Abstract

The major purpose of this research is to develop new methods for the design and analysis of complex data that are encountered in cancer clinical trials. The research will focus on four topics. ? ? 1. Efficient methods will be developed to estimate the distribution of time to event with censored data for two-stage treatment policies where one treatment is given up front as induction therapy followed by maintenance treatment given only to patients after responding to the induction treatment. Tests comparing different treatment policies will also be developed. ? ? 2. Methods for estimating the effect that treatment duration has on response will be developed for situations where treatment duration may be terminated prematurely due to adverse events using observational data. ? ? 3. Efficient and robust methods will be developed for estimating the effect of covariates on death from a specific cause using a proportional hazards model when some of the cause of death information are missing. ? ? 4. We will demonstrate the inefficiency of the adaptive design for monitoring clinical trials and develop sequential methods that are uniformly better. ? ?

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA051962-16
Application #
7024475
Study Section
Special Emphasis Panel (ZRG1-SNEM-2 (03))
Program Officer
Tiwari, Ram C
Project Start
1990-04-15
Project End
2007-07-31
Budget Start
2006-03-01
Budget End
2007-07-31
Support Year
16
Fiscal Year
2006
Total Cost
$208,528
Indirect Cost

  Institution

Name
North Carolina State University Raleigh
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
042092122
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