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10.2215/CJN.10301016 http://scihub22266oqcxt.onion/10.2215/CJN.10301016 C5498354!5498354!28242844     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Clin+J+Am+Soc+Nephrol 2017 ; 12 (7): 1181-9 Nephropedia Template TP {{tp|p=28242844|t=2017. Statistical Methods for Cohort Studies of CKD: Survival Analysis in the Setting of Competing Risks |pdf=|usr=}}{{28242844}} gab.com Text Statistical Methods for Cohort Studies of CKD: Survival Analysis in the Setting of Competing Risks JO: Clin J Am Soc Nephrol http://www.kidney.de/pget.php?&tw=1&pmid=28242844 #FOAvip Survival analysis is commonly used to evaluate factors associated with time to an event of interest (e.g., ESRD, cardiovascular disease, and mortality) among CKD populations. Time to the event of interest is typically observed only for some participants. Other participants have their event time censored because of the end of the study, death, withdrawal from the study, or some other competing event. Classic survival analysis methods, such as Cox proportional hazards regression, rely on the assumption that any censoring is independent of the event of interest. However, in most clinical settings, such as in CKD populations, this assumption is unlikely to be true. For example, participants whose follow-up time is censored because of health-related death likely would have had a shorter time to ESRD, had they not died. These types of competing events that cause dependent censoring are referred to as competing risks. Here, we first describe common circumstances in clinical renal research where competing risks operate and then review statistical approaches for dealing with competing risks. We compare two of the most popular analytical methods used in settings of competing risks: cause-specific hazards models and the Fine and Gray approach (subdistribution hazards models). We also discuss practical recommendations for analysis and interpretation of survival data that incorporate competing risks. To demonstrate each of the analytical tools, we use a study of fibroblast growth factor 23 and risks of mortality and ESRD in participants with CKD from the Chronic Renal Insufficiency Cohort Study. Twit Text FOAVip Statistical Methods for Cohort Studies of CKD: Survival Analysis in the Setting of Competing Risks ????Clin J Am Soc Nephrol http://www.kidney.de/pget.php?&tw=1&pmid=28242844 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28242844 #FOAvip English Wikipedia <ref>{{Cite pmid|28242844}}</ref> Statistical Methods for Cohort Studies of CKD: Survival Analysis in the Setting of Competing Risks #MMPMID28242844 Hsu JY; Roy JA; Xie D; Yang W; Shou H; Anderson AH; Landis JR; Jepson C; Wolf M; Isakova T; Rahman M; Feldman HI Clin J Am Soc Nephrol 2017[Jul]; 12 (7): 1181-9 PMID28242844show ga Survival analysis is commonly used to evaluate factors associated with time to an event of interest (e.g., ESRD, cardiovascular disease, and mortality) among CKD populations. Time to the event of interest is typically observed only for some participants. Other participants have their event time censored because of the end of the study, death, withdrawal from the study, or some other competing event. Classic survival analysis methods, such as Cox proportional hazards regression, rely on the assumption that any censoring is independent of the event of interest. However, in most clinical settings, such as in CKD populations, this assumption is unlikely to be true. For example, participants whose follow-up time is censored because of health-related death likely would have had a shorter time to ESRD, had they not died. These types of competing events that cause dependent censoring are referred to as competing risks. Here, we first describe common circumstances in clinical renal research where competing risks operate and then review statistical approaches for dealing with competing risks. We compare two of the most popular analytical methods used in settings of competing risks: cause-specific hazards models and the Fine and Gray approach (subdistribution hazards models). We also discuss practical recommendations for analysis and interpretation of survival data that incorporate competing risks. To demonstrate each of the analytical tools, we use a study of fibroblast growth factor 23 and risks of mortality and ESRD in participants with CKD from the Chronic Renal Insufficiency Cohort Study. äStatistical Methods for Cohort Studies of CKD: Survival Analysis in th(epmc).pdf DeepDyve Pubget Overpricing