Comprehensive evaluation of CRM, mtpi, and 3+3 relative to a benchmark
|
|
|
- Calvin Arron Garrett
- 5 years ago
- Views:
Transcription
1 Comprehensive evaluation of CRM, mtpi, and 3+3 relative to a benchmark Bethany Jablonski Horton, Ph.D. University of Virginia April 16, 2015 Bethany Jablonski Horton, Ph.D. (UVA) Early Phase Dose Finding Symposium April 16, / 22
2 Outline of Talk Methods compared Summary estimates used to compare methods Simulation study Bethany Jablonski Horton, Ph.D. (UVA) Early Phase Dose Finding Symposium April 16, / 22
3 Methods compared Methods compared Continual reassessment method (CRM) Modified toxicity probability interval (mtpi) 3+3 Benchmark Bethany Jablonski Horton, Ph.D. (UVA) Early Phase Dose Finding Symposium April 16, / 22
4 Methods compared CRM 2-stage design Stage 1 Cohort size of 1 Increase until heterogeneity is observed responses Stage 2 dfcrm package used to make dose recommendations specifying a power model to model DLT probabilities Skeleton generated with the getprior function from dfcrm getprior(0.06,0.2,2,# of dose levels) Trial terminated if at dose 1 and the lower bound of 90% confidence interval of the probability of toxicity is greater than the target toxicity Bethany Jablonski Horton, Ph.D. (UVA) Early Phase Dose Finding Symposium April 16, / 22
5 Methods compared mtpi Decisions are based on an equivalence interval [pt ɛ 1, p T + ɛ 2 ] Safety stopping measures Terminate early if probability of toxicity, given data, too high Exclude doses in future if probability of toxicity too high Specifying a cohort size of 3 and ɛ1 = ɛ 2 = 0.05 Next dose chosen by maximum unit probability mass (UPM) Bethany Jablonski Horton, Ph.D. (UVA) Early Phase Dose Finding Symposium April 16, / 22
6 Methods compared mtpi Posterior probability distribution Ji, Liu, Li, and Bekele (2010) Bethany Jablonski Horton, Ph.D. (UVA) Early Phase Dose Finding Symposium April 16, / 22
7 Methods compared Benchmark Suppose we know each patient s toxicity tolerance tolerance generated from uniform (0,1) distribution For a specified toxicity curve, know when each patient would have a DLT Example: 4 dose levels Toxicity curve: (0.1, 0.19, 0.30, 0.39) O Quigley, Paoletti, and Maccario (2002) Bethany Jablonski Horton, Ph.D. (UVA) Early Phase Dose Finding Symposium April 16, / 22
8 Methods compared Benchmark True toxicity curve: (0.1, 0.19, 0.30, 0.39) Patient Tolerance Toxicity at dose level i Est. Tox. Rate Bethany Jablonski Horton, Ph.D. (UVA) Early Phase Dose Finding Symposium April 16, / 22
9 Methods compared Benchmark True toxicity curve: (0.1, 0.19, 0.30, 0.39) Patient Tolerance Toxicity at dose level i Est. Tox. Rate Bethany Jablonski Horton, Ph.D. (UVA) Early Phase Dose Finding Symposium April 16, / 22
10 Summary estimates Summary estimates used to compare methods Percent correct selection (PCS) Accuracy index Other basic statistics: Proportion of toxicities observed Average number of subjects allocated to a dose Bethany Jablonski Horton, Ph.D. (UVA) Early Phase Dose Finding Symposium April 16, / 22
11 Summary estimates Percent correct selection (PCS) The probability of selecting the true MTD Percent of dose selection within 5% and 10% of the target toxicity rate Bethany Jablonski Horton, Ph.D. (UVA) Early Phase Dose Finding Symposium April 16, / 22
12 Summary estimates Accuracy Index For dose selection: A N = 1 K K k=1 ρ k Probability of selecting dose k K k=1 ρ k K is the number of doses ρ k = p k θ, where p k is the true toxicity probability at dose k and θ is the target toxicity Also used for subject allocation Cheung (2011) Bethany Jablonski Horton, Ph.D. (UVA) Early Phase Dose Finding Symposium April 16, / 22
13 Simulations Dose curve selection 10,000 curves were simulated using order statistics from a Beta distribution (8 dose levels) K-means clustering used to partition these curves into 12 groups Curves within groups were averaged to create the 12 curves used in the simulations Bethany Jablonski Horton, Ph.D. (UVA) Early Phase Dose Finding Symposium April 16, / 22
14 Simulations Simulation setup 2,000 simulations Target toxicity rate θ = 0.2 Varying number of dose levels 3 dose levels n=18 4 dose levels n=24 6 dose levels n=36 8 dose levels n=48 dfcrm package in R used for CRM used for mtpi Bethany Jablonski Horton, Ph.D. (UVA) Early Phase Dose Finding Symposium April 16, / 22
15 Simulations 3 Dose levels Bethany Jablonski Horton, Ph.D. (UVA) Early Phase Dose Finding Symposium April 16, / 22
16 Simulations 4 Dose levels Bethany Jablonski Horton, Ph.D. (UVA) Early Phase Dose Finding Symposium April 16, / 22
17 Simulations 6 Dose levels Bethany Jablonski Horton, Ph.D. (UVA) Early Phase Dose Finding Symposium April 16, / 22
18 Simulations 8 Dose levels Bethany Jablonski Horton, Ph.D. (UVA) Early Phase Dose Finding Symposium April 16, / 22
19 Simulations 3 Dose levels Bethany Jablonski Horton, Ph.D. (UVA) Early Phase Dose Finding Symposium April 16, / 22
20 Simulations 4 Dose levels Bethany Jablonski Horton, Ph.D. (UVA) Early Phase Dose Finding Symposium April 16, / 22
21 Simulations 6 Dose levels Bethany Jablonski Horton, Ph.D. (UVA) Early Phase Dose Finding Symposium April 16, / 22
22 Simulations 8 Dose levels Bethany Jablonski Horton, Ph.D. (UVA) Early Phase Dose Finding Symposium April 16, / 22
A web application for conducting the continual reassessment method
A web application for conducting the continual reassessment method Nolan A. Wages, PhD Biostatistics Shared Resource University of Virginia Cancer Center March 3, 2017 Dose-finding Setting Initial Safety
Bayesian Dose Escalation Study Design with Consideration of Late Onset Toxicity. Li Liu, Glen Laird, Lei Gao Biostatistics Sanofi
Bayesian Dose Escalation Study Design with Consideration of Late Onset Toxicity Li Liu, Glen Laird, Lei Gao Biostatistics Sanofi 1 Outline Introduction Methods EWOC EWOC-PH Modifications to account for
Designs for Partially-Ordered Phase I Trials : Combinations of Agents and Groups of Patients. Mark Conaway University of Virginia
Designs for Partially-Ordered Phase I Trials : Combinations of Agents and Groups of Patients Mark Conaway University of Virginia 1 Novel designs for new types of studies LoRusso, Boerner and Seymour (2010)
A modified Hierarchical Continuous Reassessment Method (CRM) applied to a first-in-man study.
A modified Hierarchical Continuous Reassessment Method (CRM) applied to a first-in-man study. BAYES2010 20 May 2010 Anne Benoit Tom Jacobs Dominic, age 8, living with epilepsy Outline 2! First in Human
Subgroup-specific dose finding in phase I clinical trials based on time to toxicity allowing adaptive subgroup combination
Received: 15 March 2018 Revised: 24 May 2018 Accepted: 24 June 2018 DOI: 10.1002/pst.1891 MAIN PAPER Subgroup-specific dose finding in phase I clinical trials based on time to toxicity allowing adaptive
Combination dose finding studies in oncology: an industry perspective
Combination dose finding studies in oncology: an industry perspective Jian Zhu, Ling Wang Symposium on Dose Selection for Cancer Treatment Drugs Stanford, May 12 th 2017 Outline Overview Practical considerations
Endpoints in Gynecologic Cancer Clinical Trials
Endpoints in Gynecologic Cancer Clinical Trials Keiichi Fujiwara Saitama Medical University International Medical Center GOTIC AGENDA Definition of Endpoint Variation of Endpoint Primary vs Secondary Direct
Workshop # 3 : Application of Bayesian Statistics in early development studies
Workshop # 3 : Application of Bayesian Statistics in early development studies Francois Vandenhende, ClinBAY Patricia Sanwald Ducray, Roche Joint Conference of European Human Pharmacological Societies
Maria-Athina Altzerinakou1, Xavier Paoletti2. 9 May, 2017
An adaptive design for the identification of the optimal dose using joint modelling of efficacy and toxicity in phase I/II clinical trials of molecularly targeted agents Maria-Athina Altzerinakou1, Xavier
Package TEQR. R topics documented: February 5, Version Date Title Target Equivalence Range Design Author M.
Package TEQR Version 6.0-0 Date 2016-02-02 Title Target Equivalence Range Design Author M. Suzette Blanchard February 5, 2016 Depends Maintainer M. Suzette Blanchard The TEQR package
ENROLLMENT AND STOPPING RULES FOR MANAGING TOXICITY IN PHASE II ONCOLOGY TRIALS WITH DELAYED OUTCOME
ENROLLMENT AND STOPPING RULES FOR MANAGING TOXICITY IN PHASE II ONCOLOGY TRIALS WITH DELAYED OUTCOME Guochen Song A dissertation submitted to the faculty of the University of North Carolina at Chapel Hill
PKPD modelling to optimize dose-escalation trials in Oncology
PKPD modelling to optimize dose-escalation trials in Oncology Marina Savelieva Design of Experiments in Healthcare, Issac Newton Institute for Mathematical Sciences Aug 19th, 2011 Outline Motivation Phase
A Comparison of Methods for Determining HIV Viral Set Point
STATISTICS IN MEDICINE Statist. Med. 2006; 00:1 6 [Version: 2002/09/18 v1.11] A Comparison of Methods for Determining HIV Viral Set Point Y. Mei 1, L. Wang 2, S. E. Holte 2 1 School of Industrial and Systems
Sample Size Considerations. Todd Alonzo, PhD
Sample Size Considerations Todd Alonzo, PhD 1 Thanks to Nancy Obuchowski for the original version of this presentation. 2 Why do Sample Size Calculations? 1. To minimize the risk of making the wrong conclusion
Small-area estimation of mental illness prevalence for schools
Small-area estimation of mental illness prevalence for schools Fan Li 1 Alan Zaslavsky 2 1 Department of Statistical Science Duke University 2 Department of Health Care Policy Harvard Medical School March
Using mixture priors for robust inference: application in Bayesian dose escalation trials
Using mixture priors for robust inference: application in Bayesian dose escalation trials Astrid Jullion, Beat Neuenschwander, Daniel Lorand BAYES2014, London, 11 June 2014 Agenda Dose escalation in oncology
MATH : Design and Analysis of Clinical Trials
MATH 654-102: Design and Analysis of Clinical Trials MID-TERM EXAM Spring, 2012 (Time allowed: TWO AND HALF HOURS) INSTRUCTIONS TO STUDENTS: 1. This test contains FIVE questions and comprises SIX printed
Speaker: Clement Ma, PhD, Dana-Farber/Boston Children s Cancer and Blood Disorders Center, Harvard Medical School
October 24 2017 Speaker: Clement Ma, PhD, Dana-Farber/Boston Children s Cancer and Blood Disorders Center, Harvard Medical School Dr. Clement Ma is Lead Biostatistician at Dana-Farber/Boston Children s
Multi-Stage Stratified Sampling for the Design of Large Scale Biometric Systems
Multi-Stage Stratified Sampling for the Design of Large Scale Biometric Systems Jad Ramadan, Mark Culp, Ken Ryan, Bojan Cukic West Virginia University 1 Problem How to create a set of biometric samples
Case Studies in Bayesian Augmented Control Design. Nathan Enas Ji Lin Eli Lilly and Company
Case Studies in Bayesian Augmented Control Design Nathan Enas Ji Lin Eli Lilly and Company Outline Drivers for innovation in Phase II designs Case Study #1 Pancreatic cancer Study design Analysis Learning
Equilibrium Selection In Coordination Games
Equilibrium Selection In Coordination Games Presenter: Yijia Zhao (yz4k@virginia.edu) September 7, 2005 Overview of Coordination Games A class of symmetric, simultaneous move, complete information games
Drug Supply for Adaptive Trials
Drug Supply for Adaptive Trials Nitin R Patel Co-founder and Chairman, Cytel Inc. Outline Challenge of planning drug supply for adaptive trials Combining statistical design simulation with drug supply
PHASE 1 CLINICAL TRIALS: DESIGNS AND CONSIDERATIONS. Sarit Assouline, MD, MSc, FRCPC Associate professor, McGill University Jewish General Hospital
PHASE 1 CLINICAL TRIALS: DESIGNS AND CONSIDERATIONS Sarit Assouline, MD, MSc, FRCPC Associate professor, McGill University Jewish General Hospital Outline Introduction Goal of Phase I studies in oncology
Individualized Treatment Effects Using a Non-parametric Bayesian Approach
Individualized Treatment Effects Using a Non-parametric Bayesian Approach Ravi Varadhan Nicholas C. Henderson Division of Biostatistics & Bioinformatics Department of Oncology Johns Hopkins University
T-Statistic-based Up&Down Design for Dose-Finding Competes Favorably with Bayesian 4-parameter Logistic Design
T-Statistic-based Up&Down Design for Dose-Finding Competes Favorably with Bayesian 4-parameter Logistic Design James A. Bolognese, Cytel Nitin Patel, Cytel Yevgen Tymofyeyef, Merck Inna Perevozskaya, Wyeth
Bayesian Bi-Cluster Change-Point Model for Exploring Functional Brain Dynamics
Int'l Conf. Bioinformatics and Computational Biology BIOCOMP'18 85 Bayesian Bi-Cluster Change-Point Model for Exploring Functional Brain Dynamics Bing Liu 1*, Xuan Guo 2, and Jing Zhang 1** 1 Department
The University of Texas MD Anderson Cancer Center Division of Quantitative Sciences Department of Biostatistics. CRM Suite. User s Guide Version 1.0.
The University of Texas MD Anderson Cancer Center Division of Quantitative Sciences Department of Biostatistics CRM Suite User s Guide Version 1.0.0 Clift Norris, John Venier, Ying Yuan, and Lin Zhang
Statistical controversies in clinical research: building the bridge to phase II efficacy estimation in dose-expansion cohorts
Annals of Oncology 28: 1427 1435, 17 doi:10.1093/annonc/mdx045 Published online 13 February 17 SPECIAL ARTICLE Statistical controversies in clinical research: building the bridge to phase II efficacy estimation
A Bayesian approach to sample size determination for studies designed to evaluate continuous medical tests
Baylor Health Care System From the SelectedWorks of unlei Cheng 1 A Bayesian approach to sample size determination for studies designed to evaluate continuous medical tests unlei Cheng, Baylor Health Care
The Changing Landscape of Phase I Trials in Cancer: A Novel Design for Dose Finding in the Era of Non-Cytotoxic Agents
The Changing Landscape of Phase I Trials in Cancer: A Novel Design for Dose Finding in the Era of Non-Cytotoxic Agents Elizabeth Garrett-Mayer, PhD 1 Cody Chiuzan, PhD 2 1 Hollings Cancer Center Department
Evolution of Early Phase Trials: Clinical Trial Design in the Modern Era
Evolution of Early Phase Trials: Clinical Trial Design in the Modern Era Shivaani Kummar, MD, FACP Professor of Medicine (Oncology) Director, Phase I Clinical Research Program Co-Director, Translational
The questions are changing in early phase cancer clinical trials: Opportunities for novel statistical designs
The questions are changing in early phase cancer clinical trials: Opportunities for novel statistical designs Elizabeth Garrett-Mayer, PhD Associate Professor Director of Biostatistics, Hollings Cancer
MEASURING THE UNDIAGNOSED FRACTION:
Friday, May 27, 2016 SPRC-PHSKC Lunchbox Talks 1 MEASURING THE UNDIAGNOSED FRACTION: Understanding the UW and CDC back-calculation models Martina Morris, PhD Director, UW CFAR SPRC Jeanette K Birnbaum,
An application of a pattern-mixture model with multiple imputation for the analysis of longitudinal trials with protocol deviations
Iddrisu and Gumedze BMC Medical Research Methodology (2019) 19:10 https://doi.org/10.1186/s12874-018-0639-y RESEARCH ARTICLE Open Access An application of a pattern-mixture model with multiple imputation
Clustering mass spectrometry data using order statistics
Proteomics 2003, 3, 1687 1691 DOI 10.1002/pmic.200300517 1687 Douglas J. Slotta 1 Lenwood S. Heath 1 Naren Ramakrishnan 1 Rich Helm 2 Malcolm Potts 3 1 Department of Computer Science 2 Department of Wood
Protocol Development: The Guiding Light of Any Clinical Study
Protocol Development: The Guiding Light of Any Clinical Study Susan G. Fisher, Ph.D. Chair, Department of Clinical Sciences 1 Introduction Importance/ relevance/ gaps in knowledge Specific purpose of the
Using dynamic prediction to inform the optimal intervention time for an abdominal aortic aneurysm screening programme
Using dynamic prediction to inform the optimal intervention time for an abdominal aortic aneurysm screening programme Michael Sweeting Cardiovascular Epidemiology Unit, University of Cambridge Friday 15th
Advanced IPD meta-analysis methods for observational studies
Advanced IPD meta-analysis methods for observational studies Simon Thompson University of Cambridge, UK Part 4 IBC Victoria, July 2016 1 Outline of talk Usual measures of association (e.g. hazard ratios)
New Challenges: Model-based Dose Finding in the Era of Targeted Agents
New Challenges: Model-based Dose Finding in the Era of Targeted Agents Elizabeth Garrett-Mayer, PhD 1 Cody Chiuzan, PhD 2 1 Hollings Cancer Center Department of Public Health Sciences Medical University
THE GOOD, THE BAD, & THE UGLY: WHAT WE KNOW TODAY ABOUT LCA WITH DISTAL OUTCOMES. Bethany C. Bray, Ph.D.
THE GOOD, THE BAD, & THE UGLY: WHAT WE KNOW TODAY ABOUT LCA WITH DISTAL OUTCOMES Bethany C. Bray, Ph.D. bcbray@psu.edu WHAT ARE WE HERE TO TALK ABOUT TODAY? Behavioral scientists increasingly are using
Landmarking, immortal time bias and. Dynamic prediction
Landmarking and immortal time bias Landmarking and dynamic prediction Discussion Landmarking, immortal time bias and dynamic prediction Department of Medical Statistics and Bioinformatics Leiden University
Cabozantinib (Cometriq )
Cabozantinib (Cometriq ) Workshop dose escalation EMA 4/5 Dec 2014 Frans Opdam, internist-clinical pharmacologist Clinical assessor, Dutch Medicines Agency Phase 3 Study XL184-301 (EXAM): Design Cabozantinib
Statistical methods for the meta-analysis of full ROC curves
Statistical methods for the meta-analysis of full ROC curves Oliver Kuss (joint work with Stefan Hirt and Annika Hoyer) German Diabetes Center, Leibniz Institute for Diabetes Research at Heinrich Heine
Module Overview. What is a Marker? Part 1 Overview
SISCR Module 7 Part I: Introduction Basic Concepts for Binary Classification Tools and Continuous Biomarkers Kathleen Kerr, Ph.D. Associate Professor Department of Biostatistics University of Washington
Continuous Safety Monitoring of Excess Toxicity in Single Arm Early Phase Biomarker Driven Trials
Oregon Health & Science University OHSU Digital Commons Scholar Archive 4-2016 Continuous Safety Monitoring of Excess Toxicity in Single Arm Early Phase Biomarker Driven Trials Racky Daffe Follow this
Bayesian Models for Combining Data Across Subjects and Studies in Predictive fmri Data Analysis
Bayesian Models for Combining Data Across Subjects and Studies in Predictive fmri Data Analysis Thesis Proposal Indrayana Rustandi April 3, 2007 Outline Motivation and Thesis Preliminary results: Hierarchical
Aer ODE B3-20. Database System for GEX B3 Dosimetry. Aérial. Optical Dosimetry Equipment. (gamma and/or electron beam versions)
B3-20 Database System for GEX B3 Dosimetry (gamma and/or electron beam versions) and GEX combine to provide a Genesys 20 version of the flexible 21 CFR Part 11 certified database dosimetry system for use
Phase I dose-escalation trials with more than one dosing regimen
Phase I dose-escalation trials with more than one dosing regimen Burak Kürşad Günhan 1 Sebastian Weber 2 Abdelkader Seroutou 2 Tim Friede 1 IBS-DR, Göttingen, 7 December, 218 1 Department of Medical Statistics,
1.4 - Linear Regression and MS Excel
1.4 - Linear Regression and MS Excel Regression is an analytic technique for determining the relationship between a dependent variable and an independent variable. When the two variables have a linear
The Roles of Short Term Endpoints in. Clinical Trial Planning and Design
The Roles of Short Term Endpoints in Clinical Trial Planning and Design Christopher Jennison Department of Mathematical Sciences, University of Bath, UK http://people.bath.ac.uk/mascj Roche, Welwyn Garden
OncoPhase: Quantification of somatic mutation cellular prevalence using phase information
OncoPhase: Quantification of somatic mutation cellular prevalence using phase information Donatien Chedom-Fotso 1, 2, 3, Ahmed Ashour Ahmed 1, 2, and Christopher Yau 3, 4 1 Ovarian Cancer Cell Laboratory,
Statistical methods for the meta-analysis of full ROC curves
Statistical methods for the meta-analysis of full ROC curves Oliver Kuss (joint work with Stefan Hirt and Annika Hoyer) German Diabetes Center, Leibniz Institute for Diabetes Research at Heinrich Heine
Intro to Probability Instructor: Alexandre Bouchard
www.stat.ubc.ca/~bouchard/courses/stat302-sp2017-18/ Intro to Probability Instructor: Alexandre Bouchard Plan for today: Bayesian inference 101 Decision diagram for non equally weighted problems Bayes
Exposure-response in the presence of confounding
Exposure-response in the presence of confounding Jonathan L. French, ScD Metrum Research Group LLC May 3, 2016 c 2016 Metrum Research Group LLC TICTS, Durham, NC, 2016 May 3, 2016 1 / 24 Outline 1 Introduction
Understanding the cluster randomised crossover design: a graphical illustration of the components of variation and a sample size tutorial
Arnup et al. Trials (2017) 18:381 DOI 10.1186/s13063-017-2113-2 METHODOLOGY Open Access Understanding the cluster randomised crossover design: a graphical illustration of the components of variation and
Bayesian Methods LABORATORY. Lesson 1: Jan Software: R. Bayesian Methods p.1/20
Bayesian Methods LABORATORY Lesson 1: Jan 24 2002 Software: R Bayesian Methods p.1/20 The R Project for Statistical Computing http://www.r-project.org/ R is a language and environment for statistical computing
Bayesian Tolerance Intervals for Sparse Data Margin Assessment
Bayesian Tolerance Intervals for Sparse Data Margin Assessment Benjamin Schroeder and Lauren Hund ASME V&V Symposium May 3, 2017 - Las Vegas, NV SAND2017-4590 C - (UUR) Sandia National Laboratories is
Decision Making in Confirmatory Multipopulation Tailoring Trials
Biopharmaceutical Applied Statistics Symposium (BASS) XX 6-Nov-2013, Orlando, FL Decision Making in Confirmatory Multipopulation Tailoring Trials Brian A. Millen, Ph.D. Acknowledgments Alex Dmitrienko
Modifying ROC Curves to Incorporate Predicted Probabilities
Modifying ROC Curves to Incorporate Predicted Probabilities C. Ferri, P. Flach 2, J. Hernández-Orallo, A. Senad Departament de Sistemes Informàtics i Computació Universitat Politècnica de València Spain
Risk-prediction modelling in cancer with multiple genomic data sets: a Bayesian variable selection approach
Risk-prediction modelling in cancer with multiple genomic data sets: a Bayesian variable selection approach Manuela Zucknick Division of Biostatistics, German Cancer Research Center Biometry Workshop,
On cows and test construction
On cows and test construction NIELS SMITS & KEES JAN KAN Research Institute of Child Development and Education University of Amsterdam, The Netherlands SEM WORKING GROUP AMSTERDAM 16/03/2018 Looking at
SEQUENTIAL MEDICAL TRIALS TREATMENT ALLOCATION WITH DATA DEPENDENT
SEQUENTIAL MEDICAL TRIALS WITH DATA DEPENDENT TREATMENT ALLOCATION B. J. FLEHINGER* IBM THOMAS J. WATSON RESEARCH CENTER and T. A. LOUIS** COLUMBIA UNIVERSITY 1. Introduction This paper is concerned with
Bayesian melding for estimating uncertainty in national HIV prevalence estimates
Bayesian melding for estimating uncertainty in national HIV prevalence estimates Leontine Alkema, Adrian E. Raftery and Tim Brown 1 Working Paper no. 82 Center for Statistics and the Social Sciences University
CONSORT 2010 checklist of information to include when reporting a randomised trial*
CONSORT 2010 checklist of information to include when reporting a randomised trial* Section/Topic Title and abstract Introduction Background and objectives Item No Checklist item 1a Identification as a
SISCR Module 7 Part I: Introduction Basic Concepts for Binary Biomarkers (Classifiers) and Continuous Biomarkers
SISCR Module 7 Part I: Introduction Basic Concepts for Binary Biomarkers (Classifiers) and Continuous Biomarkers Kathleen Kerr, Ph.D. Associate Professor Department of Biostatistics University of Washington
Defining and Measuring Recent infection
Defining and Measuring Recent infection Application to Incidence Estimation Alex Welte Alex Welte (SACEMA) Recent Infection November 2013 1 / 29 Introduction What is Recent Infection? Introduction of a
Empowered by Psychometrics The Fundamentals of Psychometrics. Jim Wollack University of Wisconsin Madison
Empowered by Psychometrics The Fundamentals of Psychometrics Jim Wollack University of Wisconsin Madison Psycho-what? Psychometrics is the field of study concerned with the measurement of mental and psychological
Combining Risks from Several Tumors Using Markov Chain Monte Carlo
University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln U.S. Environmental Protection Agency Papers U.S. Environmental Protection Agency 2009 Combining Risks from Several Tumors
Bayesian Tailored Testing and the Influence
Bayesian Tailored Testing and the Influence of Item Bank Characteristics Carl J. Jensema Gallaudet College Owen s (1969) Bayesian tailored testing method is introduced along with a brief review of its
The clinical trial information provided in this public disclosure synopsis is supplied for informational purposes only.
The clinical trial information provided in this public disclosure synopsis is supplied for informational purposes only. Please note that the results reported in any single trial may not reflect the overall
Dynamic borrowing of historical data: Performance and comparison of existing methods based on a case study
Introduction Methods Simulations Discussion Dynamic borrowing of historical data: Performance and comparison of existing methods based on a case study D. Dejardin 1, P. Delmar 1, K. Patel 1, C. Warne 1,
Modelling prognostic capabilities of tumor size: application to colorectal cancer
Session 3: Epidemiology and public health Modelling prognostic capabilities of tumor size: application to colorectal cancer Virginie Rondeau, INSERM Modelling prognostic capabilities of tumor size : application
Design for Targeted Therapies: Statistical Considerations
Design for Targeted Therapies: Statistical Considerations J. Jack Lee, Ph.D. Department of Biostatistics University of Texas M. D. Anderson Cancer Center Outline Premise General Review of Statistical Designs
Vocabulary. Bias. Blinding. Block. Cluster sample
Bias Blinding Block Census Cluster sample Confounding Control group Convenience sample Designs Experiment Experimental units Factor Level Any systematic failure of a sampling method to represent its population
Practical Bayesian Design and Analysis for Drug and Device Clinical Trials
Practical Bayesian Design and Analysis for Drug and Device Clinical Trials p. 1/2 Practical Bayesian Design and Analysis for Drug and Device Clinical Trials Brian P. Hobbs Plan B Advisor: Bradley P. Carlin
SOME PRACTICAL IMPROVEMENTS IN THE CONTINUAL REASSESSMENT METHOD FOR PHASE I STUDIES
STATISTICS IN MEDICINE, VOL. 14, 1149-1161 (1995) SOME PRACTICAL IMPROVEMENTS IN THE CONTINUAL REASSESSMENT METHOD FOR PHASE I STUDIES STEVEN N. GOODMAN, MARIANNA L. ZAHURAK AND STEVEN PIANTADOSI Johns
Chapter 5: Field experimental designs in agriculture
Chapter 5: Field experimental designs in agriculture Jose Crossa Biometrics and Statistics Unit Crop Research Informatics Lab (CRIL) CIMMYT. Int. Apdo. Postal 6-641, 06600 Mexico, DF, Mexico Introduction
A Comparison of Pseudo-Bayesian and Joint Maximum Likelihood Procedures for Estimating Item Parameters in the Three-Parameter IRT Model
A Comparison of Pseudo-Bayesian and Joint Maximum Likelihood Procedures for Estimating Item Parameters in the Three-Parameter IRT Model Gary Skaggs Fairfax County, Virginia Public Schools José Stevenson
Method Comparison Report Semi-Annual 1/5/2018
Method Comparison Report Semi-Annual 1/5/2018 Prepared for Carl Commissioner Regularatory Commission 123 Commission Drive Anytown, XX, 12345 Prepared by Dr. Mark Mainstay Clinical Laboratory Kennett Community
To assess safety profiles: significant laboratory changes and adverse events (AEs).
These results are supplied for informational purposes only. Prescribing decisions should be made based on the approved package insert in the country of prescription. Sponsor / Company: Sanofi Drug substance(s):
Binary Diagnostic Tests Paired Samples
Chapter 536 Binary Diagnostic Tests Paired Samples Introduction An important task in diagnostic medicine is to measure the accuracy of two diagnostic tests. This can be done by comparing summary measures
Sample size calculation for a stepped wedge trial
Baio et al. Trials (2015) 16:354 DOI 10.1186/s13063-015-0840-9 TRIALS RESEARCH Sample size calculation for a stepped wedge trial Open Access Gianluca Baio 1*,AndrewCopas 2, Gareth Ambler 1, James Hargreaves
A Bayesian approach for dose-escalation in a phase I clinical trial incorporating pharmacodynamic endpoints
A Bayesian approach for dose-escalation in a phase I clinical trial incorporating pharmacodynamic endpoints John Whitehead 1,*, Lisa Hampson, Yinghui Zhou 1, Edouard Ledent 3,4 Pereira 3,5 and Alvaro 1
Bayesian Confidence Intervals for Means and Variances of Lognormal and Bivariate Lognormal Distributions
Bayesian Confidence Intervals for Means and Variances of Lognormal and Bivariate Lognormal Distributions J. Harvey a,b, & A.J. van der Merwe b a Centre for Statistical Consultation Department of Statistics
Kinetic Modeling of Data Eviction in Cache
Kinetic Modeling of Data Eviction in Cache Xiameng Hu, Xiaolin Wang, Lan Zhou, YingweiLuo Peking University Chen Ding University of Rochester Zhenlin Wang Michigan Technological University 1 Background
Modeling Human Perception
Modeling Human Perception Could Stevens Power Law be an Emergent Feature? Matthew L. Bolton Systems and Information Engineering University of Virginia Charlottesville, United States of America Mlb4b@Virginia.edu
LIHS Mini Master Class Multilevel Modelling
LIHS Mini Master Class Multilevel Modelling Robert M West 9 November 2016 Robert West c University of Leeds 2016. This work is made available for reuse under the terms of the Creative Commons Attribution
Bayesian hierarchical modelling
Bayesian hierarchical modelling Matthew Schofield Department of Mathematics and Statistics, University of Otago Bayesian hierarchical modelling Slide 1 What is a statistical model? A statistical model:
Methods for Determining Random Sample Size
Methods for Determining Random Sample Size This document discusses how to determine your random sample size based on the overall purpose of your research project. Methods for determining the random sample
Clinical trials with incomplete daily diary data
Clinical trials with incomplete daily diary data N. Thomas 1, O. Harel 2, and R. Little 3 1 Pfizer Inc 2 University of Connecticut 3 University of Michigan BASS, 2015 Thomas, Harel, Little (Pfizer) Clinical
PRACTICAL STATISTICS FOR MEDICAL RESEARCH
PRACTICAL STATISTICS FOR MEDICAL RESEARCH Douglas G. Altman Head of Medical Statistical Laboratory Imperial Cancer Research Fund London CHAPMAN & HALL/CRC Boca Raton London New York Washington, D.C. Contents
Heritability. The Extended Liability-Threshold Model. Polygenic model for continuous trait. Polygenic model for continuous trait.
The Extended Liability-Threshold Model Klaus K. Holst Thomas Scheike, Jacob Hjelmborg 014-05-1 Heritability Twin studies Include both monozygotic (MZ) and dizygotic (DZ) twin
Impacts of genomic. pre-selection on classical genetic evaluations. Vincent Ducrocq, Clotilde Patry. Vincent Ducrocq & Clotilde Patry
Impacts of genomic pre-selection on classical genetic evaluations Vincent Ducrocq, Clotilde Patry 1 Overview Theoretical considerations suggest that genomic pre-selection of progeny-tested bulls leads
Item Analysis: Classical and Beyond
Item Analysis: Classical and Beyond SCROLLA Symposium Measurement Theory and Item Analysis Modified for EPE/EDP 711 by Kelly Bradley on January 8, 2013 Why is item analysis relevant? Item analysis provides
Analyzing diastolic and systolic blood pressure individually or jointly?
Analyzing diastolic and systolic blood pressure individually or jointly? Chenglin Ye a, Gary Foster a, Lisa Dolovich b, Lehana Thabane a,c a. Department of Clinical Epidemiology and Biostatistics, McMaster
Design of a Community Randomized HIV Prevention Trial in Botswana
Design of a Community Randomized HIV Prevention Trial in Botswana Rui Wang, Ph.D. Department of Population Medicine Harvard Medical School and Harvard Pilgrim HealthCare Institute November 3rd, 2016 CRT:
QA 605 WINTER QUARTER ACADEMIC YEAR
Instructor: Office: James J. Cochran 117A CAB Telephone: (318) 257-3445 Hours: e-mail: URL: QA 605 WINTER QUARTER 2006-2007 ACADEMIC YEAR Tuesday & Thursday 8:00 a.m. 10:00 a.m. Wednesday 8:00 a.m. noon
RANDOMIZATION. Outline of Talk
RANDOMIZATION Basic Ideas and Insights Marvin Zelen Harvard University Graybill Conference Ft.Collins, Colorado June 11, 2008 Outline of Talk Introduction Randomized vs. Observational Studies Conditioning
Meta-analysis of two studies in the presence of heterogeneity with applications in rare diseases
Meta-analysis of two studies in the presence of heterogeneity with applications in rare diseases Christian Röver 1, Tim Friede 1, Simon Wandel 2 and Beat Neuenschwander 2 1 Department of Medical Statistics,
On Regression Analysis Using Bivariate Extreme Ranked Set Sampling
On Regression Analysis Using Bivariate Extreme Ranked Set Sampling Atsu S. S. Dorvlo atsu@squ.edu.om Walid Abu-Dayyeh walidan@squ.edu.om Obaid Alsaidy obaidalsaidy@gmail.com Abstract- Many forms of ranked