Peter F. Thall’s Personal Website
Here is a
picture of me being a biostatistician, with a pen, a pad, a computer, and
everything. This is what “science” looks like in real life.
A proper website
seems to require a list of hobbies. In keeping with this dubious convention,
here are some of mine:
trading stock options; nonverbal canine communication; aggressively practicing
humility; intergalactic exploration; watching nearly every movie ever made;
gourmet cooking; giving terribly useful advice to people who seem to need it;
staying in hotels in cities I probably wouldn’t visit if I weren’t
attending a conference; collecting
For a copy
of my CV, click here.
Book on Dose Finding Trials
Bayesian Designs for Phase I-II
Yuan, Nguyen, and Thall. Chapman &
Hall/CRC Biostatistics Series, 2016.
Actual Reviews by Really Smart People
book is a must-read for students, statisticians, principal investigators and
researchers who wish to apply innovative and more ethical designs for Phase
I/II clinical trials. Several statisticians have previously proposed designs
for dose-finding studies modelling the dose-toxicity and the dose-efficacy
relationships. However such methods have been published in highly specialized
statistical/biostatistical journals that are not very accessible nor
comprehensible for non-initiated readers. To the best of my knowledge, no book
has yet solely focused on the design of Phase I/II clinical trials, despite the
fact that these studies represent 33% of all conducted trials (source:
ClinicalTrials.gov). This excellent book offers a well written and a step by
step guide to planning, conducting and analyzing Phase I/II clinical
trials." —Sarah Zohar, The French National
Institute of Health and Medical Research (Inserm),
Nguyen and Thall are statisticians on the forefront of both theoretical
statistics and practical implementation of adaptive trial designs, and have
combined their knowledge and experience here to provide an exceptional
textbook… A highlight of the text is a chapter on choosing priors, where the
authors demonstrate that prior calibration is critical. Casual choice of priors
can be disastrous in these trials (which have small cohorts and often small
sample sizes), and Yuan et al. provide examples to demonstrate how poorly
chosen priors can ruin operating characteristics. Complex trial designs are
explained in a clear and sensible manner, making the arguments very plainly
obvious as to the benefits of these more modern designs. The authors have
pioneered adaptive approaches for dose-finding combining toxicity and efficacy
trade-offs and present these and other designs that jointly model toxicity and
efficacy… The writing style is conversational in places, making this text more
enjoyable to read than many other statistics textbooks, and readers will
appreciate the chapters that address practical problems often ignored in
theoretical clinical trial texts: late onset and cumulative (toxicity)
outcomes, molecularly targeted agents, and missing data in adaptive designs.
Interactive software with a user-friendly interface is available for many of
the designs, with illustrations in the text which demonstrate the
implementation." —Elizabeth Garrett-Mayer, Professor of Biostatistics,
Medical University of South Carolina
book covers almost every topic that you will need when designing Phase I, Phase
II, and Phase I-II clinical trials. Each chapter is a treasure trove of
wonderful new ideas, and contains examples - based on the authors'
outstandingly broad experiences – that help the reader clearly understand the
methodological aspects involved in clinical trials… This book is a
"must-have" for every biostatistician involved in clinical
trials." —Satoshi Morita, Department of Biomedical Statistics and
Bioinformatics, Kyoto University
If you want
to read some of my publications, click on the links below. All of them are about statistics, medicine,
or clinical trials, and most are astonishingly well written. If you like one of them, be sure to tell your
friends and relatives about it.
Random Measures With Aftereffects, Annals of Probability, 1978
A theorem on regular
infinitely divisible Cox processes. Stochastic Processes and Their Applications
16: 205-210, 1983
Two-stage selection and testing
designs for comparative clinical trials, Biometrika, 1988
Analysis of recurrent events:
nonparametric methods for random interval count data. JASA 83: 339-347, 1988
regression models for longitudinal interval count data. Biometrics 44: 197-209,
Some covariance models
for longitudinal count data with overdispersion.
Biometrics 46: 657-671, 1990.
Test-based variable selection via cross-validation. J Computational
and Graphical Stat 1: 41-61, 1992
Practical Bayesian guidelines
for phase IIB clinical trials. Biometrics 50: 337-349, 1994
Bayesian sequential monitoring
designs for single-arm clinical trials with multiple outcomes. Stat in Med
Estimating genomic category
probabilities from fluorescent in situ hybridization counts with
misclassification. JRSS-C (Applied Statistics) 45:431-446, 1996
Variable selection in regression via repeated data
splitting. J Comp and Graphical Stat,
Parametric likelihoods for multiple
non-fatal competing risks and death. Stat in Med, 17:999-1016, 1998
Some extensions and applications of a Bayesian
strategy for monitoring multiple outcomes in clinical trials. Stat in Med, 17:1563-1580, 1998.
A strategy for dose finding and
safety monitoring based on efficacy and adverse outcomes in phase I/II clinical
trials. Biometrics 54:251-264, 1998.
Based on Two-Dimensional Safety and Efficacy Alternatives in Oncology Trials,
Designs for Phase II Clinical Trials with Multiple Outcomes, Biometrics 1999
Accrual strategies for phase I trials with delayed
patient outcome, Statistics in Medicine, 1999
Designs for Phase II Clinical Trials with Multiple Outcomes, Biometrics 1999
Evaluating multiple treatment
courses in clinical trials. Stat in Med,
19: 1011-1028, 2000
Evaluation of Multiple Treatment Effects, Biometrics 56: 213-219, 2000
Designs for Pre-Phase II Screening Trials in Oncology, Biometrics 2001
Dose-Finding Based on
Feasibility and Toxicity in T-cell Infusion Trials, Biometrics 2001
Selecting therapeutic strategies
based on efficacy and death in multi-course clinical trials. JASA, 97:29-39, 2002
Ethical issues in oncology biostatistics. Stat Meth in Medical Res, 11:429-448, 2002
Seamlessly Expanding a
Randomized Phase II Trial to Phase III, Biometrics, 2002
Adaptive Decision Making in a
Lymphocyte Infusion Trial, Biometrics, 2002
Monitoring the Rates of Composite
Events with Censored Data in Phase II Clinical Trials, Biometrics, 2002
Busulfan systemic exposure relative to
regimen-related toxicity and acute graft vs. host disease; defining a
therapeutic window for IVBuCy2 in chronic myelogenous leukemia. Biology of Blood and Marrow Transplantation,
approaches to phase II trials in diseases with multiple subtypes. Statistics in Medicine, 22: 763-780, 2003
Dose-finding with two agents
in phase I oncology trials. Biometrics,
Comparison of 100-day mortality rates
associated with IV busulfan and cyclophosphamide
versus other preparative regimens in allogeneic bone marrow transplant for chronic
myelogenous leukemia: Bayesian sensitivity analyses of confounded treatment and
center effects. Bone Marrow Transplantation, 33: 1191-1199, 2004.
Dose-finding based on multiple
toxicities in a soft tissue sarcoma trial.
JASA, 99:26-35, 2004
Dose-finding based on
efficacy-toxicity trade-offs. Biometrics, 60:684-693, 2004.
Determining a Maximum-Tolerated Schedule
of a Cytotoxic Agent, Biometrics 61, 335–343, 2005
Monitoring event times in early phase
clinical trials: some practical issues. Clinical Trials. 2:467-478, 2005
Adaptive dose selection using
efficacy-toxicity trade-offs: illustrations and practical considerations. J Biopharm Stat 16:623-638, 2006
A Geometric Approach to Comparing
Treatments for Rapidly Fatal Diseases, Biometrics 2006
Continuous Bayesian adaptive randomization
based on event times with covariates.
Stat in Med, 25:55-70, 2006
Simultaneously optimizing dose
and schedule of a new cytotoxic agent. Clinical Trials, 4:113-124, 2007
Bayesian and frequentist two-stage treatment
strategies based on sequential failure times subject to interval censoring.
Stat in Med 26:4687-4702,
Adaptive Therapy for Androgen-Independent
Prostate Cancer: A Randomized Selection Trial of Four Regimens, JNCI,
Phase I/II study of gemtuzumab ozogamicin added to fludarabine, melphalan and
allogeneic hematopoietic stem cell transplantation for high-risk CD33 positive myeloid
leukemias and myelodysplastic syndrome. Leukemia. 22:258-264, 2008
daily IV busulfan and fludarabine
(IV Bu-Flu) compares favorably with IV busulfan and
cyclophosphamide (IV BuCy2) as pretransplant
conditioning therapy in AML/MDS. Biology of Blood and Marrow Transplantation.
A review of phase 2-3 clinical trial
designs. Lifetime Data Analysis. 14:37-53, 2008
Determining the effective sample size of a
parametric prior. Biometrics.
Accounting for patient heterogeneity in
phase II clinical trials. Stat in Med 27:2802-2815, 2008
Monitoring late onset toxicities in
phase I trials using predicted risks.
Biostatistics. 9:442-457, 2008
Patient-Specific Dose Finding
Based on Bivariate Outcomes and Covariates, Biometrics, 2008
Bayesian adaptive model selection for
optimizing group sequential clinical trials. Stat in Med, 27:5586-5604, 2008.
Bayesian hierarchical mixture model for
platelet derived growth factor receptor phosphorylation to improve estimation of
progression-free survival in prostate cancer. JRSS-C, 59:19-34, 2010.
Utility-based optimization of
combination therapy using ordinal toxicity and efficacy in phase I/II trials.
Biometrics. 66:532-540, 2010
Evaluating the impact of prior assumptions
in Bayesian biostatistics. Statistics in
Biosciences. 2:1-17, 2010
Bayesian models and decision algorithms for complex
early phase clinical trials. Statistical Science. 25:227-244, 2010
Defining and ranking effects of individual
agents based on survival times of cancer patients treated with combination
chemotherapies. Stat in Med, 30:1777-1794, 2011.
Optimizing the concentration and bolus of
a drug delivered by continuous infusion. Biometrics. 67:1638-1646, 2011
A hybrid geometric phase II-III
clinical trial design based on treatment failure time and toxicity. J Stat
Planning and Inf. 142:944-955, 2012
progression-free survival in pediatric brain tumor patients when some
progression statuses are unknown. JRSS-C, 61:135-149, 2012
Evaluation of viable dynamic
treatment regimes in a sequentially randomized trial of advanced prostate
cancer (with discussion). JASA.
107:493-508, (with discussion, pages 509-517; rejoinder, pages 518-520),
randomization to improve utility-based dose-finding with bivariate ordinal
outcomes. J Biopharmaceutical Stat. 22:785-801, 2012.
Prior effective sample size in
conditionally independent hierarchical models. Bayesian Analysis. 7:591-614,
adaptive dose-finding based on efficacy and toxicity. J. Statistical Research.
Evaluating joint effects of
induction-salvage treatment regimes on overall survival in acute leukemia. JRSS-C. 62:67-83, 2013.
Using joint utilities of the
times to response and toxicity to adaptively optimize schedule-dose regimes.
Biometrics 69:673-682, 2013.
Using data augmentation to
facilitate conduct of phase I/II clinical trials with delayed outcomes. J American Statistical Assoc. 109:525-536, 2014.
Optimizing sedative dose in preterm
infants undergoing treatment for respiratory distress syndrome. J American Statistical Assoc. 109:931-943, 2014.
Using effective sample size for prior
calibration in Bayesian phase I-II dose-finding. Clinical Trials. 11:657-666, 2014.
A modified adaptive Lasso for identifying
interactions in the Cox model with the heredity constraint. Statistics and
Probability Letters. 93:126-133, 2014.
Optimization of multi-stage dynamic
treatment regimes utilizing accumulated data. Statistics in Medicine,
SMART design, conduct,
and analysis in oncology. In Dynamic Treatment Regimes in Practice: Planning
Trials and Analyzing Data for Personalized Medicine. E Moodie and M. Kosorok (eds),
SIAM. In press.
Some caveats for outcome
adaptive randomization in clinical trials. In Modern Adaptive Randomized
Clinical Trials: Statistical, Operational, and Regulatory Aspects. A. Sverdlov (ed).
Taylor & Francis. 2015.
Bayesian dose-finding in two
treatment cycles based on the joint utility of efficacy and toxicity. J American Statistical Assoc. 110:711-722, 2015.
estimation of targeted agent effects on biomarker change to predict clinical
outcome. Biometrics. 71:188-197, 2015.
Comparing radiation modalities for
esophageal cancer based on total toxicity burden and disease-free survival. J
Royal Statistical Society, Series C (Applied Statistics) 65:273-297, 2016.
A Decision Theoretic Phase I-II
Design for Ordinal Outcomes in Two Cycles. Biostatistics 17:304-319, 2016.
Bayesian nonparametric estimation for
dynamic treatment regimes with sequential transition times J. American Statistical
Assoc. with discussion) 111:921-950, 2016.
for randomized comparative trials with discrete outcomes. Statistics in
Medicine. 35:4285-4305, 2016.
treatment comparison based on utilities of semi-competing risks in non-small-cell lung cancer. J American Statistical
Assoc. 112:11-23, 2017.
A decision-theoretic comparison of
treatments to resolve air leaks after lung surgery based on nonparametric
modeling. Bayesian Analysis. 12(3):639-652, 2017.
Parametric dose standardization
for two-agent combinations in a phase I-II trial with ordinal outcomes. J Royal Statistical Society, Series C
(Applied Statistics). 66:201-224, 2017.
A simulation study of methods for
selecting subgroup-specific doses in phase I trials. Pharmaceutical
Statistics. 16:143-156, 2017.
selection for a semi-competing risks model with three hazard functions. Computational Statistics and Data Analysis.
Clinical trial design as a
decision problem. Applied Stochastic Models in Business and Industry. (Invited, special issue in honor of Kathryn Chaloner) 33:296-301, 2017.
A simulation study of
outcome adaptive randomization in multi-arm clinical trials. Clinical Trials. 14:432-440, 2017.
nonparametric statistics: A new toolkit for
discovery in cancer research. Pharmaceutical Statistics. 16:414-423, 2017.
machine learning method for optimizing dynamic treatment regimes. Revised for J American Statistical Assoc In
Bayesian treatment comparison
using parametric mixture priors computed from elicited histograms Statistical Methods in Medical Research. In
A utility-based design for
randomized comparative trials with ordinal outcomes and prognostic subgroups. Biometrics. In press.
Optimizing natural killer cell
doses for heterogeneous cancer patients based on multiple event times. J Royal Statistical Society, Series C
(Applied Statistics). In press.