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Slope estimation for bivariate longitudinal outcomes adjusting for informative right censoring by using a discrete survival model: application to the renal transplant cohort
Author(s) -
Jaffa Miran A.,
Woolson Robert F.,
Lipsitz Stuart R.
Publication year - 2011
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/j.1467-985x.2010.00671.x
Subject(s) - bivariate analysis , censoring (clinical trials) , medicine , transplantation , renal function , statistics , blood urea nitrogen , cohort , population , longitudinal study , survival function , kidney transplantation , survival analysis , mathematics , environmental health
Summary. Patients undergoing renal transplantation are prone to graft failure which causes loss of follow‐up measures on their levels of blood urea nitrogen and serum creatinine. These two outcomes are measured repeatedly over time to assess renal function following transplantation. Loss of follow‐up on these bivariate measures results in informative right censoring, which is a common problem in longitudinal data that should be adjusted for so that valid estimates are obtained. In this study, we propose a bivariate model that jointly models these two longitudinal correlated outcomes and generates population and individual slopes adjusting for informative right censoring by using a discrete survival approach. The approach proposed is applied to a clinical data set of patients who had undergone renal transplantation. A simulation study validates the effectiveness of the approach.