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Grant Details

Grant Number: 5R01CA075971-11 Interpret this number
Primary Investigator: Betensky, Rebecca
Organization: Harvard School Of Public Health
Project Title: Statistical Methods for Analysis of Failure Time Data
Fiscal Year: 2009
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Abstract

DESCRIPTION (provided by applicant): 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.

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Publications

Hospital volume versus outcome: an unusual example of bivariate association.
Authors: Betensky RA, Christian CK, Gustafson ML, Daley J, Zinner MJ
Source: Biometrics, 2006 Jun;62(2), p. 598-604.
PMID: 16918925
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Statistical considerations for immunohistochemistry panel development after gene expression profiling of human cancers.
Authors: Betensky RA, Nutt CL, Batchelor TT, Louis DN
Source: J Mol Diagn, 2005 May;7(2), p. 276-82.
PMID: 15858152
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Power calculations for familial aggregation studies.
Authors: Rabbee N, Betensky RA
Source: Genet Epidemiol, 2004 May;26(4), p. 316-27.
PMID: 15095391
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Hazard regression for interval-censored data with penalized spline.
Authors: Cai T, Betensky RA
Source: Biometrics, 2003 Sep;59(3), p. 570-9.
PMID: 14601758
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Analysis of a molecular genetic neuro-oncology study with partially biased selection.
Authors: Betensky RA, Louis DN, Cairncross JG
Source: Biostatistics, 2003 Apr;4(2), p. 167-78.
PMID: 12925514
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Local likelihood analysis of the latency distribution with interval censored intermediate events.
Authors: Bebchuk JD, Betensky RA
Source: Stat Med, 2002 Nov 30;21(22), p. 3475-91.
PMID: 12407685
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The use of frailty hazard models for unrecognized heterogeneity that interacts with treatment: considerations of efficiency and power.
Authors: Li Y, Betensky RA, Louis DN, Cairncross JG
Source: Biometrics, 2002 Mar;58(1), p. 232-6.
PMID: 11890320
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Maximally selected chi2 statistics for k x 2 tables.
Authors: Betensky RA, Rabinowitz D
Source: Biometrics, 1999 Mar;55(1), p. 317-20.
PMID: 11318175
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Approximating the distribution of maximally selected McNemar's statistics.
Authors: Rabinowitz D, Betensky RA
Source: Biometrics, 2000 Sep;56(3), p. 897-902.
PMID: 10985234
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Using conditional logistic regression to fit proportional odds models to interval censored data.
Authors: Rabinowitz D, Betensky RA, Tsiatis AA
Source: Biometrics, 2000 Jun;56(2), p. 511-8.
PMID: 10877311
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Multiple imputation for simple estimation of the hazard function based on interval censored data.
Authors: Bebchuk JD, Betensky RA
Source: Stat Med, 2000 Feb 15;19(3), p. 405-19.
PMID: 10649305
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An extension of Kendall's coefficient of concordance to bivariate interval censored data.
Authors: Betensky RA, Finkelstein DM
Source: Stat Med, 1999 Nov 30;18(22), p. 3101-9.
PMID: 10544309
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A non-parametric maximum likelihood estimator for bivariate interval censored data.
Authors: Betensky RA, Finkelstein DM
Source: Stat Med, 1999 Nov 30;18(22), p. 3089-100.
PMID: 10544308
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