Dose-finding for Multi-drug Combinations
|
|
- Meredith Joseph
- 5 years ago
- Views:
Transcription
1 September 16, 2011
2 Outline Background Methods Results Conclusions
3 Multiple-agent Trials In trials combining more than one drug, monotonicity assumption may not hold for every dose The ordering between toxicity probabilities of some combinations is unknown Toxicity probabilities now follow a partial order
4 Partial Ordering of Doses Example: Phase I study of Samarium Lexidronam / Bortezomib combination therapy (Berenson et al., 2009) Drug Combination Agent d 1 d 2 d 3 d 4 d 5 d 6 Sm (mci/kg) Bortezomib (mg/m 2 )
5 Partial Ordering of Doses The following order relationships between treatments are known 1 d 1 d 2 d 3 d 6 2 d 1 d 4 d 5 d 6 3 d 2 d 5 Strategy: specify all possible orderings of doses consistent known with toxicity relationships.
6 Partial Ordering of Doses This trial requires the investigation of the following five simple orders 1 d 1 d 2 d 3 d 4 d 5 d 6 2 d 1 d 2 d 4 d 3 d 5 d 6 3 d 1 d 2 d 4 d 5 d 3 d 6 4 d 1 d 4 d 2 d 3 d 5 d 6 5 d 1 d 4 d 2 d 5 d 3 d 6 A random variable M indexes the set of possible simple orders
7 Toxicity Probability Model For a particular ordering, m, (m = 1,..., M), the true probability of toxicity is modeled via a class of working models R(x j ) = Pr(Y j = 1 X j = x j ) ψ m (x j, a) for x j {d 1..., d k }
8 Prior Information Let p (m) = {p (1),..., p (M)} denote a discrete prior over the set of contending models Let g(a) represent the prior on the parameter a
9 Likelihood Function Under ordering m, the likelihood of a is given by j j L m (a Ω j ) = y l log ψ m (x l, a) + (1 y l ) log(1 ψ m (x l, a)) l=1 l=1 given the data Ω j = {x 1, y 1,..., x j, y j } for the first j patients.
10 Model Selection The posterior probability of model m is given by p (m) L m (a Ω j )g(a)da A π(m Ω j ) = M p (m) L m (a Ω j )g(a)da m=1 Choose a single ordering, h, with the largest posterior model probability π(m Ω j ) A
11 Toxicity Probability Estimates Given h, toxicity probabilities estimates are given by ˆR(d i ) = ψ h (d i, â h ); i = 1,..., k The next patient is then allocated to the dose combination with the estimated toxicity probability closest to the target.
12 Illustration R(d 1 ) = 0.04, R(d 2 ) = 0.07, R(d 3 ) = 0.20, R(d 4 ) = 0.35, R(d 5 ) = 0.55 and R(d 6 ) = Target toxicity rate θ = The trial will treat n = 24 patients. For each ordering, we used the power model, ψ m (d i, a) = αmi a ; m = 1,..., 5; i = 1,..., 6
13 Working Models Table: Working model for five simple orders Combinations M Ordering m = m = m = m = m =
14 Illustration dose patient
15 Simulation Setup 3 different toxicity scenarios. Target toxicity rate θ = The trial will treat n = 24 patients. Tables present 1 percentage of MTD recommendation over 2000 simulated trials 2 percentage of patients that were treated at each combination
16 Results Dose d 1 d 2 d 3 d 4 d 5 d 6 %tox R(d i ) % Rec % Exp R(d i ) % Rec % Exp R(d i ) % Rec % Exp
17 Matrix Orders Sometimes, it may not be feasible to consider all possible orderings Example: Consider a trial investigating two agents, A and B. Suppose A has 4 dose levels and B has 4 dose levels. Therefore, a total of 16 drug combinations are under consideration
18 Matrix Orders Table: Drug combinations for 4 4 matrix order Doses of Doses of Drug B Drug A d 13 d 14 d 15 d 16 3 d 9 d 10 d 11 d 12 2 d 5 d 6 d 7 d 8 1 d 1 d 2 d 3 d 4
19 Strategy for Matrix Orders Assume that toxicity increases monotonically for each drug when the other drug is held fixed Use known ordering information to choose a proper subset of orderings Use toxicity zones as a guide for order selection
20 Strategy for Matrix Orders Figure: An illustration of zoning a drug combination matrix Drug B 4 d 13 d 14 d 15 d 16 3 d 9 d 10 d 11 d 12 2 d 5 d 6 d 7 d 8 1 d 1 d 2 d 3 d Drug A
21 3 Possible Orders m = 1 d 1 d 2 d 5 d 3 d 6 d 9 d 4 d 7 d 10 d 13 d 8 d 11 d 14 d 12 d 15 d 16 m = 2 d 1 d 5 d 2 d 3 d 6 d 9 d 13 d 10 d 7 d 4 d 8 d 11 d 14 d 15 d 12 d 16. m = 3 d 1 d 5 d 2 d 9 d 6 d 3 d 13 d 10 d 7 d 4 d 14 d 11 d 8 d 15 d 12 d 16.
22 Concluding Remarks Overall, the proposed design is competitive with existing methods for dose-finding in multi-agent trials When the true ordering is known, the design reduces to the CRM, making it compatible to single-agent trials. Therefore, it can be considered an extension of the CRM
23 Questions? Thank You!
Dose-finding for Multi-drug Combinations
Outline Background Methods Results Conclusions Multiple-agent Trials In trials combining more than one drug, monotonicity assumption may not hold for every dose The ordering between toxicity probabilities
More informationPhase I design for locating schedule-specific maximum tolerated doses
Phase I design for locating schedule-specific maximum tolerated doses Nolan A. Wages, Ph.D. University of Virginia Division of Translational Research & Applied Statistics Department of Public Health Sciences
More informationSensitivity study of dose-finding methods
to of dose-finding methods Sarah Zohar 1 John O Quigley 2 1. Inserm, UMRS 717,Biostatistic Department, Hôpital Saint-Louis, Paris, France 2. Inserm, Université Paris VI, Paris, France. NY 2009 1 / 21 to
More informationRandomized dose-escalation design for drug combination cancer trials with immunotherapy
Randomized dose-escalation design for drug combination cancer trials with immunotherapy Pavel Mozgunov, Thomas Jaki, Xavier Paoletti Medical and Pharmaceutical Statistics Research Unit, Department of Mathematics
More informationA Bayesian decision-theoretic approach to incorporating pre-clinical information into phase I clinical trials
A Bayesian decision-theoretic approach to incorporating pre-clinical information into phase I clinical trials Haiyan Zheng, Lisa V. Hampson Medical and Pharmaceutical Statistics Research Unit Department
More informationBayesian Optimal Interval Design for Phase I Clinical Trials
Bayesian Optimal Interval Design for Phase I Clinical Trials Department of Biostatistics The University of Texas, MD Anderson Cancer Center Joint work with Suyu Liu Phase I oncology trials The goal of
More informationA weighted differential entropy based approach for dose-escalation trials
A weighted differential entropy based approach for dose-escalation trials Pavel Mozgunov, Thomas Jaki Medical and Pharmaceutical Statistics Research Unit, Department of Mathematics and Statistics, Lancaster
More informationImproving a safety of the Continual Reassessment Method via a modified allocation rule
Improving a safety of the Continual Reassessment Method via a modified allocation rule Pavel Mozgunov, Thomas Jaki Medical and Pharmaceutical Statistics Research Unit, Department of Mathematics and Statistics,
More informationCOMPOSITIONAL IDEAS IN THE BAYESIAN ANALYSIS OF CATEGORICAL DATA WITH APPLICATION TO DOSE FINDING CLINICAL TRIALS
COMPOSITIONAL IDEAS IN THE BAYESIAN ANALYSIS OF CATEGORICAL DATA WITH APPLICATION TO DOSE FINDING CLINICAL TRIALS M. Gasparini and J. Eisele 2 Politecnico di Torino, Torino, Italy; mauro.gasparini@polito.it
More informationBayesian decision procedures for dose escalation - a re-analysis
Bayesian decision procedures for dose escalation - a re-analysis Maria R Thomas Queen Mary,University of London February 9 th, 2010 Overview Phase I Dose Escalation Trial Terminology Regression Models
More informationCancer phase I trial design using drug combinations when a fraction of dose limiting toxicities is attributable to one or more agents
Cancer phase I trial design using drug combinations when a fraction of dose limiting toxicities is attributable to one or more agents José L. Jiménez 1, Mourad Tighiouart 2, Mauro Gasparini 1 1 Politecnico
More informationPubh 8482: Sequential Analysis
Pubh 8482: Sequential Analysis Joseph S. Koopmeiners Division of Biostatistics University of Minnesota Week 10 Class Summary Last time... We began our discussion of adaptive clinical trials Specifically,
More informationBayesian designs of phase II oncology trials to select maximum effective dose assuming monotonic dose-response relationship
Guo and Li BMC Medical Research Methodology 2014, 14:95 RESEARCH ARTICLE Open Access Bayesian designs of phase II oncology trials to select maximum effective dose assuming monotonic dose-response relationship
More informationBAYESIAN DOSE FINDING IN PHASE I CLINICAL TRIALS BASED ON A NEW STATISTICAL FRAMEWORK
Statistica Sinica 17(2007), 531-547 BAYESIAN DOSE FINDING IN PHASE I CLINICAL TRIALS BASED ON A NEW STATISTICAL FRAMEWORK Y. Ji, Y. Li and G. Yin The University of Texas M. D. Anderson Cancer Center Abstract:
More informationWeighted differential entropy based approaches to dose-escalation in clinical trials
Weighted differential entropy based approaches to dose-escalation in clinical trials Pavel Mozgunov, Thomas Jaki Medical and Pharmaceutical Statistics Research Unit, Department of Mathematics and Statistics,
More informationarxiv: v1 [stat.me] 11 Feb 2014
A New Approach to Designing Phase I-II Cancer Trials for Cytotoxic Chemotherapies Jay Bartroff, Tze Leung Lai, and Balasubramanian Narasimhan arxiv:1402.2550v1 [stat.me] 11 Feb 2014 Abstract Recently there
More informationPayment Rules for Combinatorial Auctions via Structural Support Vector Machines
Payment Rules for Combinatorial Auctions via Structural Support Vector Machines Felix Fischer Harvard University joint work with Paul Dütting (EPFL), Petch Jirapinyo (Harvard), John Lai (Harvard), Ben
More informationGroup-Sequential Tests for One Proportion in a Fleming Design
Chapter 126 Group-Sequential Tests for One Proportion in a Fleming Design Introduction This procedure computes power and sample size for the single-arm group-sequential (multiple-stage) designs of Fleming
More informationA simulation study of methods for selecting subgroup-specific doses in phase 1 trials
Received: 6 April 2016 Revised: 20 September 2016 Accepted: 4 November 2016 DOI 10.1002/pst.1797 MAIN PAPER A simulation study of methods for selecting subgroup-specific doses in phase 1 trials Satoshi
More informationUse of frequentist and Bayesian approaches for extrapolating from adult efficacy data to design and interpret confirmatory trials in children
Use of frequentist and Bayesian approaches for extrapolating from adult efficacy data to design and interpret confirmatory trials in children Lisa Hampson, Franz Koenig and Martin Posch Department of Mathematics
More informationAn effective approach for obtaining optimal sampling windows for population pharmacokinetic experiments
An effective approach for obtaining optimal sampling windows for population pharmacokinetic experiments Kayode Ogungbenro and Leon Aarons Centre for Applied Pharmacokinetic Research School of Pharmacy
More informationBayesian Sequential Design under Model Uncertainty using Sequential Monte Carlo
Bayesian Sequential Design under Model Uncertainty using Sequential Monte Carlo, James McGree, Tony Pettitt October 7, 2 Introduction Motivation Model choice abundant throughout literature Take into account
More informationStatistics 300A, Homework 7. T (x) = C(b) =
Statistics 300A, Homework 7 Modified based on Brad Klingenberg s Solutions 3.34 (i Suppose that g is known, then the joint density of X,..., X n is given by ( [ n Γ(gb g (x x n g exp b which is (3.9 with
More information2.6.3 Generalized likelihood ratio tests
26 HYPOTHESIS TESTING 113 263 Generalized likelihood ratio tests When a UMP test does not exist, we usually use a generalized likelihood ratio test to verify H 0 : θ Θ against H 1 : θ Θ\Θ It can be used
More informationBayesian Social Learning with Random Decision Making in Sequential Systems
Bayesian Social Learning with Random Decision Making in Sequential Systems Yunlong Wang supervised by Petar M. Djurić Department of Electrical and Computer Engineering Stony Brook University Stony Brook,
More informationBayesian Econometrics
Bayesian Econometrics Christopher A. Sims Princeton University sims@princeton.edu September 20, 2016 Outline I. The difference between Bayesian and non-bayesian inference. II. Confidence sets and confidence
More informationPubH 7470: STATISTICS FOR TRANSLATIONAL & CLINICAL RESEARCH
PubH 7470: STATISTICS FOR TRANSLATIONAL & CLINICAL RESEARCH The First Step: SAMPLE SIZE DETERMINATION THE ULTIMATE GOAL The most important, ultimate step of any of clinical research is to do draw inferences;
More informationThe information complexity of sequential resource allocation
The information complexity of sequential resource allocation Emilie Kaufmann, joint work with Olivier Cappé, Aurélien Garivier and Shivaram Kalyanakrishan SMILE Seminar, ENS, June 8th, 205 Sequential allocation
More informationLecture 6: Communication Complexity of Auctions
Algorithmic Game Theory October 13, 2008 Lecture 6: Communication Complexity of Auctions Lecturer: Sébastien Lahaie Scribe: Rajat Dixit, Sébastien Lahaie In this lecture we examine the amount of communication
More informationHierarchical Models & Bayesian Model Selection
Hierarchical Models & Bayesian Model Selection Geoffrey Roeder Departments of Computer Science and Statistics University of British Columbia Jan. 20, 2016 Contact information Please report any typos or
More informationSequential Monte Carlo Algorithms for Bayesian Sequential Design
Sequential Monte Carlo Algorithms for Bayesian Sequential Design Dr Queensland University of Technology c.drovandi@qut.edu.au Collaborators: James McGree, Tony Pettitt, Gentry White Acknowledgements: Australian
More informationAdaptive designs beyond p-value combination methods. Ekkehard Glimm, Novartis Pharma EAST user group meeting Basel, 31 May 2013
Adaptive designs beyond p-value combination methods Ekkehard Glimm, Novartis Pharma EAST user group meeting Basel, 31 May 2013 Outline Introduction Combination-p-value method and conditional error function
More informationGrundlagen der Künstlichen Intelligenz
Grundlagen der Künstlichen Intelligenz Uncertainty & Probabilities & Bandits Daniel Hennes 16.11.2017 (WS 2017/18) University Stuttgart - IPVS - Machine Learning & Robotics 1 Today Uncertainty Probability
More informationClassification for High Dimensional Problems Using Bayesian Neural Networks and Dirichlet Diffusion Trees
Classification for High Dimensional Problems Using Bayesian Neural Networks and Dirichlet Diffusion Trees Rafdord M. Neal and Jianguo Zhang Presented by Jiwen Li Feb 2, 2006 Outline Bayesian view of feature
More informationADAPTIVE EXPERIMENTAL DESIGNS. Maciej Patan and Barbara Bogacka. University of Zielona Góra, Poland and Queen Mary, University of London
ADAPTIVE EXPERIMENTAL DESIGNS FOR SIMULTANEOUS PK AND DOSE-SELECTION STUDIES IN PHASE I CLINICAL TRIALS Maciej Patan and Barbara Bogacka University of Zielona Góra, Poland and Queen Mary, University of
More informationBayesian Updating: Discrete Priors: Spring
Bayesian Updating: Discrete Priors: 18.05 Spring 2017 http://xkcd.com/1236/ Learning from experience Which treatment would you choose? 1. Treatment 1: cured 100% of patients in a trial. 2. Treatment 2:
More informationA Sampling of IMPACT Research:
A Sampling of IMPACT Research: Methods for Analysis with Dropout and Identifying Optimal Treatment Regimes Marie Davidian Department of Statistics North Carolina State University http://www.stat.ncsu.edu/
More informationPrior Effective Sample Size in Conditionally Independent Hierarchical Models
Bayesian Analysis (01) 7, Number 3, pp. 591 614 Prior Effective Sample Size in Conditionally Independent Hierarchical Models Satoshi Morita, Peter F. Thall and Peter Müller Abstract. Prior effective sample
More informationEstimating A Static Game of Traveling Doctors
Estimating A Static Game of Traveling Doctors J. Jason Bell, Thomas Gruca, Sanghak Lee, and Roger Tracy January 26, 2015 Visiting Consultant Clinicians A Visiting Consultant Clinician, or a VCC is a traveling
More informationOn the efficiency of two-stage adaptive designs
On the efficiency of two-stage adaptive designs Björn Bornkamp (Novartis Pharma AG) Based on: Dette, H., Bornkamp, B. and Bretz F. (2010): On the efficiency of adaptive designs www.statistik.tu-dortmund.de/sfb823-dp2010.html
More informationComparing Adaptive Designs and the. Classical Group Sequential Approach. to Clinical Trial Design
Comparing Adaptive Designs and the Classical Group Sequential Approach to Clinical Trial Design Christopher Jennison Department of Mathematical Sciences, University of Bath, UK http://people.bath.ac.uk/mascj
More information6 Sample Size Calculations
6 Sample Size Calculations A major responsibility of a statistician: sample size calculation. Hypothesis Testing: compare treatment 1 (new treatment) to treatment 2 (standard treatment); Assume continuous
More informationUsing Data Augmentation to Facilitate Conduct of Phase I-II Clinical Trials with Delayed Outcomes
Using Data Augmentation to Facilitate Conduct of Phase I-II Clinical Trials with Delayed Outcomes Ick Hoon Jin, Suyu Liu, Peter F. Thall, and Ying Yuan October 17, 2013 Abstract A practical impediment
More informationThe information complexity of best-arm identification
The information complexity of best-arm identification Emilie Kaufmann, joint work with Olivier Cappé and Aurélien Garivier MAB workshop, Lancaster, January th, 206 Context: the multi-armed bandit model
More informationIndividualized Treatment Effects with Censored Data via Nonparametric Accelerated Failure Time Models
Individualized Treatment Effects with Censored Data via Nonparametric Accelerated Failure Time Models Nicholas C. Henderson Thomas A. Louis Gary Rosner Ravi Varadhan Johns Hopkins University July 31, 2018
More informationChapter 9: Hypothesis Testing Sections
Chapter 9: Hypothesis Testing Sections 9.1 Problems of Testing Hypotheses 9.2 Testing Simple Hypotheses 9.3 Uniformly Most Powerful Tests Skip: 9.4 Two-Sided Alternatives 9.6 Comparing the Means of Two
More informationThe L-Shaped Method. Operations Research. Anthony Papavasiliou 1 / 44
1 / 44 The L-Shaped Method Operations Research Anthony Papavasiliou Contents 2 / 44 1 The L-Shaped Method [ 5.1 of BL] 2 Optimality Cuts [ 5.1a of BL] 3 Feasibility Cuts [ 5.1b of BL] 4 Proof of Convergence
More informationDEPARTMENT OF COMPUTER SCIENCE Autumn Semester MACHINE LEARNING AND ADAPTIVE INTELLIGENCE
Data Provided: None DEPARTMENT OF COMPUTER SCIENCE Autumn Semester 203 204 MACHINE LEARNING AND ADAPTIVE INTELLIGENCE 2 hours Answer THREE of the four questions. All questions carry equal weight. Figures
More informationBayesian Updating: Discrete Priors: Spring 2014
ian Updating: Discrete Priors: 18.05 Spring 2014 http://xkcd.com/1236/ January 1, 2017 1 / 16 Learning from experience Which treatment would you choose? 1. Treatment 1: cured 100% of patients in a trial.
More informationA Bayesian Stopping Rule for a Single Arm Study: with a Case Study of Stem Cell Transplantation
A Bayesian Stopping Rule for a Single Arm Study: with a Case Study of Stem Cell Transplantation Chao-Yin Chen Kathryn Chaloner University of Minnesota University of Iowa May-15-2002 1 A Case Study: MT9928
More informationRobustifying Trial-Derived Treatment Rules to a Target Population
1/ 39 Robustifying Trial-Derived Treatment Rules to a Target Population Yingqi Zhao Public Health Sciences Division Fred Hutchinson Cancer Research Center Workshop on Perspectives and Analysis for Personalized
More informationUsing Joint Utilities of the Times to Response and Toxicity to Adaptively Optimize Schedule Dose Regimes
Biometrics 69, 673 682 September 2013 DOI: 10.1111/biom.12065 Using Joint Utilities of the Times to Response and Toxicity to Adaptively Optimize Schedule Dose Regimes Peter F. Thall, 1, * Hoang Q. Nguyen,
More informationBayesian Updating: Discrete Priors: Spring
Bayesian Updating: Discrete Priors: 18.05 Spring 2017 http://xkcd.com/1236/ Learning from experience Which treatment would you choose? 1. Treatment 1: cured 100% of patients in a trial. 2. Treatment 2:
More informationMULTIPLE-OBJECTIVE DESIGNS IN A DOSE-RESPONSE EXPERIMENT
New Developments and Applications in Experimental Design IMS Lecture Notes - Monograph Series (1998) Volume 34 MULTIPLE-OBJECTIVE DESIGNS IN A DOSE-RESPONSE EXPERIMENT BY WEI ZHU AND WENG KEE WONG 1 State
More informationContre-examples for Bayesian MAP restoration. Mila Nikolova
Contre-examples for Bayesian MAP restoration Mila Nikolova CMLA ENS de Cachan, 61 av. du Président Wilson, 94235 Cachan cedex (nikolova@cmla.ens-cachan.fr) Obergurgl, September 26 Outline 1. MAP estimators
More informationSample Size Determination
Sample Size Determination 018 The number of subjects in a clinical study should always be large enough to provide a reliable answer to the question(s addressed. The sample size is usually determined by
More informationThe Jeffreys Prior. Yingbo Li MATH Clemson University. Yingbo Li (Clemson) The Jeffreys Prior MATH / 13
The Jeffreys Prior Yingbo Li Clemson University MATH 9810 Yingbo Li (Clemson) The Jeffreys Prior MATH 9810 1 / 13 Sir Harold Jeffreys English mathematician, statistician, geophysicist, and astronomer His
More information(3) Review of Probability. ST440/540: Applied Bayesian Statistics
Review of probability The crux of Bayesian statistics is to compute the posterior distribution, i.e., the uncertainty distribution of the parameters (θ) after observing the data (Y) This is the conditional
More informationCompetitive Equilibria in a Comonotone Market
Competitive Equilibria in a Comonotone Market 1/51 Competitive Equilibria in a Comonotone Market Ruodu Wang http://sas.uwaterloo.ca/ wang Department of Statistics and Actuarial Science University of Waterloo
More informationBayes methods for categorical data. April 25, 2017
Bayes methods for categorical data April 25, 2017 Motivation for joint probability models Increasing interest in high-dimensional data in broad applications Focus may be on prediction, variable selection,
More informationSample size re-estimation in clinical trials. Dealing with those unknowns. Chris Jennison. University of Kyoto, January 2018
Sample Size Re-estimation in Clinical Trials: Dealing with those unknowns Christopher Jennison Department of Mathematical Sciences, University of Bath, UK http://people.bath.ac.uk/mascj University of Kyoto,
More informationTopic 12 Overview of Estimation
Topic 12 Overview of Estimation Classical Statistics 1 / 9 Outline Introduction Parameter Estimation Classical Statistics Densities and Likelihoods 2 / 9 Introduction In the simplest possible terms, the
More informationUniformly Most Powerful Bayesian Tests and Standards for Statistical Evidence
Uniformly Most Powerful Bayesian Tests and Standards for Statistical Evidence Valen E. Johnson Texas A&M University February 27, 2014 Valen E. Johnson Texas A&M University Uniformly most powerful Bayes
More informationDESIGN EVALUATION AND OPTIMISATION IN CROSSOVER PHARMACOKINETIC STUDIES ANALYSED BY NONLINEAR MIXED EFFECTS MODELS
DESIGN EVALUATION AND OPTIMISATION IN CROSSOVER PHARMACOKINETIC STUDIES ANALYSED BY NONLINEAR MIXED EFFECTS MODELS Thu Thuy Nguyen, Caroline Bazzoli, France Mentré UMR 738 INSERM - University Paris Diderot,
More informationAdaptive Designs: Why, How and When?
Adaptive Designs: Why, How and When? Christopher Jennison Department of Mathematical Sciences, University of Bath, UK http://people.bath.ac.uk/mascj ISBS Conference Shanghai, July 2008 1 Adaptive designs:
More informationBayes Factor Single Arm Time-to-event User s Guide (Version 1.0.0)
Bayes Factor Single Arm Time-to-event User s Guide (Version 1.0.0) Department of Biostatistics P. O. Box 301402, Unit 1409 The University of Texas, M. D. Anderson Cancer Center Houston, Texas 77230-1402,
More informationResults of a simulation of modeling and nonparametric methodology for count data (from patient diary) in drug studies
Results of a simulation of modeling and nonparametric methodology for count data (from patient diary) in drug studies Erhard Quebe-Fehling Workshop Stat. Methoden für korrelierte Daten Bochum 24-Nov-06
More informationACCOUNTING FOR INPUT-MODEL AND INPUT-PARAMETER UNCERTAINTIES IN SIMULATION. <www.ie.ncsu.edu/jwilson> May 22, 2006
ACCOUNTING FOR INPUT-MODEL AND INPUT-PARAMETER UNCERTAINTIES IN SIMULATION Slide 1 Faker Zouaoui Sabre Holdings James R. Wilson NC State University May, 006 Slide From American
More informationEstimation MLE-Pandemic data MLE-Financial crisis data Evaluating estimators. Estimation. September 24, STAT 151 Class 6 Slide 1
Estimation September 24, 2018 STAT 151 Class 6 Slide 1 Pandemic data Treatment outcome, X, from n = 100 patients in a pandemic: 1 = recovered and 0 = not recovered 1 1 1 0 0 0 1 1 1 0 0 1 0 1 0 0 1 1 1
More informationWhat is Experimental Design?
One Factor ANOVA What is Experimental Design? A designed experiment is a test in which purposeful changes are made to the input variables (x) so that we may observe and identify the reasons for change
More informationHypothesis Test. The opposite of the null hypothesis, called an alternative hypothesis, becomes
Neyman-Pearson paradigm. Suppose that a researcher is interested in whether the new drug works. The process of determining whether the outcome of the experiment points to yes or no is called hypothesis
More informationUsing Historical Experimental Information in the Bayesian Analysis of Reproduction Toxicological Experimental Results
Using Historical Experimental Information in the Bayesian Analysis of Reproduction Toxicological Experimental Results Jing Zhang Miami University August 12, 2014 Jing Zhang (Miami University) Using Historical
More informationF. Combes (1,2,3) S. Retout (2), N. Frey (2) and F. Mentré (1) PODE 2012
Prediction of shrinkage of individual parameters using the Bayesian information matrix in nonlinear mixed-effect models with application in pharmacokinetics F. Combes (1,2,3) S. Retout (2), N. Frey (2)
More informationMoral Hazard: Characterization of SB
Moral Hazard: Characterization of SB Ram Singh Department of Economics March 2, 2015 Ram Singh (Delhi School of Economics) Moral Hazard March 2, 2015 1 / 19 Characterization of Second Best Contracts I
More informationLearning Mixtures of Truncated Basis Functions from Data
Learning Mixtures of Truncated Basis Functions from Data Helge Langseth, Thomas D. Nielsen, and Antonio Salmerón PGM This work is supported by an Abel grant from Iceland, Liechtenstein, and Norway through
More informationPubH 7405: REGRESSION ANALYSIS INTRODUCTION TO LOGISTIC REGRESSION
PubH 745: REGRESSION ANALYSIS INTRODUCTION TO LOGISTIC REGRESSION Let Y be the Dependent Variable Y taking on values and, and: π Pr(Y) Y is said to have the Bernouilli distribution (Binomial with n ).
More informationOnline Learning and Sequential Decision Making
Online Learning and Sequential Decision Making Emilie Kaufmann CNRS & CRIStAL, Inria SequeL, emilie.kaufmann@univ-lille.fr Research School, ENS Lyon, Novembre 12-13th 2018 Emilie Kaufmann Sequential Decision
More informationCase Study in the Use of Bayesian Hierarchical Modeling and Simulation for Design and Analysis of a Clinical Trial
Case Study in the Use of Bayesian Hierarchical Modeling and Simulation for Design and Analysis of a Clinical Trial William R. Gillespie Pharsight Corporation Cary, North Carolina, USA PAGE 2003 Verona,
More informationMultiple Testing in Group Sequential Clinical Trials
Multiple Testing in Group Sequential Clinical Trials Tian Zhao Supervisor: Michael Baron Department of Mathematical Sciences University of Texas at Dallas txz122@utdallas.edu 7/2/213 1 Sequential statistics
More informationOptimizing Combination Therapy under a Bivariate Weibull Distribution, with Application to Toxicity and Efficacy Responses
Optimizing Combination Therapy under a Bivariate Weibull Distribution, with Application to Toxicity and Efficacy Responses Kim, Sungwook (Peter) University of Missouri Issac Newton Institute for Mathematical
More informationComputer Intensive Methods in Mathematical Statistics
Computer Intensive Methods in Mathematical Statistics Department of mathematics johawes@kth.se Lecture 16 Advanced topics in computational statistics 18 May 2017 Computer Intensive Methods (1) Plan of
More informationNoninformative Priors for the Ratio of the Scale Parameters in the Inverted Exponential Distributions
Communications for Statistical Applications and Methods 03, Vol. 0, No. 5, 387 394 DOI: http://dx.doi.org/0.535/csam.03.0.5.387 Noninformative Priors for the Ratio of the Scale Parameters in the Inverted
More informationBandits, Experts, and Games
Bandits, Experts, and Games CMSC 858G Fall 2016 University of Maryland Intro to Probability* Alex Slivkins Microsoft Research NYC * Many of the slides adopted from Ron Jin and Mohammad Hajiaghayi Outline
More information4 Lecture Applications
4 Lecture 4 4.1 Applications We now will look at some of the applications of the convex analysis we have learned. First, we shall us a separation theorem to prove the second fundamental theorem of welfare
More informationOptimal Adaptive Designs for Dose Finding in Early Phase Clinical Trials
Optimal Adaptive Designs for Dose Finding in Early Phase Clinical Trials Alam, Muhammad Iftakhar The copyright of this thesis rests with the author and no quotation from it or information derived from
More informationBAYESIAN ANALYSIS OF DOSE-RESPONSE CALIBRATION CURVES
Libraries Annual Conference on Applied Statistics in Agriculture 2005-17th Annual Conference Proceedings BAYESIAN ANALYSIS OF DOSE-RESPONSE CALIBRATION CURVES William J. Price Bahman Shafii Follow this
More informationThe Pierre Auger Observatory: on the arrival directions of the most energetic cosmic rays
The Pierre Auger Observatory: on the arrival directions of the most energetic cosmic rays Piera L. Ghia*, for the Pierre Auger Collaboration * IFSI/INAF Torino, Italy, & IPN/CNRS Orsay, France Outline
More informationStratégies bayésiennes et fréquentistes dans un modèle de bandit
Stratégies bayésiennes et fréquentistes dans un modèle de bandit thèse effectuée à Telecom ParisTech, co-dirigée par Olivier Cappé, Aurélien Garivier et Rémi Munos Journées MAS, Grenoble, 30 août 2016
More informationCompetition relative to Incentive Functions in Common Agency
Competition relative to Incentive Functions in Common Agency Seungjin Han May 20, 2011 Abstract In common agency problems, competing principals often incentivize a privately-informed agent s action choice
More informationCorrelated Equilibrium in Games with Incomplete Information
Correlated Equilibrium in Games with Incomplete Information Dirk Bergemann and Stephen Morris Econometric Society Summer Meeting June 2012 Robust Predictions Agenda game theoretic predictions are very
More informationInfinitely Imbalanced Logistic Regression
p. 1/1 Infinitely Imbalanced Logistic Regression Art B. Owen Journal of Machine Learning Research, April 2007 Presenter: Ivo D. Shterev p. 2/1 Outline Motivation Introduction Numerical Examples Notation
More informationBayesian Drug Disease Model with Stan Using published longitudinal data summaries in population models
Bayesian Drug Disease Model with Stan Using published longitudinal data summaries in population models S. Weber 1, B. Carpenter 2, D. Lee 2, F. Y. Bois 3, A. Gelman 2, A. Racine 1 (1) Novartis, Basel;
More informationBayesian Methods and Uncertainty Quantification for Nonlinear Inverse Problems
Bayesian Methods and Uncertainty Quantification for Nonlinear Inverse Problems John Bardsley, University of Montana Collaborators: H. Haario, J. Kaipio, M. Laine, Y. Marzouk, A. Seppänen, A. Solonen, Z.
More informationClassical and Bayesian inference
Classical and Bayesian inference AMS 132 Claudia Wehrhahn (UCSC) Classical and Bayesian inference January 8 1 / 11 The Prior Distribution Definition Suppose that one has a statistical model with parameter
More informationCollection of Biostatistics Research Archive
Collection of Biostatistics Research Archive COBRA Preprint Series Year 2016 Paper 116 Semi-Parametrics Dose Finding Methods Matthieu Clertant John O Quigley Cedar Sinai Medical Center, Cancer Center,
More informationIntegral representations in models with long memory
Integral representations in models with long memory Georgiy Shevchenko, Yuliya Mishura, Esko Valkeila, Lauri Viitasaari, Taras Shalaiko Taras Shevchenko National University of Kyiv 29 September 215, Ulm
More informationMasters Comprehensive Examination Department of Statistics, University of Florida
Masters Comprehensive Examination Department of Statistics, University of Florida May 6, 003, 8:00 am - :00 noon Instructions: You have four hours to answer questions in this examination You must show
More informationMoral Hazard: Hidden Action
Moral Hazard: Hidden Action Part of these Notes were taken (almost literally) from Rasmusen, 2007 UIB Course 2013-14 (UIB) MH-Hidden Actions Course 2013-14 1 / 29 A Principal-agent Model. The Production
More informationTwo-stage Adaptive Randomization for Delayed Response in Clinical Trials
Two-stage Adaptive Randomization for Delayed Response in Clinical Trials Guosheng Yin Department of Statistics and Actuarial Science The University of Hong Kong Joint work with J. Xu PSI and RSS Journal
More informationSTAT J535: Chapter 5: Classes of Bayesian Priors
STAT J535: Chapter 5: Classes of Bayesian Priors David B. Hitchcock E-Mail: hitchcock@stat.sc.edu Spring 2012 The Bayesian Prior A prior distribution must be specified in a Bayesian analysis. The choice
More information