The goal of this application is to develop statistical methods for the analysis of failure time data in the presence of missing diagnoses or classification, missing observation time and progression measurement, and missing segments of the target population. These types of missingness are not individual-specific, but rather are characteristic of the entire study population. That is, all subjects are missing a histologically-based diagnosis, or all subjects are missing continuous-valued, continuous-time measurements of progression, or all subjects of a certain type are missing from the study population. These three dimensions of population- wide missingness encompass a broad range of real problems that I have encountered in studies of brain tumors, schwannomas, Multiple Sclerosis (MS), and coronary heart disease (CHD). The brain tumor and schwannoma studies measure several histologic features, with the goal of refining diagnoses to be more prognostic for clinical outcomes. The MS study features a failure endpoint that is defined by a discrete time ordinal longitudinal process. This is common in many cancer studies, which have scored endpoints such as radiologic progression and performance status. The CHD study typifies an emerging common design in which prospective cohorts are sampled mid-study for genetic analysis. This is commonly done to investigate genetic associations with various cancers, as well. An array of statistical methods, including Bayesian and frequentist latent class models, transitional models for ordinal processes, and pseudo likelihood estimation for a biased sample, are used to address these problems. Relevance:
This research aims to provide improved statistical methods for the design and analysis of clinical and laboratory studies of cancer. The methods may lead to faster discovery of cancer genes and effective treatments and to better understanding of disease progression through more efficient use of resources. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA075971-10
Application #
7488601
Study Section
Special Emphasis Panel (ZRG1-HOP-Q (03))
Program Officer
Mariotto, Angela B
Project Start
1998-01-01
Project End
2010-08-31
Budget Start
2008-09-16
Budget End
2009-08-31
Support Year
10
Fiscal Year
2008
Total Cost
$238,926
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
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
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
Swanson, David M; Betensky, Rebecca A (2015) Research participant compensation: A matter of statistical inference as well as ethics. Contemp Clin Trials 45:265-9
Betensky, Rebecca A; Szymonifka, Jackie; Lee, Eudocia Q et al. (2015) Computationally simple analysis of matched, outcome-based studies of ordinal disease states. Stat Med 34:2514-27
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
Mormino, Elizabeth C; Betensky, Rebecca A; Hedden, Trey et al. (2014) Amyloid and APOE ε4 interact to influence short-term decline in preclinical Alzheimer disease. Neurology 82:1760-7
Qian, Jing; Payabvash, Seyedmehdi; Kemmling, André et al. (2014) Variable selection and prediction using a nested, matched case-control study: Application to hospital acquired pneumonia in stroke patients. Biometrics 70:153-63
Austin, Matthew D; Betensky, Rebecca A (2014) Eliminating bias due to censoring in Kendall's tau estimators for quasi-independence of truncation and failure. Comput Stat Data Anal 73:16-26
Qian, Jing; Betensky, Rebecca A (2014) Assumptions regarding right censoring in the presence of left truncation. Stat Probab Lett 87:12-17
Austin, Matthew D; Simon, David K; Betensky, Rebecca A (2014) Computationally simple estimation and improved efficiency for special cases of double truncation. Lifetime Data Anal 20:335-54

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