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10.1371/journal.pone.0154870 http://scihub22266oqcxt.onion/10.1371/journal.pone.0154870 C4858202!4858202 !27149107     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=27149107 &cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215       PLoS+One 2016 ; 11 (5 ): e0154870 Nephropedia Template TP {{tp|p=27149107 |t=2016. Review of the Reporting of Survival Analyses within Randomised Controlled Trials and the Implications for Meta-Analysis |pdf=|usr=}}{{27149107 }} gab.com Text Review of the Reporting of Survival Analyses within Randomised Controlled Trials and the Implications for Meta-Analysis JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=27149107 #FOAvip BACKGROUND: Meta-analysis is a growing approach to evidence synthesis and network meta-analysis in particular represents an important and developing method within Health Technology Assessment (HTA). Meta-analysis of survival data is usually performed using the individual summary statistic-the hazard ratio (HR) from each randomised controlled trial (RCT). OBJECTIVES: The objectives of this study are to: (i) review the methods and reporting of survival analyses in oncology RCTs; and (ii) assess the suitability and relevance of survival data reported in RCTs for inclusion into meta-analysis. METHODS: Five oncology journals were searched to identify Phase III RCTs published between April and July 2015. Eligible studies included those that analysed a survival outcome. RESULTS: Thirty-two RCTs reporting survival outcomes in cancer populations were identified. None of the publications reported details relating to a strategy for statistical model building, the goodness of fit of the final model, or final model validation for the analysis of survival outcomes. The majority of studies (88%) reported the use of Cox proportional hazards (PH) regression to analyse survival endpoints. However, most publications failed to report the validation of the statistical models in terms of the PH assumption. CONCLUSIONS: This review highlights deficiencies in terms of reporting the methods and validity of survival analyses within oncology RCTs. We support previous recommendations to encourage authors to improve the reporting of survival analyses in journal publications. We also recommend that the final choice of a statistical model for survival should be informed by goodness of model fit to a given dataset, and that model assumptions are validated. The failure of trial investigators and statisticians to investigate the PH for RCT survival data is likely to result in clinical decisions based on inappropriate methods. The development of alternative approaches for the meta-analysis of survival outcomes when the PH assumption is implausible is required if valid clinical decisions are to be made. Twit Text FOAVip Review of the Reporting of Survival Analyses within Randomised Controlled Trials and the Implications for Meta-Analysis ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=27149107 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27149107 #FOAvip English Wikipedia <ref>{{Cite pmid|27149107 }}</ref> Review of the Reporting of Survival Analyses within Randomised Controlled Trials and the Implications for Meta-Analysis #MMPMID27149107 Batson S ; Greenall G ; Hudson P PLoS One 2016[]; 11 (5 ): e0154870 PMID27149107 show ga BACKGROUND: Meta-analysis is a growing approach to evidence synthesis and network meta-analysis in particular represents an important and developing method within Health Technology Assessment (HTA). Meta-analysis of survival data is usually performed using the individual summary statistic-the hazard ratio (HR) from each randomised controlled trial (RCT). OBJECTIVES: The objectives of this study are to: (i) review the methods and reporting of survival analyses in oncology RCTs; and (ii) assess the suitability and relevance of survival data reported in RCTs for inclusion into meta-analysis. METHODS: Five oncology journals were searched to identify Phase III RCTs published between April and July 2015. Eligible studies included those that analysed a survival outcome. RESULTS: Thirty-two RCTs reporting survival outcomes in cancer populations were identified. None of the publications reported details relating to a strategy for statistical model building, the goodness of fit of the final model, or final model validation for the analysis of survival outcomes. The majority of studies (88%) reported the use of Cox proportional hazards (PH) regression to analyse survival endpoints. However, most publications failed to report the validation of the statistical models in terms of the PH assumption. CONCLUSIONS: This review highlights deficiencies in terms of reporting the methods and validity of survival analyses within oncology RCTs. We support previous recommendations to encourage authors to improve the reporting of survival analyses in journal publications. We also recommend that the final choice of a statistical model for survival should be informed by goodness of model fit to a given dataset, and that model assumptions are validated. The failure of trial investigators and statisticians to investigate the PH for RCT survival data is likely to result in clinical decisions based on inappropriate methods. The development of alternative approaches for the meta-analysis of survival outcomes when the PH assumption is implausible is required if valid clinical decisions are to be made. |*Meta-Analysis as Topic [MESH] |*Survival Analysis [MESH] |Humans [MESH] |Models, Statistical [MESH]Review of the Reporting of Survival Analyses within Randomised Control(epmc).pdf DeepDyve Pubget Overpricing