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10.1016/j.csda.2015.01.001 http://scihub22266oqcxt.onion/10.1016/j.csda.2015.01.001 C4313789!4313789!25663729     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       Comput+Stat+Data+Anal 2015 ; 86 (ä): 42-51 Nephropedia Template TP {{tp|p=25663729|t=2015. Multiple comparisons for survival data with propensity score adjustment |pdf=|usr=}}{{25663729}} gab.com Text Multiple comparisons for survival data with propensity score adjustment JO: Comput Stat Data Anal http://www.kidney.de/pget.php?&tw=1&pmid=25663729 #FOAvip This article considers the practical problem in clinical and observational studies where multiple treatment or prognostic groups are compared and the observed survival data are subject to right censoring. Two possible formulations of multiple comparisons are suggested. Multiple Comparisons with a Control (MCC) compare every other group to a control group with respect to survival outcomes, for determining which groups are associated with lower risk than the control. Multiple Comparisons with the Best (MCB) compare each group to the truly minimum risk group and identify the groups that are either with the minimum risk or the practically minimum risk. To make a causal statement, potential confounding effects need to be adjusted in the comparisons. Propensity score based adjustment is popular in causal inference and can effectively reduce the confounding bias. Based on a propensity-score-stratified Cox proportional hazards model, the approaches of MCC test and MCB simultaneous confidence intervals for general linear models with normal error outcome are extended to survival outcome. This paper specifies the assumptions for causal inference on survival outcomes within a potential outcome framework, develops testing procedures for multiple comparisons and provides simultaneous confidence intervals. The proposed methods are applied to two real data sets from cancer studies for illustration, and a simulation study is also presented. Twit Text FOAVip Multiple comparisons for survival data with propensity score adjustment ????Comput Stat Data Anal http://www.kidney.de/pget.php?&tw=1&pmid=25663729 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25663729 #FOAvip English Wikipedia <ref>{{Cite pmid|25663729}}</ref> Multiple comparisons for survival data with propensity score adjustment #MMPMID25663729 Zhu H; Lu B Comput Stat Data Anal 2015[Jun]; 86 (ä): 42-51 PMID25663729show ga This article considers the practical problem in clinical and observational studies where multiple treatment or prognostic groups are compared and the observed survival data are subject to right censoring. Two possible formulations of multiple comparisons are suggested. Multiple Comparisons with a Control (MCC) compare every other group to a control group with respect to survival outcomes, for determining which groups are associated with lower risk than the control. Multiple Comparisons with the Best (MCB) compare each group to the truly minimum risk group and identify the groups that are either with the minimum risk or the practically minimum risk. To make a causal statement, potential confounding effects need to be adjusted in the comparisons. Propensity score based adjustment is popular in causal inference and can effectively reduce the confounding bias. Based on a propensity-score-stratified Cox proportional hazards model, the approaches of MCC test and MCB simultaneous confidence intervals for general linear models with normal error outcome are extended to survival outcome. This paper specifies the assumptions for causal inference on survival outcomes within a potential outcome framework, develops testing procedures for multiple comparisons and provides simultaneous confidence intervals. The proposed methods are applied to two real data sets from cancer studies for illustration, and a simulation study is also presented. äMultiple comparisons for survival data with propensity score adjustmen(epmc).pdf DeepDyve Pubget Overpricing