Abstract

The major purpose of this research is to develop new methods for the analysis of time to event data that are encountered in cancer research. Specifically, we propose the use of the accelerated failure time model for relating the effect of time dependent covariates to survival. We will demonstrate how these models could be used for analyzing the effect of treatment in a randomized clinical trial when compliance problems are an issue. Semiparametric estimates of the parameters will be obtained by using rank methods for censored data. The theoretical properties of these estimates will be established using counting processes and martingale methods. Computer algorithms will be developed for finding these estimates in an efficient manner and the small sample properties will be assessed by computer simulation. We will also develop tests for comparing multiple time to event data that are subject to right censoring among different treatments. Finally, we shall use survival methods to study the problem of constructing group sequential tests to compare response rates when there is lag time in reporting.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
1R01CA051962-01
Application #
3196721
Study Section
Special Emphasis Panel (SSS (A))
Project Start
1990-04-15
Project End
1993-03-31
Budget Start
1990-04-15
Budget End
1991-03-31
Support Year
1
Fiscal Year
1990
Total Cost
Indirect Cost

  Institution

Name
Dana-Farber Cancer Institute
Department
Type
DUNS #
149617367
City
Boston
State
MA
Country
United States
Zip Code
02215

  Publications

Milanzi, Elasma; Molenberghs, Geert; Alonso, Ariel et al. (2016) Properties of Estimators in Exponential Family Settings with Observation-based Stopping Rules. J Biom Biostat 7:
Milanzi, Elasma; Molenberghs, Geert; Alonso, Ariel et al. (2015) Estimation After a Group Sequential Trial. Stat Biosci 7:187-205
Molenberghs, Geert; Kenward, Michael G; Aerts, Marc et al. (2014) On random sample size, ignorability, ancillarity, completeness, separability, and degeneracy: sequential trials, random sample sizes, and missing data. Stat Methods Med Res 23:11-41
Schulte, Phillip J; Tsiatis, Anastasios A; Laber, Eric B et al. (2014) Q- and A-learning Methods for Estimating Optimal Dynamic Treatment Regimes. Stat Sci 29:640-661
Vock, David M; Tsiatis, Anastasios A; Davidian, Marie et al. (2013) Assessing the causal effect of organ transplantation on the distribution of residual lifetime. Biometrics 69:820-9
Zhang, Baqun; Tsiatis, Anastasios A; Laber, Eric B et al. (2012) A robust method for estimating optimal treatment regimes. Biometrics 68:1010-8
Vock, David M; Davidian, Marie; Tsiatis, Anastasios A et al. (2012) Mixed model analysis of censored longitudinal data with flexible random-effects density. Biostatistics 13:61-73
Cai, Na; Lu, Wenbin; Zhang, Hao Helen (2012) Time-varying latent effect model for longitudinal data with informative observation times. Biometrics 68:1093-102
Tsiatis, Anastasios A; Davidian, Marie (2011) Discussion of ""Connections Between Survey Calibration Estimators and Semiparametric Models for Incomplete Data"" by T. Lumley, P.A. Shaw & J.Y. Dai. Int Stat Rev 79:221-223
Tsiatis, Anastasios A; Davidian, Marie; Cao, Weihua (2011) Improved doubly robust estimation when data are monotonely coarsened, with application to longitudinal studies with dropout. Biometrics 67:536-45

Showing the most recent 10 out of 41 publications