The primary objectives of this proposal are to develop robust and efficient Bayesian adaptive designs for early phase oncology clinical trials with late-onset outcomes, and to propose a semi-parametric estimate of the dose-response curve. Conventional early phase trial designs typically assume that the toxicity and efficacy outcomes are observed shortly after the initiation of the treatment in order to assign an appropriate dose to patients newly enrolled in the trial. However, late-onset toxicity and efficacy are common in phase I studies. In the presence of late onset toxicity, using conventional trial designs may underestimate the toxicity probabilities, which would cause an undesirably large number of patients to be treated at overly toxic doses;and late onset efficacy often leads investigators to underestimate treatment efficacy and to incorrectly terminate a trial early. Moreover, parametric dose-toxicity and dose-efficacy model assumptions employed by many available early phase trial designs are not desirable, as asymptotic properties are generally not applicable for small sample sizes in early-phase trials. Misspecification of the dose-toxicity and dose-efficacy models may lead to poor operating characteristics of the trial. In this proposal, we develop robust and efficient Bayesian adaptive designs for phase I or phase I/II oncology clinical trials with late-onset outcomes. We formulate late-onset outcomes as a missing data problem and rigorously investigate characteristics and theories of the missing data induced by the late-onset outcomes. Based upon these investigations, we propose single- and multiple-agent phase I dose-finding trial designs, in which late-onset toxicity is addressed by the Bayesian data augmentation and the EM algorithm. To improve the robustness of the proposed trial designs, we propose to consider multiple dose-toxicity models simultaneously and then use Bayesian model averaging and model selection procedures to obtain robust estimates and desirable operating characteristics. Another common problem of interest in early-phase clinical trials is to estimate the relationship between the dose level of a drug and the probability of a response (e.g., toxicity or efficacy). We propose an efficient and robust semi-parametric approach that combines the advantages of parametric and nonparametric approaches. Our estimate of the dose-response curve is a weighted average of the parametric estimate and nonparametric estimate. When the true curve follows a parametric model assumption, the estimate converges to the parametric estimate, thus achieving high efficiency. When the parametric model does not hold, the estimate converges to the nonparametric estimate, thereby still providing a consistent estimate of the true dose response curve.

Public Health Relevance

Cancer has been the second deaths-leading cause in U.S. The proposed research aims to provide more efficient, robust and innovative Bayesian cancer clinical trial designs to help physicians to develop new drugs and therapies to cure cancer.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA154591-04
Application #
8635983
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Witherspoon, Kim
Project Start
2011-04-01
Project End
2015-03-31
Budget Start
2014-04-01
Budget End
2015-03-31
Support Year
4
Fiscal Year
2014
Total Cost
$286,214
Indirect Cost
$105,066
Name
University of Texas MD Anderson Cancer Center
Department
Biostatistics & Other Math Sci
Type
Other Domestic Higher Education
DUNS #
800772139
City
Houston
State
TX
Country
United States
Zip Code
77030
Zang, Yong; Lee, J Jack; Yuan, Ying (2014) Adaptive designs for identifying optimal biological dose for molecularly targeted agents. Clin Trials 11:319-327
Jin, Ick Hoon; Liu, Suyu; Thall, Peter F et al. (2014) Using Data Augmentation to Facilitate Conduct of Phase I-II Clinical Trials with Delayed Outcomes. J Am Stat Assoc 109:525-536
Cai, Chunyan; Yuan, Ying; Ji, Yuan (2014) A Bayesian Dose-finding Design for Oncology Clinical Trials of Combinational Biological Agents. J R Stat Soc Ser C Appl Stat 63:159-173
Cai, Chunyan; Liu, Suyu; Yuan, Ying (2014) A Bayesian design for phase II clinical trials with delayed responses based on multiple imputation. Stat Med 33:4017-28
Liu, Suyu; Yuan, Ying; Castillo, Richard et al. (2014) Evaluation of image registration spatial accuracy using a Bayesian hierarchical model. Biometrics 70:366-77
Cai, Chunyan; Yuan, Ying; Johnson, Valen E (2013) Bayesian adaptive phase II screening design for combination trials. Clin Trials 10:353-62
Ahn, Jaeil; Yuan, Ying; Parmigiani, Giovanni et al. (2013) DeMix: deconvolution for mixed cancer transcriptomes using raw measured data. Bioinformatics 29:1865-71
Yuan, Ying; Johnson, Valen E (2012) Goodness-of-fit diagnostics for Bayesian hierarchical models. Biometrics 68:156-64
Yuan, Ying; Thall, Peter F; Wolff, Johannes E (2012) Estimating progression-free survival in paediatric brain tumour patients when some progression statuses are unknown. J R Stat Soc Ser C Appl Stat 61:135-149
Yuan, Ying; Huang, Xuelin; Liu, Suyu (2011) A Bayesian response-adaptive covariate-balanced randomization design with application to a leukemia clinical trial. Stat Med 30:1218-29

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