The goal of this proposal is to develop statistical methods for the analysis of failure time data that involve missing prognostic factors, complicated ascertainment, and/or dependencies.
The Specific Aims are motivated by problems arising in natural history clinical studies of brain tumors and laboratory studies of the molecular biology of brain tumors and by studies of familial aggregation of multiple cancers. Empirical data analysis will play a central role in each of the Specific Aims. Brain tumors typically have histological diagnoses that are only weakly associated with prognosis and genetic features that are only partially known. The impact of the resultant unexplained heterogeneity will be investigated for a general class of failure time models. Adaptive designs to remedy the resultant loss of power will be proposed. The known heterogeneity of brain tumors, and the resultant small numbers of subjects with a given diagnosis, frequently precludes large prospective studies. This leads to the use of natural history studies, and introduces delayed entry (truncation). Tests of independence of truncation and failure for a variety of situations will be derived and evaluated, and methods that appropriately adjust for dependence will be developed. Familial aggregation studies are characterized by dependencies among and within family members and by complex ascertainment schemes. Computationally simple methods will be developed for analysis of scientific quantities of interest, i.e., conditional and partially marginal measures of response and association that retain interpretability with families of varying sizes. Extensions to ordinal and censored data will be derived. Software will be developed, and made freely available, for optimal family study design, allowing for multiple disease outcomes and complex, non-random ascertainment. ? ?

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA075971-07
Application #
6717617
Study Section
Social Sciences, Nursing, Epidemiology and Methods 4 (SNEM)
Program Officer
Tiwari, Ram C
Project Start
1998-01-01
Project End
2006-03-31
Budget Start
2004-04-01
Budget End
2005-03-31
Support Year
7
Fiscal Year
2004
Total Cost
$204,844
Indirect Cost
Name
Harvard University
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
149617367
City
Boston
State
MA
Country
United States
Zip Code
02115
Chiou, Sy Han; Austin, Matthew D; Qian, Jing et al. (2018) Transformation model estimation of survival under dependent truncation and independent censoring. Stat Methods Med Res :962280218817573
Swanson, D M; Anderson, C D; Betensky, R A (2018) Hypothesis Tests for Neyman's Bias in Case-Control Studies. J Appl Stat 45:1956-1977
Emerson, Sarah C; Waikar, Sushrut S; Fuentes, Claudio et al. (2018) Biomarker validation with an imperfect reference: Issues and bounds. Stat Methods Med Res 27:2933-2945
Matsouaka, Roland A; Singhal, Aneesh B; Betensky, Rebecca A (2018) An optimal Wilcoxon-Mann-Whitney test of mortality and a continuous outcome. Stat Methods Med Res 27:2384-2400
Atem, Folefac D; Qian, Jing; Maye, Jacqueline E et al. (2017) Linear Regression with a Randomly Censored Covariate: Application to an Alzheimer's Study. J R Stat Soc Ser C Appl Stat 66:313-328
Rentz, Dorene M; Mormino, Elizabeth C; Papp, Kathryn V et al. (2017) Cognitive resilience in clinical and preclinical Alzheimer's disease: the Association of Amyloid and Tau Burden on cognitive performance. Brain Imaging Behav 11:383-390
Atem, Folefac D; Qian, Jing; Maye, Jacqueline E et al. (2016) Multiple Imputation of a Randomly Censored Covariate Improves Logistic Regression Analysis. J Appl Stat 43:2886-2896
Swanson, David M; Betensky, Rebecca A (2015) Research participant compensation: A matter of statistical inference as well as ethics. Contemp Clin Trials 45:265-269
Betensky, Rebecca A (2015) Measures of follow-up in time-to-event studies: Why provide them and what should they be? Clin Trials 12:403-8
Matsouaka, Roland A; Betensky, Rebecca A (2015) Power and sample size calculations for the Wilcoxon-Mann-Whitney test in the presence of death-censored observations. Stat Med 34:406-31

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