Grant Number:	2R01CA083932-08A2 Interpret this number
Primary Investigator:	Thall, Peter
Organization:	University Of Tx Md Anderson Can Ctr
Project Title:	Statistical Methods for Complex Cancer Trials
Fiscal Year:	2009

DESCRIPTION (provided by applicant): The proposed research is motivated by problems arising in the design and analysis of complex cancer clinical trials. A general goal is to accommodate clinical settings, data structures and scientific aims that are more complex than those addressed by designs in the conventional phase I phase II phase III paradigm. The proposed research deals with trials having sequential, outcome-adaptive decisions, including selecting what treatment, dose or schedule to give the next patient, deciding whether the trial should be stopped early, deciding whether accrual should be stopped for particular patient subgroups, and what final conclusions may be drawn about treatment effects. At each interim analysis during trial conduct, a Bayesian model is fit to the current data and posterior-based decision criteria are computed and applied. Each design's operating characteristics are evaluated by computer simulation under a set of scenarios, where a scenario is characterized by a fixed parameter vector of the assumed probability model, or under alternative probability distributions to study robustness. The scenarios are chosen to represent a suitably wide range of clinically meaningful cases. Operating characteristics include trial duration, number of patients assigned to each treatment or dose, and probabilities of possible decisions and conclusions. These evaluations are used to calibrate design parameters to ensure that the design has scientifically and ethically desirable properties. The proposed projects encompass a variety of clinical settings, including dose-finding trials, phase II-III trials, two- arm phase III trials with multiple outcomes, and any Bayesian setting where an informative prior is elicited. Models, methods, and computational algorithms will be developed for each of the following: (1) Choosing the optimal dose pair of a chemotherapeutic agent and a biologic agent used in combination. Patient outcome is a trinary vector of ordinal variables accounting for two types of toxicity, one associated with each agent, and treatment efficacy. Based on elicited numerical utilities of the possible patient outcomes, a dose pair is chosen for each successive patient cohort to maximize the current posterior mean utility. (2) Comparing multiple experimental treatments to standard therapy based on toxicity and progression-free-survival (PFS) time. The probability of a two-dimensional region based on elicited joint toxicity and PFS target probabilities will be used as the basis for treatment selection and confirmatory comparison, allowing the probability of toxicity and the PFS time distribution each to vary with patient covariates, while controlling overall generalized power and type I error. (3) Comparing treatments in a two-arm trial based on the trade-off between two outcomes such as, for example, quality of life and survival time. A general geometric method is proposed in which decisions are based on posterior probabilities of four sets that parition a two-dimensional parameter space. (4) Using nonlinear regression to estimate prior hyperparameters from elicited information. The goal is to provide a general, practical method for establishing priors in a Bayesian analysis based on information elicited from area experts in settings where the number of pieces of elicited information is much larger than the number of hyperparameters characterizing the prior. PUBLIC HEALTH RELEVANCE: The proposed research will provide more efficient and more ethically desirable designs for complex cancer clinical trials by making formal use of historical data, using individualized, patient-specific rules for treatment assignment and early stopping, and combining successive phases of treatment evaluation. The improved efficiencies will accelerate clinical evaluation and increase the likelihood that new treatments providing a clinical improvement over standard therapies will be detected while unpromising or unsafe treatments will be dropped.




Risk-benefit trade-offs and precision utilities in phase I-II clinical trials.
Authors: Msaouel P. , Lee J. , Thall P.F. .
Source: Clinical Trials (london, England), 2024 Jun; 21(3), p. 287-297.
EPub date: 2023-12-18 00:00:00.0.
PMID: 38111231
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Bayesian non-parametric survival regression for optimizing precision dosing of intravenous busulfan in allogeneic stem cell transplantation.
Authors: Xu Y. , Thall P.F. , Hua W. , Andersson B.S. .
Source: Journal Of The Royal Statistical Society. Series C, Applied Statistics, 2019 Apr; 68(3), p. 809-828.
EPub date: 2018-12-16 00:00:00.0.
PMID: 31467455
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Bayesian Semiparametric Joint Regression Analysis of Recurrent Adverse Events and Survival in Esophageal Cancer Patients.
Authors: Lee J. , Thall P.F. , Lin S.H. .
Source: The Annals Of Applied Statistics, 2019 Mar; 13(1), p. 221-247.
EPub date: 2019-04-10 00:00:00.0.
PMID: 31681453
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Optimizing natural killer cell doses for heterogeneous cancer patients on the basis of multiple event times.
Authors: Lee J. , Thall P.F. , Rezvani K. .
Source: Journal Of The Royal Statistical Society. Series C, Applied Statistics, 2019 Feb; 68(2), p. 461-474.
EPub date: 2018-03-15 00:00:00.0.
PMID: 31105345
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Subgroup-specific dose finding in phase I clinical trials based on time to toxicity allowing adaptive subgroup combination.
Authors: Chapple A.G. , Thall P.F. .
Source: Pharmaceutical Statistics, 2018 Nov; 17(6), p. 734-749.
EPub date: 2018-08-15 00:00:00.0.
PMID: 30112806
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A Hybrid Phase I-II/III Clinical Trial Design Allowing Dose Re-Optimization in Phase III.
Authors: Chapple A.G. , Thall P.F. .
Source: Biometrics, 2018-10-26 00:00:00.0; , .
EPub date: 2018-10-26 00:00:00.0.
PMID: 30367457
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A utility-based design for randomized comparative trials with ordinal outcomes and prognostic subgroups.
Authors: Murray T.A. , Yuan Y. , Thall P.F. , Elizondo J.H. , Hofstetter W.L. .
Source: Biometrics, 2018-01-22 00:00:00.0; , .
EPub date: 2018-01-22 00:00:00.0.
PMID: 29359314
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A Bayesian Machine Learning Approach for Optimizing Dynamic Treatment Regimes.
Authors: Murray T.A. , Yuan Y. , Thall P.F. .
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A simulation study of outcome adaptive randomization in multi-arm clinical trials.
Authors: Wathen J.K. , Thall P.F. .
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EPub date: 2017-02-01 00:00:00.0.
PMID: 28982263
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Bayesian variable selection for a semi-competing risks model with three hazard functions.
Authors: Chapple A.G. , Vannucci M. , Thall P.F. , Lin S. .
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Bayesian nonparametric statistics: A new toolkit for discovery in cancer research.
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A Simulation Study Of Methods For Selecting Subgroup-specific Doses In Phase 1 Trials
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Parametric Dose Standardization for Optimizing Two-Agent Combinations in a Phase I-II Trial with Ordinal Outcomes.
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Source: Journal Of The Royal Statistical Society. Series C, Applied Statistics, 2017 Jan; 66(1), p. 201-224.
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Bayesian treatment comparison using parametric mixture priors computed from elicited histograms.
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Robust treatment comparison based on utilities of semi-competing risks in non-small-cell lung cancer.
Authors: Murray T.A. , Thall P.F. , Yuan Y. , McAvoy S. , Gomez D.R. .
Source: Journal Of The American Statistical Association, 2017; 112, p. 11-23.
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A Decision-Theoretic Comparison of Treatments to Resolve Air Leaks After Lung Surgery Based on Nonparametric Modeling.
Authors: Xu Y. , Thall P.F. , Müller P. , Mehran R.J. .
Source: Bayesian Analysis, 2017; 12(3), p. 639-652.
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Utility-based designs for randomized comparative trials with categorical outcomes.
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Source: Statistics In Medicine, 2016-10-30 00:00:00.0; 35(24), p. 4285-4305.
EPub date: 2016-10-30 00:00:00.0.
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Bayesian regression analyses of radiation modality effects on pericardial and pleural effusion and survival in esophageal cancer.
Authors: He L. , Chapple A. , Liao Z. , Komaki R. , Thall P.F. , Lin S.H. .
Source: Radiotherapy And Oncology : Journal Of The European Society For Therapeutic Radiology And Oncology, 2016 Oct; 121(1), p. 70-74.
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The potential for adaptive maintenance of diversity in insect antimicrobial peptides.
Authors: Unckless R.L. , Lazzaro B.P. .
Source: Philosophical Transactions Of The Royal Society Of London. Series B, Biological Sciences, 2016-05-26 00:00:00.0; 371(1695), .
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A decision-theoretic phase I-II design for ordinal outcomes in two cycles.
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Bayesian Group Sequential Clinical Trial Design using Total Toxicity Burden and Progression-Free Survival.
Authors: Hobbs B.P. , Thall P.F. , Lin S.H. .
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Bayesian Nonparametric Estimation For Dynamic Treatment Regimes With Sequential Transition Times
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Optimization of multi-stage dynamic treatment regimes utilizing accumulated data.
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Phase 1 adaptive dose-finding study of neoadjuvant gemcitabine combined with radiation therapy for patients with high-risk extremity and trunk soft tissue sarcoma.
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Phase I/II trial of lenalidomide and high-dose melphalan with autologous stem cell transplantation for relapsed myeloma.
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Statistical controversies in clinical research: scientific and ethical problems with adaptive randomization in comparative clinical trials.
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Bayesian Dose-Finding in Two Treatment Cycles Based on the Joint Utility of Efficacy and Toxicity.
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Bayesian nonparametric estimation of targeted agent effects on biomarker change to predict clinical outcome.
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Effective sample size for computing prior hyperparameters in Bayesian phase I-II dose-finding.
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A Modified Adaptive Lasso for Identifying Interactions in the Cox Model with the Heredity Constraint.
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Optimizing Sedative Dose in Preterm Infants Undergoing Treatment for Respiratory Distress Syndrome.
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Increasing chimerism after allogeneic stem cell transplantation is associated with longer survival time.
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Using Data Augmentation to Facilitate Conduct of Phase I-II Clinical Trials with Delayed Outcomes.
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Using joint utilities of the times to response and toxicity to adaptively optimize schedule-dose regimes.
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Evaluating Joint Effects of Induction-Salvage Treatment Regimes on Overall Survival in Acute Leukemia.
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Prior Effective Sample Size in Conditionally Independent Hierarchical Models.
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Evaluation of Viable Dynamic Treatment Regimes in a Sequentially Randomized Trial of Advanced Prostate Cancer.
Authors: Wang L. , Rotnitzky A. , Lin X. , Millikan R.E. , Thall P.F. .
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A Hybrid Geometric Phase II/III Clinical Trial Design based on Treatment Failure Time and Toxicity.
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Estimating progression-free survival in paediatric brain tumour patients when some progression statuses are unknown.
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Adaptive randomization to improve utility-based dose-finding with bivariate ordinal outcomes.
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Optimizing the concentration and bolus of a drug delivered by continuous infusion.
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Defining and ranking effects of individual agents based on survival times of cancer patients treated with combination chemotherapies.
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Placental growth factor and soluble c-kit receptor dynamics characterize the cytokine signature of imatinib in prostate cancer and bone metastases.
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Utility-based optimization of combination therapy using ordinal toxicity and efficacy in phase I/II trials.
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Bayesian Models and Decision Algorithms for Complex Early Phase Clinical Trials.
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Atypical Choroid Plexus Papilloma: Clinical Experience In The Cpt-siop-2000 Study
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A Bayesian hierarchical mixture model for platelet derived growth factor receptor phosphorylation to improve estimation of progression-free survival in prostate cancer.
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