End-stage renal disease (ESRD) and other forms of organ failure have become a major public health concern in the United States due to their mortality, morbidity and associated health care costs. Due to increased incidence, the demand for donor organs has eclipsed the supply and the appropriate allocation of the scarce supply of donor organs is a hotly contended issue. This project addresses several fundamental questions on organ failure which cannot be addressed using existing failure time methods.
In Aim 1, we propose survival analysis methods for estimating the transplant survival benefit (i.e., contrast between wait-list and post-transplant mortality) when such benefit interacts with another time-dependent covariate. The methods view the data structure from a novel perspective and the resulting parameter estimates have a much improved interpretation relative to the existing models.
In Aim 2, we develop random effects additive rates models for recurrent event data. For a multi-center study, the proposed methods permit the statistical inference to apply to the population of centers from which those in the study were selected. The model parameters have a much improved interpretation relative to existing multiplicative rates models. Weighted regression models for are developed in Aim 3. The proposed models can be applied to unrepresentative samples and, unlike existing weighted Cox models, will be valid when the sample inclusion probabilities are not known and must be estimated from the data. Methods for center-specific outcomes are proposed in Aim 4. We develop and evaluate methods to compare center-specific (i) mortality and (ii) transplant benefit. The mortality methods propose novel and easily interpretable summary statistics to describe and test the center effects. The methods for transplant benefit are valid in the presence of interactions between transplant and other covariates.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Research Project (R01)
Project #
1R01DK070869-01
Application #
6905108
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Eggers, Paul Wayne
Project Start
2005-04-01
Project End
2009-03-31
Budget Start
2005-04-01
Budget End
2006-03-31
Support Year
1
Fiscal Year
2005
Total Cost
$163,636
Indirect Cost
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
Kim, Sehee; Schaubel, Douglas E; McCullough, Keith P (2018) A C-index for recurrent event data: Application to hospitalizations among dialysis patients. Biometrics 74:734-743
Smith, Abigail R; Zhu, Danting; Goodrich, Nathan P et al. (2018) Estimating the effect of a rare time-dependent treatment on the recurrent event rate. Stat Med 37:1986-1996
Welling, Theodore H; Eddinger, Kevin; Carrier, Kristen et al. (2018) Multicenter Study of Staging and Therapeutic Predictors of Hepatocellular Carcinoma Recurrence Following Transplantation. Liver Transpl 24:1233-1242
Wang, Xin; Schaubel, Douglas E (2018) Modeling restricted mean survival time under general censoring mechanisms. Lifetime Data Anal 24:176-199
Zhan, Tianyu; Schaubel, Douglas E (2018) Semiparametric temporal process regression of survival-out-of-hospital. Lifetime Data Anal :
Schaubel, Douglas E; Nan, Bin (2018) Special issue dedicated to Jack Kalbfleisch. Lifetime Data Anal 24:1-2
Zhong, Yingchao; Schaubel, Douglas E; Kalbfleisch, John D et al. (2018) Reevaluation of the Kidney Donor Risk Index (KDRI). Transplantation :
Gong, Qi; Schaubel, Douglas E (2018) Tobit regression for modeling mean survival time using data subject to multiple sources of censoring. Pharm Stat 17:117-125
Dharmarajan, Sai H; Schaubel, Douglas E; Saran, Rajiv (2018) Evaluating center performance in the competing risks setting: Application to outcomes of wait-listed end-stage renal disease patients. Biometrics 74:289-299
Schaubel, Douglas E (2017) Statistical Methods in Organ Failure and Transplantation. Stat Biosci 9:317-319

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