Sample Size calculations for Stepped Wedge Trials
|
|
|
- Bartholomew Watson
- 5 years ago
- Views:
Transcription
1 Sample Size calculations for Stepped Wedge Trials Gianluca Baio University College London Department of Statistical Science (Joint work with Rumana Omar, Andrew Copas, Emma Beard, James Hargreaves and Gareth Ambler) Stepped Wedge Trials Symposium London School of Hygiene & Tropical Medicine London, 22 September 2015 Gianluca Baio ( UCL) Sample size calculations for SWTs LSHTM Symposium, 22 Sep / 10
2 Objectives of the project/paper Work supported by a NIHR Research Methods Opportunity Funding Scheme Grant (RMOFS ) Critically investigate the conditions under which applying a stepped wedge design can result in potential gains in terms of Efficiency Statistical power Financial/ethical implications Produce a toolbox to perform power calculations Simulation-based approach Extension to more general models Gianluca Baio ( UCL) Sample size calculations for SWTs LSHTM Symposium, 22 Sep / 10
3 Objectives of the project/paper Work supported by a NIHR Research Methods Opportunity Funding Scheme Grant (RMOFS ) Critically investigate the conditions under which applying a stepped wedge design can result in potential gains in terms of Efficiency Statistical power Financial/ethical implications Produce a toolbox to perform power calculations Simulation-based approach Extension to more general models Have lots of fun working in the Special Issue Crew! Gianluca Baio ( UCL) Sample size calculations for SWTs LSHTM Symposium, 22 Sep / 10
4 Modelling & sample size calculations Analytical formulæ Gianluca Baio ( UCL) Sample size calculations for SWTs LSHTM Symposium, 22 Sep / 10
5 Modelling & sample size calculations Analytical formulæ Hussey & Hughes (2007) + Reprise : Hughes et al (2015) Specifically for cross-sectional data. Defines cluster- and time-specific average outcome as µ ij = µ + α i + β j + X ijθ Can compute ( ) θ Power = Φ z α/2 V (θ) where V (θ) = f(x, I, J, σ 2 e, σ 2 α) Can use asymptotic normality, eg for binary or count outcomes Gianluca Baio ( UCL) Sample size calculations for SWTs LSHTM Symposium, 22 Sep / 10
6 Modelling & sample size calculations Analytical formulæ Hussey & Hughes (2007) + Reprise : Hughes et al (2015) Specifically for cross-sectional data. Defines cluster- and time-specific average outcome as µ ij = µ + α i + β j + X ijθ Can compute ( ) θ Power = Φ z α/2 V (θ) where V (θ) = f(x, I, J, σ 2 e, σ 2 α) Can use asymptotic normality, eg for binary or count outcomes Design Effect (DE) Wortman et al (2013) + Hemming & Taljaard (2015) Compute inflation factor to account for induce correlation and re-scale sample size for a parallel RCT Based on Hussey and Hughes (2007) cross-sectional data Gianluca Baio ( UCL) Sample size calculations for SWTs LSHTM Symposium, 22 Sep / 10
7 Modelling & sample size calculations Analytical formulæ Hussey & Hughes (2007) + Reprise : Hughes et al (2015) Specifically for cross-sectional data. Defines cluster- and time-specific average outcome as µ ij = µ + α i + β j + X ijθ Can compute ( ) θ Power = Φ z α/2 V (θ) where V (θ) = f(x, I, J, σ 2 e, σ 2 α) Can use asymptotic normality, eg for binary or count outcomes Design Effect (DE) Wortman et al (2013) + Hemming & Taljaard (2015) Compute inflation factor to account for induce correlation and re-scale sample size for a parallel RCT Based on Hussey and Hughes (2007) cross-sectional data Some generalisations (Hemming et al 2014) Multiple layers of clustering + incomplete SWT Gianluca Baio ( UCL) Sample size calculations for SWTs LSHTM Symposium, 22 Sep / 10
8 Modelling & sample size calculations Simulation-based calculations Baio et al (2015) Can directly model different types of outcomes (eg binary or counts) The linear predictor is just defined using a suitable transformation g( ) Can extend model to account for specific features of the SWT Repeated measurements (eg closed-cohort) add extra random effect v ik Normal(0, σ 2 v) Specify time trends (eg quadratic or polynomial) Include cluster-specific intervention effects X ij(θ + u i) with u i Normal(0, σ 2 u) Gianluca Baio ( UCL) Sample size calculations for SWTs LSHTM Symposium, 22 Sep / 10
9 Modelling & sample size calculations Simulation-based calculations Baio et al (2015) Can directly model different types of outcomes (eg binary or counts) The linear predictor is just defined using a suitable transformation g( ) Can extend model to account for specific features of the SWT Repeated measurements (eg closed-cohort) add extra random effect v ik Normal(0, σ 2 v) Specify time trends (eg quadratic or polynomial) Include cluster-specific intervention effects X ij(θ + u i) with u i Normal(0, σ 2 u) Helps alignment of design and analysis model This is one of the issues identified by the literature review More flexibility at design stage to match complexity of data generating process as well as analysis model (mixed effects, GEE, etc) Gianluca Baio ( UCL) Sample size calculations for SWTs LSHTM Symposium, 22 Sep / 10
10 Simulation-based vs analytical calculations ICC Analytical power based on HH Simulation-based calculations K = 20, J = 6 K = 20, J = 6 Continuous outcome a Binary outcome b Count outcome c a Intervention effect = ; σe = b Baseline outcome probability = 0.26; OR = c Baseline outcome rate = 1.5; RR = 0.8. Notation: K = number of subjects per cluster; J = total number of time points, including one baseline. The cells in the table are the estimated number of clusters as a function of the ICC and outcome type, to obtain 80% power Gianluca Baio ( UCL) Sample size calculations for SWTs LSHTM Symposium, 22 Sep / 10
11 Cross-sectional vs closed-cohort data Effect size & ICC Continuous outcome Cross-sectional Closed-cohort I = 25 clusters, each with K = 20 subjects; J = 6 time points ( measurements) including one baseline Gianluca Baio ( UCL) Sample size calculations for SWTs LSHTM Symposium, 22 Sep / 10
12 Cross-sectional vs closed-cohort data Number of steps Binary outcome Cross-sectional Closed-cohort I = 24 clusters, each with K = 20 subjects; individual-level ICC= for closed-cohort Gianluca Baio ( UCL) Sample size calculations for SWTs LSHTM Symposium, 22 Sep / 10
13 R package SWSamp Will allow the user to run simulations for a set of basic models Cross-sectional + closed-cohort data Continuous (normal), binary and count outcome Provide template for custom data-generating models Include Bayesian alternative (based on INLA) Comparable computational time to REML Can use default priors but can also customise Explore issues with open-cohorts & time-to-event outcomes Gianluca Baio ( UCL) Sample size calculations for SWTs LSHTM Symposium, 22 Sep / 10
14 R package SWSamp Will allow the user to run simulations for a set of basic models Cross-sectional + closed-cohort data Continuous (normal), binary and count outcome Provide template for custom data-generating models Include Bayesian alternative (based on INLA) Comparable computational time to REML Can use default priors but can also customise Explore issues with open-cohorts & time-to-event outcomes Can use the name Samp... Gianluca Baio ( UCL) Sample size calculations for SWTs LSHTM Symposium, 22 Sep / 10
15 (Only a very few!) References Baio G, Copas A, Ambler G, Hargreaves J, Beard E, Omar R. (2015) Sample size calculations for a stepped wedge trial. Trials. 16:354. doi: /s Hemming K, Lilford R, Girling A. (2014) Stepped-wedge cluster randomised controlled trials: a generic framework including parallel and multiple-level design. Statistics in Medicine. 34(2): doi: /sim.6325 Hemming K, Taljaard M. (2015) Relative efficiencies of stepped wedge and cluster randomized trials were easily compared using a unified approach. Journal of Clinical Epidemiology. doi: /j.jclinepi Hussey M and Hughes J. (2007). Design and analysis of stepped wedge cluster randomized trials. Contemporary Clinical Trials. 28: Hughes J, Granston T and Heagerty, P. (2015). Current issues in the design and analysis of stepped wedge trials. Contemporary Clinical Trials. doi: /j.cct Woertman W, de Hoopa E, Moerbeek M, Zuidemac S, Gerritsen D and Teerenstra S. (2013). Stepped wedge designs could reduce the required sample size in cluster randomized trials. Journal of Clinical Epidemiology. 66: Gianluca Baio ( UCL) Sample size calculations for SWTs LSHTM Symposium, 22 Sep / 10
16 Thank you! Gianluca Baio ( UCL) Sample size calculations for SWTs LSHTM Symposium, 22 Sep / 10
POWER AND SAMPLE SIZE DETERMINATION FOR STEPPED WEDGE CLUSTER RANDOMIZED TRIALS
POWER AND SAMPLE SIZE DETERMINATION FOR STEPPED WEDGE CLUSTER RANDOMIZED TRIALS by Christopher M. Keener B.S. Molecular Biology, Clarion University of Pittsburgh, 2009 Submitted to the Graduate Faculty
Package swcrtdesign. February 12, 2018
Package swcrtdesign February 12, 2018 Type Package Title Stepped Wedge Cluster Randomized Trial (SW CRT) Design Version 2.2 Date 2018-2-4 Maintainer Jim Hughes Author Jim Hughes and Navneet
INLA: an introduction
INLA: an introduction Håvard Rue 1 Norwegian University of Science and Technology Trondheim, Norway May 2009 1 Joint work with S.Martino (Trondheim) and N.Chopin (Paris) Latent Gaussian models Background
GAMs semi-parametric GLMs. Simon Wood Mathematical Sciences, University of Bath, U.K.
GAMs semi-parametric GLMs Simon Wood Mathematical Sciences, University of Bath, U.K. Generalized linear models, GLM 1. A GLM models a univariate response, y i as g{e(y i )} = X i β where y i Exponential
Creating an R Package
Creating an R Package M. Quartagno 1 1 Department of Medical Statistics London School of Hygiene and Tropical Medicine EMERGE Group meeting, 2015 Terminology. Terminology Repositories Package: extension
Randolph E. Bucklin Catarina Sismeiro The Anderson School at UCLA. Overview. Introduction/Clickstream Research Conceptual Framework
A Model of Web Site Browsing Behavior Estimated on Clickstream Data Randolph E. Bucklin Catarina Sismeiro The Anderson School at UCLA Overview Introduction/Clickstream Research Conceptual Framework 4 Within-site
Smoothing and Forecasting Mortality Rates with P-splines. Iain Currie. Data and problem. Plan of talk
Smoothing and Forecasting Mortality Rates with P-splines Iain Currie Heriot Watt University Data and problem Data: CMI assured lives : 20 to 90 : 1947 to 2002 Problem: forecast table to 2046 London, June
An imputation approach for analyzing mixed-mode surveys
An imputation approach for analyzing mixed-mode surveys Jae-kwang Kim 1 Iowa State University June 4, 2013 1 Joint work with S. Park and S. Kim Ouline Introduction Proposed Methodology Application to Private
Clustering and The Expectation-Maximization Algorithm
Clustering and The Expectation-Maximization Algorithm Unsupervised Learning Marek Petrik 3/7 Some of the figures in this presentation are taken from An Introduction to Statistical Learning, with applications
10-701/15-781, Fall 2006, Final
-7/-78, Fall 6, Final Dec, :pm-8:pm There are 9 questions in this exam ( pages including this cover sheet). If you need more room to work out your answer to a question, use the back of the page and clearly
Problem 1 (20 pt) Answer the following questions, and provide an explanation for each question.
Problem 1 Answer the following questions, and provide an explanation for each question. (5 pt) Can linear regression work when all X values are the same? When all Y values are the same? (5 pt) Can linear
STOCHASTIC USER EQUILIBRIUM TRAFFIC ASSIGNMENT WITH MULTIPLE USER CLASSES AND ELASTIC DEMAND
STOCHASTIC USER EQUILIBRIUM TRAFFIC ASSIGNMENT WITH MULTIPLE USER CLASSES AND ELASTIC DEMAND Andrea Rosa and Mike Maher School of the Built Environment, Napier University, U e-mail: a.rosa@napier.ac.uk
The linear mixed model: modeling hierarchical and longitudinal data
The linear mixed model: modeling hierarchical and longitudinal data Analysis of Experimental Data AED The linear mixed model: modeling hierarchical and longitudinal data 1 of 44 Contents 1 Modeling Hierarchical
A Deterministic Global Optimization Method for Variational Inference
A Deterministic Global Optimization Method for Variational Inference Hachem Saddiki Mathematics and Statistics University of Massachusetts, Amherst saddiki@math.umass.edu Andrew C. Trapp Operations and
Introduction to Mixed Models: Multivariate Regression
Introduction to Mixed Models: Multivariate Regression EPSY 905: Multivariate Analysis Spring 2016 Lecture #9 March 30, 2016 EPSY 905: Multivariate Regression via Path Analysis Today s Lecture Multivariate
Modelling Personalized Screening: a Step Forward on Risk Assessment Methods
Modelling Personalized Screening: a Step Forward on Risk Assessment Methods Validating Prediction Models Inmaculada Arostegui Universidad del País Vasco UPV/EHU Red de Investigación en Servicios de Salud
Rolling Markov Chain Monte Carlo
Rolling Markov Chain Monte Carlo Din-Houn Lau Imperial College London Joint work with Axel Gandy 4 th September 2013 RSS Conference 2013: Newcastle Output predicted final ranks of the each team. Updates
CALIBRATION BETWEEN DEPTH AND COLOR SENSORS FOR COMMODITY DEPTH CAMERAS. Cha Zhang and Zhengyou Zhang
CALIBRATION BETWEEN DEPTH AND COLOR SENSORS FOR COMMODITY DEPTH CAMERAS Cha Zhang and Zhengyou Zhang Communication and Collaboration Systems Group, Microsoft Research {chazhang, zhang}@microsoft.com ABSTRACT
Patient Safety Initiatives Implementation (WP5): Presentation of Main Outcomes
Patient Safety Initiatives Implementation (WP5): Presentation of Main Outcomes Lena Mehrmann, Tugce Aksoy, Christian Thomeczek German Agency for Quality in Medicine (AQuMed / ÄZQ) PaSQ 5 th Coordination
INLA: Integrated Nested Laplace Approximations
INLA: Integrated Nested Laplace Approximations John Paige Statistics Department University of Washington October 10, 2017 1 The problem Markov Chain Monte Carlo (MCMC) takes too long in many settings.
Types of missingness and common strategies
9 th UK Stata Users Meeting 20 May 2003 Multiple imputation for missing data in life course studies Bianca De Stavola and Valerie McCormack (London School of Hygiene and Tropical Medicine) Motivating example
DATA ANALYSIS USING HIERARCHICAL GENERALIZED LINEAR MODELS WITH R
DATA ANALYSIS USING HIERARCHICAL GENERALIZED LINEAR MODELS WITH R Lee, Rönnegård & Noh LRN@du.se Lee, Rönnegård & Noh HGLM book 1 / 24 Overview 1 Background to the book 2 Crack growth example 3 Contents
Recursion. COMS W1007 Introduction to Computer Science. Christopher Conway 26 June 2003
Recursion COMS W1007 Introduction to Computer Science Christopher Conway 26 June 2003 The Fibonacci Sequence The Fibonacci numbers are: 1, 1, 2, 3, 5, 8, 13, 21, 34,... We can calculate the nth Fibonacci
Creating summary tables using the sumtable command
Creating summary tables using the sumtable command Lauren Scott and Chris Rogers University of Bristol Clinical Trials and Evaluation Unit 2016 London Stata Users Group meeting Scott LJ, Rogers CA. Creating
Learning a Manifold as an Atlas Supplementary Material
Learning a Manifold as an Atlas Supplementary Material Nikolaos Pitelis Chris Russell School of EECS, Queen Mary, University of London [nikolaos.pitelis,chrisr,lourdes]@eecs.qmul.ac.uk Lourdes Agapito
Package samplingdatacrt
Version 1.0 Type Package Package samplingdatacrt February 6, 2017 Title Sampling Data Within Different Study Designs for Cluster Randomized Trials Date 2017-01-20 Author Diana Trutschel, Hendrik Treutler
Constrained Optimal Sample Allocation in Multilevel Randomized Experiments Using PowerUpR
Constrained Optimal Sample Allocation in Multilevel Randomized Experiments Using PowerUpR Metin Bulus & Nianbo Dong University of Missouri March 3, 2017 Contents Introduction PowerUpR Package COSA Functions
Non-Linearity of Scorecard Log-Odds
Non-Linearity of Scorecard Log-Odds Ross McDonald, Keith Smith, Matthew Sturgess, Edward Huang Retail Decision Science, Lloyds Banking Group Edinburgh Credit Scoring Conference 6 th August 9 Lloyds Banking
Template text for search methods: New reviews
Template text for search methods: New reviews This document draws together best practice examples from the Cochrane Information Specialist community in relation to writing the search methods in a Cochrane
Hidden Markov Models in the context of genetic analysis
Hidden Markov Models in the context of genetic analysis Vincent Plagnol UCL Genetics Institute November 22, 2012 Outline 1 Introduction 2 Two basic problems Forward/backward Baum-Welch algorithm Viterbi
The impact of patient, intervention, comparison, outcome (PICO) as a search strategy tool on literature search quality: a systematic review
The impact of patient, intervention, comparison, outcome (PICO) as a search strategy tool on literature search quality: a systematic review Mette Brandt Eriksen, PhD; Tove Faber Frandsen, PhD APPENDIX
Real-world bandit applications: Bridging the gap between theory and practice
Real-world bandit applications: Bridging the gap between theory and practice Audrey Durand EWRL 2018 The bandit setting (Thompson, 1933; Robbins, 1952; Lai and Robbins, 1985) Action Agent Context Environment
Searching the Cochrane Library
Searching the Cochrane Library To book your place on the course contact the library team: www.epsom-sthelier.nhs.uk/lis E: hirsonlibrary@esth.nhs.uk T: 020 8296 2430 Learning objectives At the end of this
Applied Longitudinal Analysis, 2nd Edition
Applied Longitudinal Analysis, 2nd Edition Garrett M. Fitzmaurice Nan M. Laird James H. Ware Typos, Errors and Omissions: Despite our best efforts, the first printing contains the following typos and errors.
Over-contribution in discretionary databases
Over-contribution in discretionary databases Mike Klaas klaas@cs.ubc.ca Faculty of Computer Science University of British Columbia Outline Over-contribution in discretionary databases p.1/1 Outline Social
Industrialising Small Area Estimation at the Australian Bureau of Statistics
Industrialising Small Area Estimation at the Australian Bureau of Statistics Peter Radisich Australian Bureau of Statistics Workshop on Methods in Official Statistics - March 14 2014 Outline Background
r v i e w o f s o m e r e c e n t d e v e l o p m
O A D O 4 7 8 O - O O A D OA 4 7 8 / D O O 3 A 4 7 8 / S P O 3 A A S P - * A S P - S - P - A S P - - - - L S UM 5 8 - - 4 3 8 -F 69 - V - F U 98F L 69V S U L S UM58 P L- SA L 43 ˆ UéL;S;UéL;SAL; - - -
Design and Analysis Methods for Cluster Randomized Trials with Pair-Matching on Baseline Outcome: Reduction of Treatment Effect Variance
Virginia Commonwealth University VCU Scholars Compass Theses and Dissertations Graduate School 2006 Design and Analysis Methods for Cluster Randomized Trials with Pair-Matching on Baseline Outcome: Reduction
ipads and IPs: Maximizing the Use of Mobile Technology within Infection Prevention and Healthcare Epidemiology September 26 th, 2012
ipads and IPs: Maximizing the Use of Mobile Technology within Infection Prevention and Healthcare Epidemiology September 26 th, 2012 Matthew E. Weissenbach, MPH, CPH, CIC Clinical Program Manager, Infection
Link Mining & Entity Resolution. Lise Getoor University of Maryland, College Park
Link Mining & Entity Resolution Lise Getoor University of Maryland, College Park Learning in Structured Domains Traditional machine learning and data mining approaches assume: A random sample of homogeneous
Missing Data and Imputation
Missing Data and Imputation NINA ORWITZ OCTOBER 30 TH, 2017 Outline Types of missing data Simple methods for dealing with missing data Single and multiple imputation R example Missing data is a complex
Rolling Markov Chain Monte Carlo
Rolling Markov Chain Monte Carlo Din-Houn Lau Imperial College London Joint work with Axel Gandy 4 th July 2013 Predict final ranks of the each team. Updates quick update of predictions. Accuracy control
Detecting changes in streaming data using adaptive estimation
Detecting changes in streaming data using adaptive estimation Dean Bodenham, ETH Zürich ( joint work with Niall Adams) Loading [MathJax]/jax/output/HTML-CSS/jax.js Outline Change detection in streaming
Expectation-Maximization. Nuno Vasconcelos ECE Department, UCSD
Expectation-Maximization Nuno Vasconcelos ECE Department, UCSD Plan for today last time we started talking about mixture models we introduced the main ideas behind EM to motivate EM, we looked at classification-maximization
Graphical Models & HMMs
Graphical Models & HMMs Henrik I. Christensen Robotics & Intelligent Machines @ GT Georgia Institute of Technology, Atlanta, GA 30332-0280 hic@cc.gatech.edu Henrik I. Christensen (RIM@GT) Graphical Models
for Images A Bayesian Deformation Model
Statistics in Imaging Workshop July 8, 2004 A Bayesian Deformation Model for Images Sining Chen Postdoctoral Fellow Biostatistics Division, Dept. of Oncology, School of Medicine Outline 1. Introducing
MODEL SELECTION AND MODEL AVERAGING IN THE PRESENCE OF MISSING VALUES
UNIVERSITY OF GLASGOW MODEL SELECTION AND MODEL AVERAGING IN THE PRESENCE OF MISSING VALUES by KHUNESWARI GOPAL PILLAY A thesis submitted in partial fulfillment for the degree of Doctor of Philosophy in
A step by step Guide to Searching the Cochrane database
A step by step Guide to Searching the Cochrane database www.knowledgenet.ashfordstpeters.nhs.uk/knet/ 2011 KnowledgeNet www.knowledgenet.ashfordstpeters.nhs.uk/knet/ By the end of this presentation you
Context-sensitive Classification Forests for Segmentation of Brain Tumor Tissues
Context-sensitive Classification Forests for Segmentation of Brain Tumor Tissues D. Zikic, B. Glocker, E. Konukoglu, J. Shotton, A. Criminisi, D. H. Ye, C. Demiralp 3, O. M. Thomas 4,5, T. Das 4, R. Jena
Non-Bayesian Classifiers Part II: Linear Discriminants and Support Vector Machines
Non-Bayesian Classifiers Part II: Linear Discriminants and Support Vector Machines Selim Aksoy Department of Computer Engineering Bilkent University saksoy@cs.bilkent.edu.tr CS 551, Spring 2007 c 2007,
Asreml-R: an R package for mixed models using residual maximum likelihood
Asreml-R: an R package for mixed models using residual maximum likelihood David Butler 1 Brian Cullis 2 Arthur Gilmour 3 1 Queensland Department of Primary Industries Toowoomba 2 NSW Department of Primary
3ie evidence gap maps platform Automatic importer guide
3ie evidence gap maps platform Automatic importer guide Steve Lacey Manta Ray Media Ltd Jennifer Stevenson International Initiative for Impact Evaluation, 3ie March 2018 Contents 1. Overview... 1 1.1 Building
Statistical Analysis of the 3WAY Block Cipher
Statistical Analysis of the 3WAY Block Cipher By Himanshu Kale Project Report Submitted In Partial Fulfilment of the Requirements for the Degree of Master of Science In Computer Science Supervised by Professor
Distribution of the Number of Encryptions in Revocation Schemes for Stateless Receivers
Discrete Mathematics and Theoretical Computer Science DMTCS vol. (subm.), by the authors, 1 1 Distribution of the Number of Encryptions in Revocation Schemes for Stateless Receivers Christopher Eagle 1
Non-Parametric and Semi-Parametric Methods for Longitudinal Data
PART III Non-Parametric and Semi-Parametric Methods for Longitudinal Data CHAPTER 8 Non-parametric and semi-parametric regression methods: Introduction and overview Xihong Lin and Raymond J. Carroll Contents
Global modelling of air pollution using multiple data sources
Global modelling of air pollution using multiple data sources Matthew Thomas M.L.Thomas@bath.ac.uk Supervised by Dr. Gavin Shaddick In collaboration with IHME and WHO June 14, 2016 1/ 1 MOTIVATION Air
Mixture Models and the EM Algorithm
Mixture Models and the EM Algorithm Padhraic Smyth, Department of Computer Science University of California, Irvine c 2017 1 Finite Mixture Models Say we have a data set D = {x 1,..., x N } where x i is
STATA 13 INTRODUCTION
STATA 13 INTRODUCTION Catherine McGowan & Elaine Williamson LONDON SCHOOL OF HYGIENE & TROPICAL MEDICINE DECEMBER 2013 0 CONTENTS INTRODUCTION... 1 Versions of STATA... 1 OPENING STATA... 1 THE STATA
STIR. Software for Tomographic Image Reconstruction. Kris Thielemans
STIR Software for Tomographic Image Reconstruction http://stir.sourceforge.net Kris Thielemans University College London Algorithms And Software Consulting Ltd OPENMP support Forthcoming release Gradient-computation
POSTGRADUATE DIPLOMA IN RESEARCH METHODS
POSTGRADUATE DIPLOMA IN RESEARCH METHODS AWARD SCHEME ACADEMIC YEAR 2012-2013 1. INTRODUCTION 1.1 This Award Scheme sets out rules for making awards for the Postgraduate Diploma in Research Methods which
Recent advances in Metamodel of Optimal Prognosis. Lectures. Thomas Most & Johannes Will
Lectures Recent advances in Metamodel of Optimal Prognosis Thomas Most & Johannes Will presented at the Weimar Optimization and Stochastic Days 2010 Source: www.dynardo.de/en/library Recent advances in
Statistical Modeling of Neuroimaging Data: Targeting Activation, Task-Related Connectivity, and Prediction
Statistical Modeling of Neuroimaging Data: Targeting Activation, Task-Related Connectivity, and Prediction F. DuBois Bowman Department of Biostatistics and Bioinformatics Emory University, Atlanta, GA,
Model-based Recursive Partitioning for Subgroup Analyses
EBPI Epidemiology, Biostatistics and Prevention Institute Model-based Recursive Partitioning for Subgroup Analyses Torsten Hothorn; joint work with Heidi Seibold and Achim Zeileis 2014-12-03 Subgroup analyses
Analyzing Longitudinal Data Using Regression Splines
Analyzing Longitudinal Data Using Regression Splines Zhang Jin-Ting Dept of Stat & Appl Prob National University of Sinagpore August 18, 6 DSAP, NUS p.1/16 OUTLINE Motivating Longitudinal Data Parametric
SVM in Analysis of Cross-Sectional Epidemiological Data Dmitriy Fradkin. April 4, 2005 Dmitriy Fradkin, Rutgers University Page 1
SVM in Analysis of Cross-Sectional Epidemiological Data Dmitriy Fradkin April 4, 2005 Dmitriy Fradkin, Rutgers University Page 1 Overview The goals of analyzing cross-sectional data Standard methods used
Numerical-Asymptotic Approximation at High Frequency
Numerical-Asymptotic Approximation at High Frequency University of Reading Mathematical and Computational Aspects of Maxwell s Equations Durham, July 12th 2016 Our group on HF stuff in Reading Steve Langdon,
Statistical modelling with missing data using multiple imputation. Session 2: Multiple Imputation
Statistical modelling with missing data using multiple imputation Session 2: Multiple Imputation James Carpenter London School of Hygiene & Tropical Medicine Email: james.carpenter@lshtm.ac.uk www.missingdata.org.uk
Linear Regression QuadraJc Regression Year
Linear Regression Regression Given: n Data X = x (1),...,x (n)o where x (i) 2 R d n Corresponding labels y = y (1),...,y (n)o where y (i) 2 R September Arc+c Sea Ice Extent (1,000,000 sq km) 9 8 7 6 5
Facilitating Data Discovery through a Semantic Data Catalog
Facilitating Data Discovery through a Semantic Data Catalog HUMAN HEALTH ENVIRONMENTAL HEALTH 1 2014 PerkinElmer Agenda Why is Data Visualization only part of the story? What is a Semantic Data Catalog?
1 : Introduction to GM and Directed GMs: Bayesian Networks. 3 Multivariate Distributions and Graphical Models
10-708: Probabilistic Graphical Models, Spring 2015 1 : Introduction to GM and Directed GMs: Bayesian Networks Lecturer: Eric P. Xing Scribes: Wenbo Liu, Venkata Krishna Pillutla 1 Overview This lecture
Motivating Example. Missing Data Theory. An Introduction to Multiple Imputation and its Application. Background
An Introduction to Multiple Imputation and its Application Craig K. Enders University of California - Los Angeles Department of Psychology cenders@psych.ucla.edu Background Work supported by Institute
Remember to SHOW ALL STEPS. You must be able to solve analytically. Answers are shown after each problem under A, B, C, or D.
Math 165 - Review Chapters 3 and 4 Name Remember to SHOW ALL STEPS. You must be able to solve analytically. Answers are shown after each problem under A, B, C, or D. Find the quadratic function satisfying
REGRESSION ANALYSIS : LINEAR BY MAUAJAMA FIRDAUS & TULIKA SAHA
REGRESSION ANALYSIS : LINEAR BY MAUAJAMA FIRDAUS & TULIKA SAHA MACHINE LEARNING It is the science of getting computer to learn without being explicitly programmed. Machine learning is an area of artificial
Bayes Factor Single Arm Binary User s Guide (Version 1.0)
Bayes Factor Single Arm Binary User s Guide (Version 1.0) Department of Biostatistics P. O. Box 301402, Unit 1409 The University of Texas, M. D. Anderson Cancer Center Houston, Texas 77230-1402, USA November
Module 4. Non-linear machine learning econometrics: Support Vector Machine
Module 4. Non-linear machine learning econometrics: Support Vector Machine THE CONTRACTOR IS ACTING UNDER A FRAMEWORK CONTRACT CONCLUDED WITH THE COMMISSION Introduction When the assumption of linearity
A Dendrogram. Bioinformatics (Lec 17)
A Dendrogram 3/15/05 1 Hierarchical Clustering [Johnson, SC, 1967] Given n points in R d, compute the distance between every pair of points While (not done) Pick closest pair of points s i and s j and
A Neuro Probabilistic Language Model Bengio et. al. 2003
A Neuro Probabilistic Language Model Bengio et. al. 2003 Class Discussion Notes Scribe: Olivia Winn February 1, 2016 Opening thoughts (or why this paper is interesting): Word embeddings currently have
Performance of Sequential Imputation Method in Multilevel Applications
Section on Survey Research Methods JSM 9 Performance of Sequential Imputation Method in Multilevel Applications Enxu Zhao, Recai M. Yucel New York State Department of Health, 8 N. Pearl St., Albany, NY
MATH3016: OPTIMIZATION
MATH3016: OPTIMIZATION Lecturer: Dr Huifu Xu School of Mathematics University of Southampton Highfield SO17 1BJ Southampton Email: h.xu@soton.ac.uk 1 Introduction What is optimization? Optimization is
R-Square Coeff Var Root MSE y Mean
STAT:50 Applied Statistics II Exam - Practice 00 possible points. Consider a -factor study where each of the factors has 3 levels. The factors are Diet (,,3) and Drug (A,B,C) and there are n = 3 observations
Chapter 7: Numerical Prediction
Ludwig-Maximilians-Universität München Institut für Informatik Lehr- und Forschungseinheit für Datenbanksysteme Knowledge Discovery in Databases SS 2016 Chapter 7: Numerical Prediction Lecture: Prof. Dr.
Analysis of Imputation Methods for Missing Data. in AR(1) Longitudinal Dataset
Int. Journal of Math. Analysis, Vol. 5, 2011, no. 45, 2217-2227 Analysis of Imputation Methods for Missing Data in AR(1) Longitudinal Dataset Michikazu Nakai Innovation Center for Medical Redox Navigation,
Supplementary methods
Supplementary methods This section provides additional technical details on the sample, the applied imaging and analysis steps and methods. Structural imaging Trained radiographers placed all participants
Missing Data and Imputation
Missing Data and Imputation Hoff Chapter 7, GH Chapter 25 April 21, 2017 Bednets and Malaria Y:presence or absence of parasites in a blood smear AGE: age of child BEDNET: bed net use (exposure) GREEN:greenness
HANDLING MISSING DATA
GSO international workshop Mathematic, biostatistics and epidemiology of cancer Modeling and simulation of clinical trials Gregory GUERNEC 1, Valerie GARES 1,2 1 UMR1027 INSERM UNIVERSITY OF TOULOUSE III
Multidimensional Item Response Theory (MIRT) University of Kansas Item Response Theory Stats Camp 07
Multidimensional Item Response Theory (MIRT) University of Kansas Item Response Theory Stats Camp 07 Overview Basics of MIRT Assumptions Models Applications Why MIRT? Many of the more sophisticated approaches
(Not That) Advanced Hierarchical Models
(Not That) Advanced Hierarchical Models Ben Goodrich StanCon: January 10, 2018 Ben Goodrich Advanced Hierarchical Models StanCon 1 / 13 Obligatory Disclosure Ben is an employee of Columbia University,
Clustering: Classic Methods and Modern Views
Clustering: Classic Methods and Modern Views Marina Meilă University of Washington mmp@stat.washington.edu June 22, 2015 Lorentz Center Workshop on Clusters, Games and Axioms Outline Paradigms for clustering
Missing Data. Where did it go?
Missing Data Where did it go? 1 Learning Objectives High-level discussion of some techniques Identify type of missingness Single vs Multiple Imputation My favourite technique 2 Problem Uh data are missing
Bayesian Analysis of Differential Gene Expression
Bayesian Analysis of Differential Gene Expression Biostat Journal Club Chuan Zhou chuan.zhou@vanderbilt.edu Department of Biostatistics Vanderbilt University Bayesian Modeling p. 1/1 Lewin et al., 2006
Edge-exchangeable graphs and sparsity
Edge-exchangeable graphs and sparsity Tamara Broderick Department of EECS Massachusetts Institute of Technology tbroderick@csail.mit.edu Diana Cai Department of Statistics University of Chicago dcai@uchicago.edu
60 2 Convex sets. {x a T x b} {x ã T x b}
60 2 Convex sets Exercises Definition of convexity 21 Let C R n be a convex set, with x 1,, x k C, and let θ 1,, θ k R satisfy θ i 0, θ 1 + + θ k = 1 Show that θ 1x 1 + + θ k x k C (The definition of convexity
Spring 2011 CSE Qualifying Exam Core Subjects. February 26, 2011
Spring 2011 CSE Qualifying Exam Core Subjects February 26, 2011 1 Architecture 1. Consider a memory system that has the following characteristics: bandwidth to lower level memory type line size miss rate
Multiple Imputation with Mplus
Multiple Imputation with Mplus Tihomir Asparouhov and Bengt Muthén Version 2 September 29, 2010 1 1 Introduction Conducting multiple imputation (MI) can sometimes be quite intricate. In this note we provide
UK COSMOS PARTICIPANT NEWSLETTER FEBRUARY 2014
UK COSMOS PARTICIPANT NEWSLETTER FEBRUARY 14 Welcome Another year has passed and we are pleased to be back in touch with our participants to share the exciting progress of UK COSMOS to date! COSMOS remains
7.2. The Standard Normal Distribution
7.2 The Standard Normal Distribution Standard Normal The standard normal curve is the one with mean μ = 0 and standard deviation σ = 1 We have related the general normal random variable to the standard
Modelling Proportions and Count Data
Modelling Proportions and Count Data Rick White May 4, 2016 Outline Analysis of Count Data Binary Data Analysis Categorical Data Analysis Generalized Linear Models Questions Types of Data Continuous data:
for Fast Clustering of Data on the Sphere
Supplement to A k-mean-directions Algorithm for Fast Clustering of Data on the Sphere Ranjan Maitra and Ivan P. Ramler S-1. ADDITIONAL EXPERIMENTAL EVALUATIONS The k-mean-directions algorithm developed
Lecture 3: Geometric and Signal 3D Processing (and some Visualization)
Lecture 3: Geometric and Signal 3D Processing (and some Visualization) Chandrajit Bajaj Algorithms & Tools Structure elucidation: filtering, contrast enhancement, segmentation, skeletonization, subunit
Visualization and text mining of patent and non-patent data
of patent and non-patent data Anton Heijs Information Solutions Delft, The Netherlands http://www.treparel.com/ ICIC conference, Nice, France, 2008 Outline Introduction Applications on patent and non-patent