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10.1002/sim.7372 http://scihub22266oqcxt.onion/10.1002/sim.7372 C5575530!5575530!28620945     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Stat+Med 2017 ; 36 (21): 3283-301 Nephropedia Template TP {{tp|p=28620945|t=2017. Measuring the statistical validity of summary meta?analysis and meta?regression results for use in clinical practice |pdf=|usr=}}{{28620945}} gab.com Text Measuring the statistical validity of summary meta analysis and meta regression results for use in clinical practice JO: Stat Med http://www.kidney.de/pget.php?&tw=1&pmid=28620945 #FOAvip An important question for clinicians appraising a meta?analysis is: are the findings likely to be valid in their own practice?does the reported effect accurately represent the effect that would occur in their own clinical population? To this end we advance the concept of statistical validity?where the parameter being estimated equals the corresponding parameter for a new independent study. Using a simple (?leave?one?out?) cross?validation technique, we demonstrate how we may test meta?analysis estimates for statistical validity using a new validation statistic, Vn, and derive its distribution.We compare this with the usual approach of investigating heterogeneity in meta?analyses and demonstrate the link between statistical validity and homogeneity. Using a simulation study, the properties of Vn and the Q statistic are compared for univariate random effects meta?analysis and a tailored meta?regression model, where information from the setting (included as model covariates) is used to calibrate the summary estimate to the setting of application. Their properties are found to be similar when there are 50 studies or more, but for fewer studies Vn has greater power but a higher type 1 error rate than Q. The power and type 1 error rate of Vn are also shown to depend on the within?study variance, between?study variance, study sample size, and the number of studies in the meta?analysis. Finally, we apply Vn to two published meta?analyses and conclude that it usefully augments standard methods when deciding upon the likely validity of summary meta?analysis estimates in clinical practice. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. Twit Text FOAVip Measuring the statistical validity of summary meta analysis and meta regression results for use in clinical practice ????Stat Med http://www.kidney.de/pget.php?&tw=1&pmid=28620945 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28620945 #FOAvip English Wikipedia <ref>{{Cite pmid|28620945}}</ref> Measuring the statistical validity of summary meta?analysis and meta?regression results for use in clinical practice #MMPMID28620945 Willis BH; Riley RD Stat Med 2017[Sep]; 36 (21): 3283-301 PMID28620945show ga An important question for clinicians appraising a meta?analysis is: are the findings likely to be valid in their own practice?does the reported effect accurately represent the effect that would occur in their own clinical population? To this end we advance the concept of statistical validity?where the parameter being estimated equals the corresponding parameter for a new independent study. Using a simple (?leave?one?out?) cross?validation technique, we demonstrate how we may test meta?analysis estimates for statistical validity using a new validation statistic, Vn, and derive its distribution.We compare this with the usual approach of investigating heterogeneity in meta?analyses and demonstrate the link between statistical validity and homogeneity. Using a simulation study, the properties of Vn and the Q statistic are compared for univariate random effects meta?analysis and a tailored meta?regression model, where information from the setting (included as model covariates) is used to calibrate the summary estimate to the setting of application. Their properties are found to be similar when there are 50 studies or more, but for fewer studies Vn has greater power but a higher type 1 error rate than Q. The power and type 1 error rate of Vn are also shown to depend on the within?study variance, between?study variance, study sample size, and the number of studies in the meta?analysis. Finally, we apply Vn to two published meta?analyses and conclude that it usefully augments standard methods when deciding upon the likely validity of summary meta?analysis estimates in clinical practice. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. äMeasuring the statistical validity of summary meta?analysis and meta?r(epmc).pdf DeepDyve Pubget Overpricing