Importance: There were over 700,000 end-stage renal disease (ESRD) patients receiving renal replacement therapy dialysis in the United States at the end of 2015. ESRD has a substantial impact on mortality, morbidity, health care cost and quality of life. The preferred therapy, kidney transplantation, is in relatively short supply; e.g., less than 19,000 kidney transplants occurred in the U.S. in 2015, with about 83,000 patients remaining on the wait list at year end. Problem: Given that mortality and hospitalization rates are quite high among ESRD patients, flexible, broadly applicable and easily implementable methods of analysis are required for modeling hospitalization, death, and the two processes simultaneously. Existing methods either fail to target quantities of interest in Aims 1-3 (below), or do so using strong assumptions which limit their applicability. Overall Objective: The overarching goal of this project is to develop survival analysis methodology to support analyses that will produce a deeper understanding of morbidity and mortality patterns among ESRD patients. Such increased understanding should lead to improvements in renal replacement therapy and, in turn, improved survival and quality of life among ESRD patients. Target Audience: With respect to methodology, the target audience includes biostatisticians, particularly practitioners studying ESRD and other chronic illnesses. Results based on the proposed analyses would be of interest to nephrologists, transplant surgeons and ESRD patients. Products: Novel and innovative methods for the analysis of survival and recurrent event data.
Specific Aim 1 : Methods for alternating recurrent event data Methods will be developed for analyzing alternating gap time data (e.g., time to readmission; length of hospital stay). They will be applied to the Dialysis Outcomes and Practice Patterns Study (DOPPS).
Specific Aim 2 : Simultaneously modeling state prevalence and survival We will develop methods for jointly modeling state prevalence probability and survival. The methods will be applied to SRTR data to analyze the probability of being active on the kidney transplant wait list.
Specific Aim 3 : Instrumental variable (IV) methods based on restricted mean survival time (RMST) Methods to estimate causal treatment effects on survival through IV analysis will be developed. The methods will be applied to compare survival by dialytic modality using USRDS data. For each Aim, the methods will be easily implementable since user-friendly software (SAS, R) will be developed and made available online.

Public Health Relevance

The proposed project is relevant to public health in the United States since end-stage renal disease (ESRD) is associated with high mortality and morbidity, with the preferred treatment modality (kidney transplantation) being in relatively short supply. Moreover, ESRD prevalence has been increasing for some time in the U.S., a trend which would be expected to continue given the increased incidence of diabetes and the aging of the U.S. population. Our proposal is relevant to the National Institutes of Diabetes and Digestive and Kidney Diseases (NIDDK) since analyses based on the project's products (novel and innovative biostatistical methods) will lead to considerable advances in the understanding of ESRD morbidity and mortality patterns and hence, in turn, improve the health and quality of life of ESRD patients.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Research Project (R01)
Project #
5R01DK070869-14
Application #
10115699
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Abbott, Kevin C
Project Start
2005-04-01
Project End
2023-02-28
Budget Start
2021-03-01
Budget End
2022-02-28
Support Year
14
Fiscal Year
2021
Total Cost
Indirect Cost
Name
University of Pennsylvania
Department
Biostatistics & Other Math Sci
Type
Schools of Medicine
DUNS #
042250712
City
Philadelphia
State
PA
Country
United States
Zip Code
19104
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
Sharma, Pratima; Goodrich, Nathan P; Schaubel, Douglas E et al. (2017) National assessment of early hospitalization after liver transplantation: Risk factors and association with patient survival. Liver Transpl 23:1143-1152

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