|Grant Number:||5R01CA094893-07 Interpret this number|
|Primary Investigator:||Fine, Jason|
|Organization:||Univ Of North Carolina Chapel Hill|
|Project Title:||Frailty Models and Survival Analysis in Cancer Research|
DESCRIPTION (provided by applicant): The proposal's objective is to develop practicable survival analysis tools for clinical, epidemiologic, and basic science studies in oncology and other chronic diseases in the health sciences. In Aim 1, a main motivation is AIDS clinical trials surrogate primary endpoints, like viral failure may be censored by informative dropout and naive Cox model analyses may be misleading. I investigate time-dependent regression models for the surrogate endpoints and time-dependent dependence models, including conservative sensitivity analyses, which account for dependent censoring. The analyses may detect subtle temporal changes in treatment efficacy and the censoring mechanism and are useful complements to naive regression analyses in AIDS other chronic disease studies where dropout is problematic. In Aim 2, I investigate associations in multivariate competing risks data, an important topic in population based genetic epidemiologic survival studies, like the Cache County Study of Aging, where familial onset associations for chronic diseases, like dementia, are of interest. Standard censored data association analyses do not address that the onset ages may be dependently censored by death. I will extend classic univariate analyses of cause-specific hazard and cumulative incidence functions to obtain novel time-varying association measures and tests. The methods will provide fundamental knowledge about familial disease assocations which may not be detected by simpler parametric methods. In cancer trials, like those at the National Surgical Adjuvant Breast and Bowel Project, ad hoc approaches are often used when testing covariate effects, where a few transformations are compared informally and multiple testing issues may be ignored. Such "cheating" inflates the type I error rate and may give misleading results. Aim 3 proposes optimal inferences for covariates using parametric covariate transformations when developing clinical risk indices for survival endpoints. Tests are constructed which may be more powerful than naive tests with fixed transformations. These results provide critical guidance to analysts in exploratory subgroup analyses for cancer prognosis. In Aim 4, I study nonparametric quantile inference for competing risks data. Cumulative incidence estimates are often reported in cancer trials, for example, rates of locoregional recurrence with combined radiation and chemotherapy. The dependent censoring from non-locoregional events complicates quantile definition, a commonly used summary in survival analysis. Quantiles for independently censored data are widely reported using Kaplan-Meier curves, but are not appropriate with competing risks. The proposed methodology will be broadly applicable in cancer applications, addressing a key methodologic gap in cancer research. For each aim, user friendly software will made publicly avaiable. Expository papers in leading scientific journals will disseminate the methodology to high impact subject matter audiences. PUBLIC HEALTH RELEVANCE: The goal of this grant is to develop statistical methods for time-to-event endpoints which will be widely applicable in clinical, epidemiologic, and basic scientific research in oncology. The methods will be useful in identifying familial and environmental risk factors which are critical to correctly assessing future cancer risk in unaffected individuals and to developing effective preventive and therapeutic interventions in cancer patients. Current statistical methods are inadequate and have hindered the design and analysis of studies which could bring about improvements in cancer prognosis and treatment.
Competing risks regression for clustered data.
Authors: Zhou B. , Fine J. , Latouche A. , Labopin M. .
Source: Biostatistics (Oxford, England), 2012 Jul; 13(3), p. 371-83.
EPub date: 2011-10-31.
Summarizing differences in cumulative incidence functions.
Authors: Zhang M.J. , Fine J. .
Source: Statistics in medicine, 2008-10-30; 27(24), p. 4939-49.