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10.1111/biom.12427 http://scihub22266oqcxt.onion/10.1111/biom.12427 C4846598!4846598!26501480     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biometrics 2016 ; 72 (2): 525-34 Nephropedia Template TP {{tp|p=26501480|t=2016. Semiparametric Methods to Contrast Gap Time Survival Functions: Application to Repeat Kidney Transplantation |pdf=|usr=}}{{26501480}} gab.com Text Semiparametric Methods to Contrast Gap Time Survival Functions: Application to Repeat Kidney Transplantation JO: Biometrics http://www.kidney.de/pget.php?&tw=1&pmid=26501480 #FOAvip Times between successive events (i.e., gap times) are of great importance in survival analysis. Although many methods exist for estimating covariate effects on gap times, very few existing methods allow for comparisons between gap times themselves. Motivated by the comparison of primary and repeat transplantation, our interest is specifically in contrasting the gap time survival functions and their integration (restricted mean gap time). Two major challenges in gap time analysis are non-identifiability of the marginal distributions and the existence of dependent censoring (for all but the first gap time). We use Cox regression to estimate the (conditional) survival distributions of each gap time (given the previous gap times). Combining fitted survival functions based on those models, along with multiple imputation applied to censored gap times, we then contrast the first and second gap times with respect to average survival and restricted mean lifetime. Large-sample properties are derived, with simulation studies carried out to evaluate finite-sample performance. We apply the proposed methods to kidney transplant data obtained from a national organ transplant registry. Mean 10-year graft survival of the primary transplant is significantly greater than that of the repeat transplant, by 3.9 months (p = 0.023), a result that may lack clinical importance. Twit Text FOAVip Semiparametric Methods to Contrast Gap Time Survival Functions: Application to Repeat Kidney Transplantation ????Biometrics http://www.kidney.de/pget.php?&tw=1&pmid=26501480 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26501480 #FOAvip English Wikipedia <ref>{{Cite pmid|26501480}}</ref> Semiparametric Methods to Contrast Gap Time Survival Functions: Application to Repeat Kidney Transplantation #MMPMID26501480 Shu X; Schaubel DE Biometrics 2016[Jun]; 72 (2): 525-34 PMID26501480show ga Times between successive events (i.e., gap times) are of great importance in survival analysis. Although many methods exist for estimating covariate effects on gap times, very few existing methods allow for comparisons between gap times themselves. Motivated by the comparison of primary and repeat transplantation, our interest is specifically in contrasting the gap time survival functions and their integration (restricted mean gap time). Two major challenges in gap time analysis are non-identifiability of the marginal distributions and the existence of dependent censoring (for all but the first gap time). We use Cox regression to estimate the (conditional) survival distributions of each gap time (given the previous gap times). Combining fitted survival functions based on those models, along with multiple imputation applied to censored gap times, we then contrast the first and second gap times with respect to average survival and restricted mean lifetime. Large-sample properties are derived, with simulation studies carried out to evaluate finite-sample performance. We apply the proposed methods to kidney transplant data obtained from a national organ transplant registry. Mean 10-year graft survival of the primary transplant is significantly greater than that of the repeat transplant, by 3.9 months (p = 0.023), a result that may lack clinical importance. äSemiparametric Methods to Contrast Gap Time Survival Functions: Applic(epmc).pdf DeepDyve Pubget Overpricing