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      What is the optimal systemic treatment of men with metastatic, hormone-naive prostate cancer? A STOPCAP systematic review and network meta-analysis

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          Abstract Background Our prior Systemic Treatment Options for Cancer of the Prostate systematic reviews showed improved survival for men with metastatic hormone-naive prostate cancer when abiraterone acetate plus prednisolone/prednisone (AAP) or docetaxel (Doc), but not zoledronic acid (ZA), were added to androgen-deprivation therapy (ADT). Trial evidence also suggests a benefit of combining celecoxib (Cel) with ZA and ADT. To establish the optimal treatments, a network meta-analysis (NMA) was carried out based on aggregate data (AD) from all available studies. Methods Overall survival (OS) and failure-free survival data from completed Systemic Treatment Options for Cancer of the Prostate reviews of Doc, ZA and AAP and from recent trials of ZA and Cel contributed to this comprehensive AD-NMA. The primary outcome was OS. Correlations between treatment comparisons within one multi-arm, multi-stage trial were estimated from control-arm event counts. Network consistency and a common heterogeneity variance were assumed. Results We identified 10 completed trials which had closed to recruitment, and one trial in which recruitment was ongoing, as eligible for inclusion. Results are based on six trials including 6204 men (97% of men randomised in all completed trials). Network estimates of effects on OS were consistent with reported comparisons with ADT alone for AAP [hazard ration (HR) = 0.61, 95% confidence interval (CI) 0.53–0.71], Doc (HR = 0.77, 95% CI 0.68–0.87), ZA + Cel (HR = 0.78, 95% CI 0.62–0.97), ZA + Doc (HR = 0.79, 95% CI 0.66–0.94), Cel (HR = 0.94 95% CI 0.75–1.17) and ZA (HR = 0.90 95% CI 0.79–1.03). The effect of ZA + Cel is consistent with the additive effects of the individual treatments. Results suggest that AAP has the highest probability of being the most effective treatment both for OS (94% probability) and failure-free survival (100% probability). Doc was the second-best treatment of OS (35% probability). Conclusions Uniquely, we have included all available results and appropriately accounted for inclusion of multi-arm, multi-stage trials in this AD-NMA. Our results support the use of AAP or Doc with ADT in men with metastatic hormone-naive prostate cancer. AAP appears to be the most effective treatment, but it is not clear to what extent and whether this is due to a true increased benefit with AAP or the variable features of the individual trials. To fully account for patient variability across trials, changes in prognosis or treatment effects over time and the potential impact of treatment on progression, a network meta-analysis based on individual participant data is in development.

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          The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials

          Flaws in the design, conduct, analysis, and reporting of randomised trials can cause the effect of an intervention to be underestimated or overestimated. The Cochrane Collaboration’s tool for assessing risk of bias aims to make the process clearer and more accurate
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            Practical methods for incorporating summary time-to-event data into meta-analysis

            Background In systematic reviews and meta-analyses, time-to-event outcomes are most appropriately analysed using hazard ratios (HRs). In the absence of individual patient data (IPD), methods are available to obtain HRs and/or associated statistics by carefully manipulating published or other summary data. Awareness and adoption of these methods is somewhat limited, perhaps because they are published in the statistical literature using statistical notation. Methods This paper aims to 'translate' the methods for estimating a HR and associated statistics from published time-to-event-analyses into less statistical and more practical guidance and provide a corresponding, easy-to-use calculations spreadsheet, to facilitate the computational aspects. Results A wider audience should be able to understand published time-to-event data in individual trial reports and use it more appropriately in meta-analysis. When faced with particular circumstances, readers can refer to the relevant sections of the paper. The spreadsheet can be used to assist them in carrying out the calculations. Conclusion The methods cannot circumvent the potential biases associated with relying on published data for systematic reviews and meta-analysis. However, this practical guide should improve the quality of the analysis and subsequent interpretation of systematic reviews and meta-analyses that include time-to-event outcomes.
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              Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis: an overview and tutorial.

              To present some simple graphical and quantitative ways to assist interpretation and improve presentation of results from multiple-treatment meta-analysis (MTM). We reanalyze a published network of trials comparing various antiplatelet interventions regarding the incidence of serious vascular events using Bayesian approaches for random effects MTM, and we explore the advantages and drawbacks of various traditional and new forms of quantitative displays and graphical presentations of results. We present the results under various forms, conventionally based on the mean of the distribution of the effect sizes; based on predictions; based on ranking probabilities; and finally, based on probabilities to be within an acceptable range from a reference. We show how to obtain and present results on ranking of all treatments and how to appraise the overall ranks. Bayesian methodology offers a multitude of ways to present results from MTM models, as it enables a natural and easy estimation of all measures based on probabilities, ranks, or predictions. Copyright © 2011 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                Journal
                Annals of Oncology
                Annals of Oncology
                Oxford University Press (OUP)
                09237534
                May 2018
                May 2018
                : 29
                : 5
                : 1249-1257
                Article
                10.1093/annonc/mdy071
                d365dc4c-a04a-43a2-a040-8d61bfba7bbc
                © 2018

                https://www.elsevier.com/tdm/userlicense/1.0/

                http://creativecommons.org/licenses/by/4.0/

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