Studies of cancer and AIDS typically record clinical events and laboratory measurements for participating subjects at several time points. Frequently, the times to the clinical events and their association are of primary interest. The relationship between the serial laboratory measurements and the times to clinical events is of interest also for the improvement of patient management and for the identification of potential surrogate markers. These data are usually plagued by problems of missingness and censoring due to missed study visits or to the discrete time observation of continuous processes. Interval censored data arise when exact event times are censored within intervals that are unique to each individual, and thus are overlapping. The broad goal of this proposal is the development of methods for analysis of interval and right censored data that addresses medical questions frequently posed by clinicians. The first specific aim of this proposal is the estimation of the distribution function for bivariate and univariate interval censored failure time data. Both nonparamaetric and """"""""loosely parametric"""""""" estimators will be derived.
The second aim i s he analysis of failure time data with accompanying right and interval censored intermediate event times. Smooth estimates of the hazard functions for the terminal event before and after the intermediate event, an estimate of the survivor function adjusted for the intermediate event, and estimates of the latency distribution between the times will be derived.
The third aim i s the development of methods for testing for independence between bivariate interval censored data, assessing the impact of covariates on multiple interval censored outcomes, and comparing adjusted survivor curves.
The fourth aim i s the development of methods for analysis of interval censored data that are derived from serial laboratory measurements. These include a new parametric frailty model for interval censored data, adjustment for the measurement error and biologic variation of the underlying processes, and estimation of smooth hazard functions for interval censored data in the presence of a time-varying covariate. To accomplish these aims, local likelihood estimation, multiple imputation, estimating equations, approximations of first passage time distributions for continuous stochastic processes, and convex optimization theory will be used.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
First Independent Research Support & Transition (FIRST) Awards (R29)
Project #
5R29CA075971-05
Application #
6489111
Study Section
Special Emphasis Panel (ZRG7-STA (01))
Program Officer
Erickson, Burdette (BUD) W
Project Start
1998-01-01
Project End
2003-03-31
Budget Start
2002-01-01
Budget End
2003-03-31
Support Year
5
Fiscal Year
2002
Total Cost
$112,645
Indirect Cost
Name
Harvard University
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
City
Boston
State
MA
Country
United States
Zip Code
02115
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
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
Mi, Michael Y; Betensky, Rebecca A (2013) An analysis of adaptive design variations on the sequential parallel comparison design for clinical trials. Clin Trials 10:207-15
Kemmling, André; Lev, Michael H; Payabvash, Seyedmehdi et al. (2013) Hospital acquired pneumonia is linked to right hemispheric peri-insular stroke. PLoS One 8:e71141
Mandel, Micha; Mercier, Francois; Eckert, Benjamin et al. (2013) Estimating time to disease progression comparing transition models and survival methods--an analysis of multiple sclerosis data. Biometrics 69:225-34
Schoenfeld, David A; Rajicic, Natasa; Ficociello, Linda H et al. (2011) A test for the relationship between a time-varying marker and both recovery and progression with missing data. Stat Med 30:718-24
Finkelstein, Dianne M; Wang, Rui; Ficociello, Linda H et al. (2010) A score test for association of a longitudinal marker and an event with missing data. Biometrics 66:726-32
Greenberg, Steven M; Nandigam, R N Kaveer; Delgado, Pilar et al. (2009) Microbleeds versus macrobleeds: evidence for distinct entities. Stroke 40:2382-6
Rajicic, Natasa; Finkelstein, Dianne M; Schoenfeld, David A et al. (2009) Analysis of the relationship between longitudinal gene expressions and ordered categorical event data. Stat Med 28:2817-32
Betensky, Rebecca A; Louis, David N; Cairncross, J Gregory (2002) Influence of unrecognized molecular heterogeneity on randomized clinical trials. J Clin Oncol 20:2495-9