Abstract

The major purpose of this research is to develop new methods for the design and analysis of time to event data that are encountered in cancer clinical trials. The research will focus on four topics. 1. Methods will be developed for jointly modeling and estimating the relationship of longitudinally measured covariates to censored survival data using a proportional hazards regression model. 2. A comprehensive approach will be developed for estimating and testing relationships regarding a primary outcome variable that is missing on some individuals due to incomplete follow-up. 3. Since the difficulty with incomplete follow-up is most pronounced during interim monitoring, improper inferential procedures on the primary outcome can severely bias stopping rules for early termination of a clinical trial. We will show how to use the tests derived in topic (2) above to build group-sequential stopping rules which have the appropriate operating characteristics. 4. We will show how multiple imputation can be used to estimate the parameters and the standard error of these estimates in a proportional hazards model with missing covariates.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA051962-12
Application #
6512674
Study Section
Special Emphasis Panel (ZRG1-SNEM-5 (01))
Program Officer
Erickson, Burdette (BUD) W
Project Start
1990-04-15
Project End
2003-02-28
Budget Start
2002-03-01
Budget End
2003-02-28
Support Year
12
Fiscal Year
2002
Total Cost
$155,164
Indirect Cost

  Institution

Name
North Carolina State University Raleigh
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
City
Raleigh
State
NC
Country
United States
Zip Code
27695

  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
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
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
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