer (ENZAMET) trials5,35 assessed clinical progression-free survival, comprising radiographic progression, symptomatic progression/initiation of new anticancer treatment, or death as the failure events. We combined rPFS and modified rPFS, assuming that radiographic progression occurs earlier than symptomatic progression or initiation of new anticancer treatment and death from other causes. Trials also assessed prostate-specific antigen progression-free survival, time to pain progression, and other outcomes (eTable 2 in the Supplement). However, the inconsistency in outcome measures across trials precluded treatment comparison based on these outcomes.

    Risk of Bias

    For rPFS, all 7 trials raised some concerns regarding the overall risk of bias. For OS, the overall risk of bias was low in 2 trials (CHAARTED and ENZAMET), but the remaining 5 trials raised some concerns (Table 2). Specifically, missing outcome data raised concerns of bias in 3 trials for both outcomes.7,11,33 In these trials, the number of participants with missing data was more than 10% of the observed number of events and distributed unevenly between treatment groups, yet no analysis was done to correct for bias due to missing data and sensitivity analysis was not performed to determine whether the results were little changed under a range of plausible assumptions about the association between missing data and the true value of those data. Selection of the reported results raised concerns of bias in 2 trials12,34 for both outcomes. In these trials, the trial protocol and, when available, the statistical analysis plan, was finalized after the unblinded outcome data may have been made available to the data analysts. Measurement of the outcome raised concerns of bias in 4 trials for rPFS5,33-35; in these trials, the outcome assessors were aware of the intervention received by the study participants; thus, the open-label assessment of the outcome could have been influenced by the knowledge of intervention received.

    Syntheses of Results

    The main results of individual trials are summarized in eTable 3 in the Supplement. Network meta-analyses included all 7 trials for effectiveness outcomes and 6 trials for safety outcomes; the STAMPEDE trial did not report safety outcomes separately for patients with mCSPC.

    Efficacy Outcomes

    Ordered from the most to the least effective, Quiz Ref IDtreatments with significantly improved OS in randomized clinical trials when added to ADT included abiraterone acetate (HR, 0.61; 95% CI, 0.54-0.70), apalutamide (HR, 0.67; 95% CI, 0.51-0.89), docetaxel (HR, 0.79; 95% CI, 0.71-0.89), with nonsignificant findings for enzalutamide (HR, 0.81; 95% CI, 0.53-1.24), and standard nonsteroidal antiandrogen (HR, 1.21; 95% CI, 0.73-1.99); treatments associated with significantly improved rPFS when added to ADT included enzalutamide (HR, 0.39; 95% CI, 0.30-0.50), apalutamide (HR, 0.48; 95% CI, 0.39-0.60), abiraterone acetate (HR, 0.51; 95% CI, 0.45-0.58), docetaxel (HR, 0.67; 95% CI, 0.60-0.74), with nonsignificant findings for standard nonsteroidal antiandrogen (HR, 0.97; 95% CI, 0.71-1.33) (Figure 2). A league table presenting the overall time-invariant HR for all possible pairwise comparisons between treatments is available in eTable 4 in the Supplement. Quiz Ref IDTreatment ranking probabilities suggested that abiraterone acetate had the highest probability of being the best treatment regarding OS (64%, ie, based on the available RCT evidence, there is a 64% probability that abiraterone acetate is the best treatment for patients with mCSPC regarding OS) and enzalutamide had the highest probability of being the best treatment regarding rPFS (88%) (eFigure 2 in the Supplement).

    All included trials reported time-invariant HR of rPFS for patient subgroups based on disease volume, high vs low stratified by the CHAARTED trial criteria.5 High-volume disease was defined as the presence of visceral metastases or 4 or more bone metastases, with at least 1 metastasis outside the vertebral column or pelvis. Subgroup analysis based on disease volume provided consistent results with the primary analysis. Ordered from the most to the least effective, for high-volume prostate cancers, treatments associated with significantly improved rPFS in randomized clinical trials when added to ADT included enzalutamide (HR, 0.43; 95% CI, 0.33-0.56), abiraterone acetate (HR, 0.46; 95% CI, 0.40-0.53), apalutamide (HR, 0.53; 95% CI, 0.41-0.68), docetaxel (HR, 0.57; 95% CI, 0.49-0.66), with nonsignificant findings for standard nonsteroidal antiandrogen (HR, 0.96; 95% CI, 0.67-1.36); for low-volume prostate cancers, treatments associated with significantly improved rPFS when added to ADT included enzalutamide (HR, 0.25; 95% CI, 0.14-0.45), apalutamide (HR, 0.36; 95% CI, 0.22-0.58), abiraterone acetate (HR, 0.48; 95% CI, 0.37-0.63), docetaxel (HR, 0.74; 95% CI, 0.61-0.91), with nonsignificant findings for standard nonsteroidal antiandrogen (HR, 0.83; 95% CI, 0.42-1.64) (eFigure 3 in the Supplement). Subgroup analysis based on disease volume was not feasible for OS, because the ARCHES trial did not report OS results based on disease volume.11 Subgroup analyses based on other baseline characteristics (age, Eastern Cooperative Oncology Group performance, Gleason score, prostate-specific antigen level) were not feasible because not all trials examined these subgroups and, for those that did, the cutoff points were inconsistent across trials.

    Allowing the HR to change over time, parametric survival network meta-analysis provided consistent treatment ranking. Specifically, the first-order fractional polynomial model fit the OS data best when power parameter = −0.5 and fit rPFS data best when power parameter = 0 (equivalent to Weibull distribution). Figure 3 shows the expected OS and rPFS curves up to 48 months post randomization for each treatment, which are based on the estimated time-varying HRs of each treatment relative to placebo/no treatment and subsequently applied to a parametric reference curve with no treatment obtained from the STAMPEDE trial. According to the survival curves, abiraterone acetate appeared to be associated with the highest OS probability and enzalutamide appeared to be associated with the highest rPFS probability. The treatment ranking and the HR of each treatment relative to placebo/no treatment over time are presented in eFigure 4 and eFigure 5 in the Supplement.

    Safety Outcomes

    According to the overall ORs compared with placebo/no treatment and median ranks, treatments ordered from the safest to the least safe regarding their associations with SAE risks were standard nonsteroidal antiandrogen (OR, 0.66; 95% CI, 0.45-0.96), enzalutamide (OR, 0.92; 95% CI, 0.68-1.23), apalutamide (0.97; 95% CI, 0.72-1.32), abiraterone acetate (1.42; 95% CI, 1.10-1.83), and docetaxel (23.72; 95% CI, 13.37-45.15) (Figure 2). A league table presenting the ORs for all possible treatment comparisons is available in eTable 4 in the Supplement. Treatment ranking probabilities suggested that standard nonsteroidal antiandrogen agents have the highest probability of being the safest (94%) regarding SAEs and docetaxel has the highest probability of being the least safe treatment (100%) (eFigure 2 in the Supplement).

    Exploration for Inconsistency

    According to the treatment network (Figure 1), effect estimates in the triangular loop formed by docetaxel, abiraterone acetate, and placebo/no treatment were informed by both direct and indirect evidence. We found no evidence of statistical inconsistency for any outcomes (P = .82 for OS; P = .14 for rPFS).

    Discussion

    This study compared systemic treatments for mCSPC to inform decision-making. We conducted a comprehensive search for eligible RCTs, critically appraised trial quality, synthesized trial data, and ranked treatments by effectiveness and safety shown in randomized clinical trials. We identified 7 eligible trials constructing a scarce network in which most treatments have not been compared in head-to-head trials, which highlights the importance of the study. Quiz Ref IDThree drugs were associated with significantly improved OS when added to ADT. Among them, abiraterone acetate was associated with significantly larger OS benefit than docetaxel, and similar OS benefit to apalutamide. In terms of safety, docetaxel was associated with substantially increased SAEs, abiraterone acetate with slightly increased SAEs, and apalutamide with no increase in SAEs.

    Our study provides several insights. First, we evaluated comparative drug safety, which was not evaluated in previous reviews. Second, we modeled the time-varying HR, which has not been addressed by previous reviews. We closely modeled the observed Kaplan-Meier curves and validated the robustness of results against different assumptions of HRs (time invariant vs time varying). This analysis is necessary given that nonproportional hazards were detected in the STAMPEDE trial.8,42 Third, we confirmed and updated findings of previous reviews. Our results are consistent with those of a previous review that compared abiraterone acetate, docetaxel, zoledronic acid, and celecoxib for mCSPC and suggested that abiraterone acetate may be the most effective treatment followed by docetaxel.43 Our findings are different from those of a review that suggested enzalutamide to be the most effective treatment; relative rankings of other drugs were similar to our estimates.44 However, the most recent ARCHES trial was not included in that review, which showed no OS benefit when comparing enzalutamide with placebo.11

    This study is important for patients, clinicians, and payers given the uncertainty about the optimal treatment for mCSPC, which causes significant morbidity and mortality among older men. The findings of this study may be applicable to patients with mCSPC in different countries because most included trials were multinational, recruiting patients from America, Europe, Asia, and Oceania. Future cost-effectiveness research based on our findings may inform value-based decision-making.

    Limitations

    Our study has limitations, many of which reflect opportunities for improving the design and data sharing of the underlying RCTs that we relied on for our analysis. First, the inconsistency in outcome measures across trials precluded treatment comparison for many outcomes. Although we examined rPFS, its inconsistent definition required us to assume that radiographic progression occurs earlier than symptomatic progression and initiation of new anticancer treatment and death from other causes. This assumption is supported by observations in the LATITUDE trial in which the median rPFS was 14.7 months compared with a median time to pain progression of 16.6 months and a median time to subsequent prostate cancer therapy of 21.2 months in the placebo plus ADT arm.9 Second, subgroup analyses were not feasible for many baseline characteristics because of different cutoff levels across trials. Third, the follow-up durations were different across trials. The relatively short follow-up periods for some treatments may bias against their long-term effectiveness estimation. Specifically, trials for enzalutamide and apalutamide had 14- and 23-month follow-ups, respectively. The immature OS data may bias against these treatments, especially for enzalutamide. Fourth, although hormonal therapies are estimated to be safer than docetaxel in terms of SAEs, the results were based on RCTs with 14- to 84-month duration. In addition, although docetaxel is typically used for 6 cycles (18 weeks), hormonal therapies are administered until disease progression occurs (approximately 2 years).9 Long-term adverse effects of hormonal therapies need continuous monitoring and further assessment. Fifth, a network meta-analysis based on individual patient data, which would be more informative, was attempted but infeasible owing to suboptimal data sharing.45

    Conclusions

    As add-on treatments to ADT, abiraterone acetate and apalutamide may provide the largest OS benefits with relatively low SAE risks among patients with mCSPC in RCTs. Although enzalutamide may improve rPFS to the greatest extent, longer follow-up is needed to examine its OS benefits.

    Back to top
    Article Information

    Accepted for Publication: October 8, 2020.

    Published Online: January 14, 2021. doi:10.1001/jamaoncol.2020.6973

    Corresponding Author: Otis Brawley, MD, Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, 1550 Orleans St, Baltimore, MD 21231 (otis.brawley@jhu.edu).

    Author Contributions: Dr Wang had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Wang, Paller, Hong, Brawley.

    Acquisition, analysis, or interpretation of data: Wang, Hong, De Felice, Alexander.

    Drafting of the manuscript: Wang, De Felice, Brawley.

    Critical revision of the manuscript for important intellectual content: Wang, Paller, Hong, Alexander.

    Statistical analysis: Wang, Hong.

    Obtained funding: Wang.

    Administrative, technical, or material support: Paller, Brawley.

    Supervision: Alexander, Brawley.

    Conflict of Interest Disclosures: Dr Wang reported receiving grants from the Dyar Memorial Fund and the Pharmaceutical Research and Manufacturers of America Foundation during the conduct of the study. Dr Alexander reported Dr Alexander is past chair of US Food and Drug Administration’s Peripheral and Central Nervous System Advisory Committee; has served as a paid adviser to IQVIA; is a cofounding principal and equity holder in Monument Analytics, a health care consultancy whose clients include the life sciences industry as well as plaintiffs in opioid litigation; and is a member of OptumRx's National P&T Committee. Dr Brawley reported receiving grants from the US National Cancer Institute during the conduct of the study and personal fees from Genentech outside the submitted work. No other disclosures were reported.

    Funding/Support: This work was supported by the Dyar Memorial Fund and Pharmaceutical Research and Manufacturers of America Foundation 2020 Predoctoral Fellowship in Health Outcomes Research (Dr Wang).

    Role of the Funder/Sponsor: The funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    References
    1.
    US Cancer Society. Prostate at a glance. Estimated new cases, 2020. Accessed May 28, 2020. https://cancerstatisticscenter.cancer.org/#!/cancer-site/Prostate
    2.
    Cancer Stat Facts SEER. Prostate cancer. National Cancer Institute. Accessed May 28, 2020. https://seer.cancer.gov/statfacts/html/prost.html
    3.
    Alpajaro  SIR, Harris  JAK, Evans  CP.  Non-metastatic castration resistant prostate cancer: a review of current and emerging medical therapies.   Prostate Cancer Prostatic Dis. 2019;22(1):16-23. doi:10.1038/s41391-018-0078-1 PubMedGoogle ScholarCrossref
    4.
    Sartor  O, de Bono  JS.  Metastatic prostate cancer.   N Engl J Med. 2018;378(7):645-657. doi:10.1056/NEJMra1701695 PubMedGoogle ScholarCrossref
    5.
    Sweeney  CJ, Chen  YH, Carducci  M,  et al.  Chemohormonal therapy in metastatic hormone–sensitive prostate cancer.   N Engl J Med. 2015;373(8):737-746. doi:10.1056/NEJMoa1503747 PubMedGoogle ScholarCrossref
    6.
    James  ND, Sydes  MR, Clarke  NW,  et al; STAMPEDE investigators.  Addition of docetaxel, zoledronic acid, or both to first-line long-term hormone therapy in prostate cancer (STAMPEDE): survival results from an adaptive, multiarm, multistage, platform randomised controlled trial.   Lancet. 2016;387(10024):1163-1177. doi:10.1016/S0140-6736(15)01037-5 PubMedGoogle ScholarCrossref
    7.
    Fizazi  K, Tran  N, Fein  L,  et al; LATITUDE Investigators.  Abiraterone plus prednisone in metastatic, castration-sensitive prostate cancer.   N Engl J Med. 2017;377(4):352-360. doi:10.1056/NEJMoa1704174 PubMedGoogle ScholarCrossref
    8.
    James  ND, de Bono  JS, Spears  MR,  et al; STAMPEDE Investigators.  Abiraterone for prostate cancer not previously treated with hormone therapy.   N Engl J Med. 2017;377(4):338-351. doi:10.1056/NEJMoa1702900 PubMedGoogle ScholarCrossref
    9.
    Fizazi  K, Tran  N, Fein  L,  et al.  Abiraterone acetate plus prednisone in patients with newly diagnosed high-risk metastatic castration-sensitive prostate cancer (LATITUDE): final overall survival analysis of a randomised, double-blind, phase 3 trial.   Lancet Oncol. 2019;20(5):686-700. doi:10.1016/S1470-2045(19)30082-8 PubMedGoogle ScholarCrossref
    10.
    Hoyle  AP, Ali  A, James  ND,  et al; STAMPEDE Investigators.  Abiraterone in “high-” and “low-risk” metastatic hormone-sensitive prostate cancer.   Eur Urol. 2019;76():719-728. doi:10.1016/j.eururo.2019.08.006 PubMedGoogle ScholarCrossref
    11.
    Armstrong  AJ, Szmulewitz  RZ, Petrylak  DP,  et al.  ARCHES: a randomized, phase III study of androgen deprivation therapy with enzalutamide or placebo in men with metastatic hormone-sensitive prostate cancer.   J Clin Oncol. 2019;37(32):2974-2986. doi:10.1200/JCO.19.00799 PubMedGoogle ScholarCrossref
    12.
    Chi  KN, Agarwal  N, Bjartell  A,  et al; TITAN Investigators.  Apalutamide for metastatic, castration-sensitive prostate cancer.   N Engl J Med. 2019;381(1):13-24. doi:10.1056/NEJMoa1903307 PubMedGoogle ScholarCrossref
    13.
    European Association of Oncology. Guidelines on prostate cancer. Full-text guidelines. Published 2020. Accessed May 28, 2020 https://uroweb.org/guidelin.e/prostate-cancer/
    14.
    National Comprehensive Cancer Network. Prostate cancer: version 2.2020. Accessed May 28, 2020. https://www.nccn.org/professionals/physician_gls/pdf/prostate.pdf
    15.
    US Department of Veterans Affairs National Acquisition Center (CCST). Published 2019. Accessed October 27, 2019. https://www.vendorportal.ecms.va.gov/nac/Pharma/List
    16.
    Moher  D, Liberati  A, Tetzlaff  J, Altman  DG; PRISMA Group.  Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement.   PLoS Med. 2009;6(7):e1000097. doi:10.1371/journal.pmed.1000097 PubMedGoogle Scholar
    17.
    Hutton  B, Salanti  G, Caldwell  DM,  et al.  The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations.   Ann Intern Med. 2015;162(11):777-784. doi:10.7326/M14-2385 PubMedGoogle ScholarCrossref
    18.
    Sterne  JAC, Savović  J, Page  MJ,  et al.  RoB 2: a revised tool for assessing risk of bias in randomised trials.   BMJ. 2019;366:l4898. doi:10.1136/bmj.l4898 PubMedGoogle ScholarCrossref
    19.
    Sofia Dias  AEA, Welton NJ, Jansen JP, Sutton AJ. Network Meta-Analysis for Decision-Making (Statistics in Practice). Wiley; 2018.
    20.
    Franchini  AJ, Dias  S, Ades  AE, Jansen  JP, Welton  NJ.  Accounting for correlation in network meta-analysis with multi-arm trials.   Res Synth Methods. 2012;3(2):142-160. doi:10.1002/jrsm.1049 PubMedGoogle ScholarCrossref
    21.
    Rohatigi  A. WebPlotDigitizer, version 4.3. Published 2020. Accessed October 23, 2020. https://automeris.io/WebPlotDigitizer
    22.
    Guyot  P, Ades  AE, Ouwens  MJ, Welton  NJ.  Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan-Meier survival curves.   BMC Med Res Methodol. 2012;12:9. doi:10.1186/1471-2288-12-9 PubMedGoogle ScholarCrossref
    23.
    Wei  Y, Royston  P.  Reconstructing time-to-event data from published Kaplan-Meier curves.   Stata J. 2017;17(4):786-802. doi:10.1177/1536867X1801700402 PubMedGoogle ScholarCrossref
    24.
    Jansen  JP.  Network meta-analysis of survival data with fractional polynomials.   BMC Med Res Methodol. 2011;11:61. doi:10.1186/1471-2288-11-61 PubMedGoogle ScholarCrossref
    25.
    Spiegelhalter  DJ, Best  N, Carlin  BP, Linde  A.  Bayesian measures of model complexity and fit (with discussion).   J Royal Stat Soc. 2002;64:583-639. doi:10.1111/1467-9868.00353 Google ScholarCrossref
    26.
    Van Valkenhoef  G, Kuiper  J. gemtc: network meta-analysis using bayesian methods. R package, version 0.8-4. Updated August 10, 2020. Accessed May 28, 2020. https://CRAN.R-project.org/package=gemtc
    27.
    R Foundation for Statistical Computing. R: a language and environment for statistical computing. Published 2019. Accessed May 28, 2020. https://www.R-project.org/
    28.
    Lunn  DJ, Thomas  A, Best  N, Spiegelhalter  D.  WinBUGS: a bayesian modelling framework: concepts, structure, and extensibility.   Stat Comput. 2000;10:325-337. doi:10.1023/A:1008929526011Google ScholarCrossref
    29.
    Dias  S, Welton  NJ, Caldwell  DM, Ades  AE.  Checking consistency in mixed treatment comparison meta-analysis.   Stat Med. 2010;29(7-8):932-944. doi:10.1002/sim.3767 PubMedGoogle ScholarCrossref
    30.
    Van Valkenhoef  G, Dias  S, Ades  AE, Welton  NJ.  Automated generation of node-splitting models for assessment of inconsistency in network meta-analysis.   Res Synth Methods. 2016;7(1):80-93. doi:10.1002/jrsm.1167 PubMedGoogle ScholarCrossref
    31.
    Gelman  A. Inference and monitoring convergence. In:  Markov Chain Monte Carlo in Practice. London: Chapman & Hall; 1996.
    32.
    Brooks  S, Gelman  A.  General methods for monitoring convergence of iterative simulations.   J Comput Graph Stat. 1998;7:434-455.Google Scholar
    33.
    Sydes  MR, Spears  MR, Mason  MD,  et al; STAMPEDE Investigators.  Adding abiraterone or docetaxel to long-term hormone therapy for prostate cancer: directly randomised data from the STAMPEDE multi-arm, multi-stage platform protocol.   Ann Oncol. 2018;29(5):1235-1248. doi:10.1093/annonc/mdy072 PubMedGoogle ScholarCrossref
    34.
    Gravis  G, Fizazi  K, Joly  F,  et al.  Androgen-deprivation therapy alone or with docetaxel in non-castrate metastatic prostate cancer (GETUG-AFU 15): a randomised, open-label, phase 3 trial.   Lancet Oncol. 2013;14(2):149-158. doi:10.1016/S1470-2045(12)70560-0 PubMedGoogle ScholarCrossref
    35.
    Davis  ID, Martin  AJ, Stockler  MR,  et al; ENZAMET Trial Investigators and the Australian and New Zealand Urogenital and Prostate Cancer Trials Group.  Enzalutamide with standard first-line therapy in metastatic prostate cancer.   N Engl J Med. 2019;381(2):121-131. doi:10.1056/NEJMoa1903835 PubMedGoogle ScholarCrossref
    36.
    Kyriakopoulos  CE, Chen  YH, Carducci  MA,  et al.  Chemohormonal therapy in metastatic hormone-sensitive prostate cancer: long-term survival analysis of the randomized phase III E3805 CHAARTED trial.   J Clin Oncol. 2018;36(11):1080-1087. doi:10.1200/JCO.2017.75.3657 PubMedGoogle ScholarCrossref
    37.
    Morgans  AK, Chen  YH, Sweeney  CJ,  et al.  Quality of life during treatment with chemohormonal therapy: analysis of E3805 chemohormonal androgen ablation randomized trial in prostate cancer.   J Clin Oncol. 2018;36(11):1088-1095. doi:10.1200/JCO.2017.75.3335 PubMedGoogle ScholarCrossref
    38.
    Gravis  G, Boher  JM, Joly  F,  et al; GETUG.  Androgen deprivation therapy (ADT) plus docetaxel versus ADT alone in metastatic non castrate prostate cancer: impact of metastatic burden and long-term survival analysis of the randomized phase 3 GETUG-AFU15 Trial.   Eur Urol. 2016;70(2):256-262. doi:10.1016/j.eururo.2015.11.005 PubMedGoogle ScholarCrossref
    39.
    Chi  KN, Protheroe  A, Rodríguez-Antolín  A,  et al.  Patient-reported outcomes following abiraterone acetate plus prednisone added to androgen deprivation therapy in patients with newly diagnosed metastatic castration-naive prostate cancer (LATITUDE): an international, randomised phase 3 trial.   Lancet Oncol. 2018;19(2):194-206. doi:10.1016/S1470-2045(17)30911-7 PubMedGoogle ScholarCrossref
    40.
    Fukasawa  S, Suzuki  H, Kawaguchi  K,  et al.  Efficacy and safety of abiraterone acetate plus prednisone in Japanese patients with newly diagnosed, metastatic hormone-naïve prostate cancer: a subgroup analysis of LATITUDE, a randomized, double-blind, placebo-controlled, Phase 3 study.   Jpn J Clin Oncol. 2018;48(11):1012-1021. doi:10.1093/jjco/hyy129 PubMedGoogle ScholarCrossref
    41.
    Agarwal  N, McQuarrie  K, Bjartell  A,  et al; TITAN investigators.  Health-related quality of life after apalutamide treatment in patients with metastatic castration-sensitive prostate cancer (TITAN): a randomised, placebo-controlled, phase 3 study.   Lancet Oncol. 2019;20(11):1518-1530. doi:10.1016/S1470-2045(19)30620-5 PubMedGoogle ScholarCrossref
    42.
    Clarke  NW, Ali  A, Ingleby  FC,  et al.  Addition of docetaxel to hormonal therapy in low- and high-burden metastatic hormone sensitive prostate cancer: long-term survival results from the STAMPEDE trial.   Ann Oncol. 2019;30(12):1992-2003. doi:10.1093/annonc/mdz396 PubMedGoogle ScholarCrossref
    43.
    Vale  CL, Fisher  DJ, White  IR,  et al.  What is the optimal systemic treatment of men with metastatic, hormone-naive prostate cancer? a STOPCAP systematic review and network meta-analysis.   Ann Oncol. 2018;29(5):1249-1257. doi:10.1093/annonc/mdy071 PubMedGoogle ScholarCrossref
    44.
    Sathianathen  NJ, Koschel  S, Thangasamy  IA,  et al.  Indirect comparisons of efficacy between combination approaches in metastatic hormone-sensitive prostate cancer: a systematic review and network meta-analysis.   Eur Urol. 2020;77(3):365-372. doi:10.1016/j.eururo.2019.09.004 PubMedGoogle ScholarCrossref
    45.
    Wang  L, Hong  H, Paller  C, Brawley  O, Li  T. I s current trial data sharing status conducive for evidence generation for personalized medicine: a failed attempt to conduct an individual pattient trial data network meta-analysis.   Drugs Generics Org Pract. 2020;23(suppl 1):S139-S140. doi:10.1016/j.jval.2020.04.345 Google Scholar
    ×