We propose novel methods for the analysis of survival data arising from studies of organ failure patients. The organ failure setting yields data structures to which existing univariate and multivariate survival analysis methods cannot be applied to address many questions of great interest to clinicians, health policy makers and patients. The issues addressed by the proposed methods are general, in that they will have wide applicability outside the organ replacement setting. There is currently great interest in quantifying the survival benefit of organ transplantation and, correspondingly, this proposal is targeted strongly in that direction.
In Aim 1 we propose semiparametric methods to quantify the effect of an experimental time-dependent therapy relative to conventional therapy on mean time until failure. Methods developed in Aim 1 will be used to quantify the benefit of expanded criteria donor kidney transplantation relative to conventional therapy.
In Aim 2, we develop semiparametric methods to predict patient-specific differences between post-treatment and treatment-free mean lifetime. These predictions are intended to be the basis of a score to replace the current system for prioritizing patients wait listed for liver transplantation. We also propose methods for estimating the marginal effect of a time-dependent treatment on mean lifetime, obtained by averaging over the patient- specific estimates used in the proposed score.
In Aim 3, we develop and evaluate semiparametric methods to make covariate-adjusted comparisons of gap time survival distributions. The methods proposed in Aim3 will be used to compare mean lifetime of first and second kidney transplants, in order to assess the value of repeat kidney transplantation.

Public Health Relevance

We propose novel methods for the analysis of survival data arising from studies of organ failure patients. There is currently great interest in quantifying the survival benefit of organ transplantation and, correspondingly, this proposal is targeted strongly in that direction.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Research Project (R01)
Project #
5R01DK070869-07
Application #
8324698
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Narva, Andrew
Project Start
2005-04-01
Project End
2013-06-30
Budget Start
2012-07-01
Budget End
2013-06-30
Support Year
7
Fiscal Year
2012
Total Cost
$248,708
Indirect Cost
$84,368
Name
University of Michigan Ann Arbor
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
073133571
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109
Liu, Dandan; Kalbfleisch, John D; Schaubel, Douglas E (2014) Methods for Estimating Center Effects on Recurrent Events. Stat Biosci 6:19-37
Mathur, Amit K; Schaubel, Douglas E; Zhang, Hui et al. (2014) Disparities in liver transplantation: the association between donor quality and recipient race/ethnicity and sex. Transplantation 97:862-9
Pan, Qing; Schaubel, Douglas E (2014) Proportional hazards regression in the presence of missing study eligibility information. Lifetime Data Anal 20:424-43
Sun, Rena Jie; Kalbfleisch, John D; Schaubel, Douglas E (2014) A weighted cumulative sum (WCUSUM) to monitor medical outcomes with dependent censoring. Stat Med 33:3114-29
He, Kevin; Schaubel, Douglas E (2014) Methods for comparing center-specific survival outcomes using direct standardization. Stat Med 33:2048-61
He, Kevin; Schaubel, Douglas E (2014) Semiparametric methods for center effect measures based on the ratio of survival functions. Lifetime Data Anal 20:619-44
Li, Yun; Schaubel, Douglas E; He, Kevin (2014) Matching methods for obtaining survival functions to estimate the effect of a time-dependent treatment. Stat Biosci 6:105-126
Gong, Qi; Schaubel, Douglas E (2013) Partly conditional estimation of the effect of a time-dependent factor in the presence of dependent censoring. Biometrics 69:338-47
Kalbfleisch, John D; Schaubel, Douglas E; Ye, Yining et al. (2013) An estimating function approach to the analysis of recurrent and terminal events. Biometrics 69:366-74
Zhang, Hui; Schaubel, Douglas E; Kalbfleisch, John D et al. (2012) Dialysis outcomes and analysis of practice patterns suggests the dialysis schedule affects day-of-week mortality. Kidney Int 81:1108-15

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