Methods for Meta-analysis in Medical Research

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Methods for Meta-analysis in Medical Research"

Transcription

1 Methods for Meta-analysis in Medical Research Alex J. Sutton University of Leicester, UK Keith R. Abrams University of Leicester, UK David R. Jones University of Leicester, UK Trevor A. Sheldon University of York, UK Fujian Song University of York, UK JOHN WILEY & SONS, LTD Chichester New York Weinheim Brisbane Singapore Toronto

2 Contents Preface Acknowledgements Part A: Meta-Analysis Methodology: The Basics 1. Introduction - Meta-analysis: Its Development and Uses. 1 Evidence-based health care.2 Evidence-based everything!.3 Pulling together the evidence - systematic reviews.4 Why meta-analysis?.5 Aim of this book.6 Concluding remarks References XV xvii Deflning Outcome Measures used for Combining via Meta-analysis Introduction 2.2 Non-comparative binary outcomes Odds Incidence rates 2.3 Comparative binary outcomes The Odds ratio Relative risk (or rate ratio/relative rate) Risk differences between proportions (or the absolute risk reduction) The number needed to treat Comparisons of rates Other scales of measurement used in summarizing binary data Which scale to use?

3 vi CONTENTS 2.4 Continuous data Outcomes defined on their original metric (mean difference) Outcomes defined using standardized mean differences Ordinal outcomes Summary/Discussion 33 References Assessing Between Study Heterogeneity Introduction Hypothesis tests for presence of heterogeneity Standard x 2 test Extensions/alternative tests Example: Testing for heterogeneity in the cholesterol lowering trial dataset Graphical informal tests/explorations of heterogeneity Plot of normalized (z) scores Forest plot Radial plot (Galbraith diagram) VAbbe plot Possible causes of heterogeneity Specific factors that may cause heterogeneity in RCTs Methods for investigating and dealing with sources of heterogeneity Change scale of outcome variable Include covariates in a regression model (meta-regression) Exclude studies Analyse groups of studies separately Use of random effects models Use of mixed-effect models The validity of pooling studies with heterogeneous outcomes Summary/Discussion 53 References Fixed Effects Methods for Combining Study Estimates Introduction General fixed effect model - the inverse variance-weighted method Example: Combining odds ratios using the inverse variance-weighted method Example: Combining standardized mean differences using a continuous outcome scale 62

4 CONTENTS vii 4.3 Specific methods for combining odds ratios Mantel-Haenszel method for combining odds ratios Peto's method for combining odds ratios Combining odds ratios via maximum-likelihood techniques Exact methods of interval estimation Discussion of the relative merits of each method Summary/Discussion 70 References Random Effects Models for Combining Study Estimates Introduction Algebraic derivation for random effects models by the weighted method Maximum likelihood and restricted maximum likelihood estimate solutions Comparison of estimation methods Example: Combining the cholesterol lowering trials using a random effects model Extensions to the random effects model Including uncertainty induced by estimating the between study variance Exact approach to random effects meta-analysis of binary data Miscellaneous extensions to the random effects model Comparison of random with fixed effect models Summary/Discussion 84 References Exploring Between Study Heterogeneity Introduction Subgroup analyses Example: Stratification by study characteristics Example: Stratification by patient characteristics Regression models for meta-analysis Meta-regression models (fixed-effects regression) Meta-regression example: a meta-analysis of Bacillus Calmette-Guerin (BCG) vaccine for the prevention of tuberculosis (TB) Mixed effect models (random-effects regression) Mixed model example: A re-analysis of Bacillus Calmette-Guerin (BCG) vaccine for the prevention of tuberculosis (TB) trials 99

5 viii CONTENTS Mixed modelling extensions Summary/Discussion 104 References Publication Bias Introduction Evidence of publication and related bias Survey of authors Published versus registered trials in a meta-analysis Follow-up of cohorts of registered studies Non-empirical evidence Evidence of language bias The seriousness and consequences of publication bias for meta-analysis Predictors of publication bias (factors effecting the probability a study will get published) Identifying publication bias in a meta-analysis The funnel plot Rank correlation test Linear regression test Other methods to detect publication bias Practical advice on methods for detecting publication bias' Taking into account publication bias or adjusting the results of a meta-analysis in the presence of publication bias Analysing only the largest studies Rosenthal's 'file drawer' method Models which estimate the number of unpublished studies, but do not adjust Selection models using weighted distribution theory The'Trim and Fill'method The sensitivity approach of Copas Broader perspective solutions to publication bias Prospective registration of trials Changes in publication process and journals Including unpublished information Summary/Discussion 127 References Study Quality Introduction Methodological factors that may affect the quality of studies Experimental studies 135

6 CONTENTS ix Observational Studies Incorporating study quality into a meta-analysis Graphical plot Cumulative methods Regression model Weighting Excluding studies Sensitivity analysis Practical implementation Summary/Discussion 144 References Sensitivity Analysis Introduction Sensitivity of results to inclusion criteria Sensitivity of results to meta-analytic methods Assessing the impact of choice of study weighting Summary/Discussion 151 References Reporting the Results of a Meta-analysis Introduction Overview and structure of a report Graphical displays used for reporting the findings of a meta-analysis Forest plots Radial plots Funnel plots Displaying the distribution of effect size estimates Graphs investigating length of follow-up Summary/Discussion 158 References 158 Part B: Advanced and Specialized Meta-analysis Topics Bayesian Methods in Meta-analysis Introduction Bayesian methods in health research General introduction General advantages/disadvantages of Bayesian methods Example: Bayesian analysis of a single trial using a normal conjugate model 167

7 x CONTENTS 11.3 Bayesian meta-analysis of normally distributed data Example: Combining trials with continuous outcome measures using Bayesian methods Bayesian meta-analysis of binary data Example: Combining binary outcome measures using Bayesian methods Empirical Bayes methods in meta-analysis Advantages/disadvantages of Bayesian methods in meta-analysis Advantages Disadvantages Extensions and specific areas of application Incorporating study quality Inclusion of covariates Model selection Hierarchical models Sensitivity analysis Comprehensive modelling Other developments Summary/Discussion 183 References 183 / 12. Meta-analysis of Individual Patient Data Introduction Procedural methodology Data collection Checking data Issues involved in carrying out IPD meta-analyses Comparing meta-analysis using IPD or summary data? Combining individual patient and summary data Summary/Discussion 196 References Missing Data Introduction Reasons for missing data Categories of missing data at the study level Analytic methods for dealing with missing data General missing data methods which can be applied in the meta-analysis context Missing data methods specific to meta-analysis Example: Dealing with missing standard deviations of estimates in a meta-analysis 202

8 CONTENTS xi 13.5 Bayesian methods for missing data Summary/Discussion 203 References Meta-analysis of Different Types of Data Introduction Combining ordinal data Issues concerning scales of measurement when combining data Transforming scales, maintaining same data type Binary outcome data reported on different scales Combining studies whose outcomes are reported using different data types Combining summaries of binary outcomes with those of continuous outcomes Non-parametric method of combining different data type effect measures Meta-analysis of diagnostic test accuracy Combining binary test results Combining ordered categorical test results Combining continuous test results Meta-analysis using surrogate markers Combining a number of cross-over trials using the patient preference outcome Vote-counting methods Combining/7-values/significance levels Minimum p method Sum of z's method Sum of logs method Logit method Other methods of combining significance levels Appraisal of the methods Example of combining/7-values Novel applications of meta-analysis using non-standard methods or data Summary/Discussion 223 References Meta-analysis of Multiple and Correlated Outcome Measures Introduction Combining multiple/^-values Method for reducing multiple outcomes to a single measure for each study 231

9 xii CONTENTS 15.4 Development of a multivariate model Model of Raudenbush et al Model of Gleser and Olkin Multiple outcome model for clinical trials Random effect multiple outcome regression model DuMouchel's extended model for multiple outcomes Illustration of the use of multiple outcome models Summary/Discussion 236 References Meta-analysis of Epidemiological and Other Observational Studies Introduction Extraction and derivation of study estimates Scales of measurement used to report and combine observational studies Data manipulation for data extraction Methods for transforming and adjusting reported results Analysis of summary data Heterogeneity of observational studies Fixed or random effects? Weighting of observational studies Methods for combining estimates of observational studies Dealing with heterogeneity and combining the OC and breast cancer studies Reporting the results of meta-analysis of observational studies Use of sensitivity and influence analysis Study quality considerations for observational studies Other issues concerning meta-analysis of observational studies, Analysing individual patient data from observational studies Combining dose-response data Meta-analysis of single case research Unresolved issues concerning the meta-analysis of observational studies Summary/Discussion 255 References 255

10 CONTENTS xiii 17. Generalized Synthesis of Evidence - Combining Different Sources of Evidence Introduction Incorporating single-arm studies: models for incorporating historical controls Example Combining matched and unmatched data Approaches for combining studies containing multiple and/or different treatment arms Approach of Gleser and Olkin Models of Berkey et al Method of Higgins Mixed model of DuMouchel The confidence profile method Cross-design synthesis Beginnings Bayesian hierarchical models Grouped random effects models of Larose and Dey Synthesizing studies with disparate designs to assess the exposure effects on the incidence of a rare adverse event Combining the results of cancer studies in humans and other species Combining biochemical and epidemiological evidence Combining information from disparate toxicological studies using stratified ordinal regression Summary/Discussion 273 References Meta-analysis of Survival Data Introduction Inferring/estimating and combining (log) hazard ratios Calculation of the'log-rank'odds ratio Calculation of pooled survival rates Method of Hunink and Wong Iterative generalized least squares for meta-analysis of survival data at multiple times Application of the model Identifying prognostic factors using a log (relative risk) measure 282

11 xiv CONTENTS 18.8 Combining quality of life adjusted survival data Meta-analysis of survival data using individual patient data Pooling independent samples of survival data to form an estimator of the common survival function Is obtaining and using survival data necessary? Summary/Discussion 284 References Cumulative Meta-analysis Introduction Example: Ordering by date of publication Using study characteristics other than date of publication Example: Ordering the cholesterol trials by baseline risk in the control group Bayesian approaches Issues regarding uses of cumulative meta-analysis Summary/Discussion 292 References 292 / 20. Miscellaneous and Developing Areas of Application in Meta-analysis Introduction Alternatives to conventional meta-analysis Estimating and extrapolating a response surface Odd man out method Best evidence synthesis Developing areas Prospective meta-analysis Economic evaluation through meta-analysis ' Combining meta-analysis and decision analysis > Net benefit model synthesizing disparate sources of information 299 References 299 Appendix I: Software Used for the Examples in this Book 301 Subject index 309

Using Criteria to Appraise a Meta-analyses

Using Criteria to Appraise a Meta-analyses Using Criteria to Appraise a Meta-analyses Paul Cronin B.A., M.B. B.Ch. B.A.O., M.S., M.R.C.P.I.,.F.R.C.R. Department of Radiology, Division of Cardiothoracic Radiology, University of Michigan, Ann Arbor,

More information

When Does it Make Sense to Perform a Meta-Analysis?

When Does it Make Sense to Perform a Meta-Analysis? CHAPTER 40 When Does it Make Sense to Perform a Meta-Analysis? Introduction Are the studies similar enough to combine? Can I combine studies with different designs? How many studies are enough to carry

More information

Meta-Regression CHAPTER 20

Meta-Regression CHAPTER 20 CHAPTER 20 Meta-Regression Introduction Fixed-effect model Fixed or random effects for unexplained heterogeneity Random-effects model INTRODUCTION In primary studies we use regression, or multiple regression,

More information

A Bayesian hierarchical surrogate outcome model for multiple sclerosis

A Bayesian hierarchical surrogate outcome model for multiple sclerosis A Bayesian hierarchical surrogate outcome model for multiple sclerosis 3 rd Annual ASA New Jersey Chapter / Bayer Statistics Workshop David Ohlssen (Novartis), Luca Pozzi and Heinz Schmidli (Novartis)

More information

Statistical Rules of Thumb

Statistical Rules of Thumb Statistical Rules of Thumb Second Edition Gerald van Belle University of Washington Department of Biostatistics and Department of Environmental and Occupational Health Sciences Seattle, WA WILEY AJOHN

More information

INTRODUCTORY STATISTICS

INTRODUCTORY STATISTICS INTRODUCTORY STATISTICS FIFTH EDITION Thomas H. Wonnacott University of Western Ontario Ronald J. Wonnacott University of Western Ontario WILEY JOHN WILEY & SONS New York Chichester Brisbane Toronto Singapore

More information

Journal Article Reporting Standards (JARS)

Journal Article Reporting Standards (JARS) APPENDIX Journal Article Reporting Standards (JARS), Meta-Analysis Reporting Standards (MARS), and Flow of Participants Through Each Stage of an Experiment or Quasi-Experiment 245 Journal Article Reporting

More information

An Introduction to the Critical Appraisal Section (including example questions)

An Introduction to the Critical Appraisal Section (including example questions) An Introduction to the Critical Appraisal Section (including example questions) Introduction This section is common to the specialties of Dental Public Health, Oral Medicine, Oral Surgery, Orthodontics,

More information

Regression Modeling Strategies

Regression Modeling Strategies Frank E. Harrell, Jr. Regression Modeling Strategies With Applications to Linear Models, Logistic Regression, and Survival Analysis With 141 Figures Springer Contents Preface Typographical Conventions

More information

Fixed-Effect Versus Random-Effects Models

Fixed-Effect Versus Random-Effects Models CHAPTER 13 Fixed-Effect Versus Random-Effects Models Introduction Definition of a summary effect Estimating the summary effect Extreme effect size in a large study or a small study Confidence interval

More information

Systematic Reviews and Meta-analyses

Systematic Reviews and Meta-analyses Systematic Reviews and Meta-analyses Introduction A systematic review (also called an overview) attempts to summarize the scientific evidence related to treatment, causation, diagnosis, or prognosis of

More information

Applied Regression Analysis and Other Multivariable Methods

Applied Regression Analysis and Other Multivariable Methods THIRD EDITION Applied Regression Analysis and Other Multivariable Methods David G. Kleinbaum Emory University Lawrence L. Kupper University of North Carolina, Chapel Hill Keith E. Muller University of

More information

EBM Cheat Sheet- Measurements Card

EBM Cheat Sheet- Measurements Card EBM Cheat Sheet- Measurements Card Basic terms: Prevalence = Number of existing cases of disease at a point in time / Total population. Notes: Numerator includes old and new cases Prevalence is cross-sectional

More information

Effect Size Calculations and Elementary Meta-Analysis

Effect Size Calculations and Elementary Meta-Analysis Effect Size Calculations and Elementary Meta-Analysis David B. Wilson, PhD George Mason University August 2011 The End-Game Forest-Plot of Odds-Ratios and 95% Confidence Intervals for the Effects of Cognitive-Behavioral

More information

Multivariate Statistical Inference and Applications

Multivariate Statistical Inference and Applications Multivariate Statistical Inference and Applications ALVIN C. RENCHER Department of Statistics Brigham Young University A Wiley-Interscience Publication JOHN WILEY & SONS, INC. New York Chichester Weinheim

More information

Analysis of Microdata

Analysis of Microdata Rainer Winkelmann Stefan Boes Analysis of Microdata With 38 Figures and 41 Tables 4y Springer Contents 1 Introduction 1 1.1 What Are Microdata? 1 1.2 Types of Microdata 4 1.2.1 Qualitative Data 4 1.2.2

More information

Recent developments in meta-analysis

Recent developments in meta-analysis STATISTICS IN MEDICINE Statist. Med. 2008; 27:625 650 Published online 25 June 2007 in Wiley InterScience (www.interscience.wiley.com).2934 Recent developments in meta-analysis Alexander J. Sutton 1,,,

More information

Prognostic Outcome Studies

Prognostic Outcome Studies Prognostic Outcome Studies Edwin Chan PhD Singapore Clinical Research Institute Singapore Branch, Australasian Cochrane Centre Duke-NUS Graduate Medical School Contents Objectives Incidence studies (Prognosis)

More information

Study Design and Statistical Analysis

Study Design and Statistical Analysis Study Design and Statistical Analysis Anny H Xiang, PhD Department of Preventive Medicine University of Southern California Outline Designing Clinical Research Studies Statistical Data Analysis Designing

More information

DEVELOPING AN ANALYTICAL PLAN

DEVELOPING AN ANALYTICAL PLAN The Fundamentals of International Clinical Research Workshop February 2004 DEVELOPING AN ANALYTICAL PLAN Mario Chen, PhD. Family Health International 1 The Analysis Plan for a Study Summary Analysis Plan:

More information

Training Program in Meta-Analysis

Training Program in Meta-Analysis Training Program in Meta-Analysis June 23-25, 2015 The University of Arizona College of Pharmacy Tucson, Arizona A 3-day, hands-on training program for personnel in healthcare decisionmaking, industry

More information

Outline. Publication and other reporting biases; funnel plots and asymmetry tests. The dissemination of evidence...

Outline. Publication and other reporting biases; funnel plots and asymmetry tests. The dissemination of evidence... Cochrane Methodology Annual Training Assessing Risk Of Bias In Cochrane Systematic Reviews Loughborough UK, March 0 Publication and other reporting biases; funnel plots and asymmetry tests Outline Sources

More information

Data Analysis, Research Study Design and the IRB

Data Analysis, Research Study Design and the IRB Minding the p-values p and Quartiles: Data Analysis, Research Study Design and the IRB Don Allensworth-Davies, MSc Research Manager, Data Coordinating Center Boston University School of Public Health IRB

More information

Tips for surviving the analysis of survival data. Philip Twumasi-Ankrah, PhD

Tips for surviving the analysis of survival data. Philip Twumasi-Ankrah, PhD Tips for surviving the analysis of survival data Philip Twumasi-Ankrah, PhD Big picture In medical research and many other areas of research, we often confront continuous, ordinal or dichotomous outcomes

More information

Statistics Graduate Courses

Statistics Graduate Courses Statistics Graduate Courses STAT 7002--Topics in Statistics-Biological/Physical/Mathematics (cr.arr.).organized study of selected topics. Subjects and earnable credit may vary from semester to semester.

More information

Institute of Actuaries of India Subject CT3 Probability and Mathematical Statistics

Institute of Actuaries of India Subject CT3 Probability and Mathematical Statistics Institute of Actuaries of India Subject CT3 Probability and Mathematical Statistics For 2015 Examinations Aim The aim of the Probability and Mathematical Statistics subject is to provide a grounding in

More information

Introduction to Meta-Analysis

Introduction to Meta-Analysis Introduction to Meta-Analysis Michael Borenstein Larry Hedges Hannah Rothstein www.meta-analysis.com Page 1 Dedication Dedicated in honor of Sir Iain Chalmers and to the memory of Dr. Thomas Chalmers.

More information

CONTENTS PREFACE 1 INTRODUCTION 1 2 DATA VISUALIZATION 19

CONTENTS PREFACE 1 INTRODUCTION 1 2 DATA VISUALIZATION 19 PREFACE xi 1 INTRODUCTION 1 1.1 Overview 1 1.2 Definition 1 1.3 Preparation 2 1.3.1 Overview 2 1.3.2 Accessing Tabular Data 3 1.3.3 Accessing Unstructured Data 3 1.3.4 Understanding the Variables and Observations

More information

Univariate and Multivariate Methods PEARSON. Addison Wesley

Univariate and Multivariate Methods PEARSON. Addison Wesley Time Series Analysis Univariate and Multivariate Methods SECOND EDITION William W. S. Wei Department of Statistics The Fox School of Business and Management Temple University PEARSON Addison Wesley Boston

More information

The Joanna Briggs Institute Reviewers Manual 2014. The Systematic Review of Prevalence and Incidence Data

The Joanna Briggs Institute Reviewers Manual 2014. The Systematic Review of Prevalence and Incidence Data The Joanna Briggs Institute Reviewers Manual 2014 The Systematic Review of Prevalence and Incidence Data Joanna Briggs Institute Reviewers Manual: 2014 edition/supplement Copyright The Joanna Briggs Institute

More information

Adequacy of Biomath. Models. Empirical Modeling Tools. Bayesian Modeling. Model Uncertainty / Selection

Adequacy of Biomath. Models. Empirical Modeling Tools. Bayesian Modeling. Model Uncertainty / Selection Directions in Statistical Methodology for Multivariable Predictive Modeling Frank E Harrell Jr University of Virginia Seattle WA 19May98 Overview of Modeling Process Model selection Regression shape Diagnostics

More information

MULTIVARIATE DATA ANALYSIS i.-*.'.. ' -4

MULTIVARIATE DATA ANALYSIS i.-*.'.. ' -4 SEVENTH EDITION MULTIVARIATE DATA ANALYSIS i.-*.'.. ' -4 A Global Perspective Joseph F. Hair, Jr. Kennesaw State University William C. Black Louisiana State University Barry J. Babin University of Southern

More information

Survival Analysis Using SPSS. By Hui Bian Office for Faculty Excellence

Survival Analysis Using SPSS. By Hui Bian Office for Faculty Excellence Survival Analysis Using SPSS By Hui Bian Office for Faculty Excellence Survival analysis What is survival analysis Event history analysis Time series analysis When use survival analysis Research interest

More information

Organizing Your Approach to a Data Analysis

Organizing Your Approach to a Data Analysis Biost/Stat 578 B: Data Analysis Emerson, September 29, 2003 Handout #1 Organizing Your Approach to a Data Analysis The general theme should be to maximize thinking about the data analysis and to minimize

More information

13. Poisson Regression Analysis

13. Poisson Regression Analysis 136 Poisson Regression Analysis 13. Poisson Regression Analysis We have so far considered situations where the outcome variable is numeric and Normally distributed, or binary. In clinical work one often

More information

A simple method for converting an odds ratio to eect size for use in meta-analysis

A simple method for converting an odds ratio to eect size for use in meta-analysis STATISTICS IN MEDICINE Statist. Med. 2000; 19:3127 3131 A simple method for converting an odds ratio to eect size for use in meta-analysis Susan Chinn ; Department of Public Health Sciences; King s College;

More information

IBM SPSS Complex Samples 22

IBM SPSS Complex Samples 22 IBM SPSS Complex Samples 22 Note Before using this information and the product it supports, read the information in Notices on page 51. Product Information This edition applies to version 22, release 0,

More information

Service courses for graduate students in degree programs other than the MS or PhD programs in Biostatistics.

Service courses for graduate students in degree programs other than the MS or PhD programs in Biostatistics. Course Catalog In order to be assured that all prerequisites are met, students must acquire a permission number from the education coordinator prior to enrolling in any Biostatistics course. Courses are

More information

Semester 1 Statistics Short courses

Semester 1 Statistics Short courses Semester 1 Statistics Short courses Course: STAA0001 Basic Statistics Blackboard Site: STAA0001 Dates: Sat. March 12 th and Sat. April 30 th (9 am 5 pm) Assumed Knowledge: None Course Description Statistical

More information

More details on the inputs, functionality, and output can be found below.

More details on the inputs, functionality, and output can be found below. Overview: The SMEEACT (Software for More Efficient, Ethical, and Affordable Clinical Trials) web interface (http://research.mdacc.tmc.edu/smeeactweb) implements a single analysis of a two-armed trial comparing

More information

Computer-Aided Multivariate Analysis

Computer-Aided Multivariate Analysis Computer-Aided Multivariate Analysis FOURTH EDITION Abdelmonem Af if i Virginia A. Clark and Susanne May CHAPMAN & HALL/CRC A CRC Press Company Boca Raton London New York Washington, D.C Contents Preface

More information

UNDERSTANDING CLINICAL TRIAL STATISTICS. Prepared by Urania Dafni, Xanthi Pedeli, Zoi Tsourti

UNDERSTANDING CLINICAL TRIAL STATISTICS. Prepared by Urania Dafni, Xanthi Pedeli, Zoi Tsourti UNDERSTANDING CLINICAL TRIAL STATISTICS Prepared by Urania Dafni, Xanthi Pedeli, Zoi Tsourti DISCLOSURES Urania Dafni has reported no conflict of interest Xanthi Pedeli has reported no conflict of interest

More information

Guide to Biostatistics

Guide to Biostatistics MedPage Tools Guide to Biostatistics Study Designs Here is a compilation of important epidemiologic and common biostatistical terms used in medical research. You can use it as a reference guide when reading

More information

LEVEL ONE MODULE EXAM PART ONE [Clinical Questions Literature Searching Types of Research Levels of Evidence Appraisal Scales Statistic Terminology]

LEVEL ONE MODULE EXAM PART ONE [Clinical Questions Literature Searching Types of Research Levels of Evidence Appraisal Scales Statistic Terminology] 1. What does the letter I correspond to in the PICO format? A. Interdisciplinary B. Interference C. Intersession D. Intervention 2. Which step of the evidence-based practice process incorporates clinical

More information

THE CERTIFIED SIX SIGMA BLACK BELT HANDBOOK

THE CERTIFIED SIX SIGMA BLACK BELT HANDBOOK THE CERTIFIED SIX SIGMA BLACK BELT HANDBOOK SECOND EDITION T. M. Kubiak Donald W. Benbow ASQ Quality Press Milwaukee, Wisconsin Table of Contents list of Figures and Tables Preface to the Second Edition

More information

Module 223 Major A: Concepts, methods and design in Epidemiology

Module 223 Major A: Concepts, methods and design in Epidemiology Module 223 Major A: Concepts, methods and design in Epidemiology Module : 223 UE coordinator Concepts, methods and design in Epidemiology Dates December 15 th to 19 th, 2014 Credits/ECTS UE description

More information

Describe what is meant by a placebo Contrast the double-blind procedure with the single-blind procedure Review the structure for organizing a memo

Describe what is meant by a placebo Contrast the double-blind procedure with the single-blind procedure Review the structure for organizing a memo Readings: Ha and Ha Textbook - Chapters 1 8 Appendix D & E (online) Plous - Chapters 10, 11, 12 and 14 Chapter 10: The Representativeness Heuristic Chapter 11: The Availability Heuristic Chapter 12: Probability

More information

Lecture - 32 Regression Modelling Using SPSS

Lecture - 32 Regression Modelling Using SPSS Applied Multivariate Statistical Modelling Prof. J. Maiti Department of Industrial Engineering and Management Indian Institute of Technology, Kharagpur Lecture - 32 Regression Modelling Using SPSS (Refer

More information

Critical appraisal form

Critical appraisal form Assessing scientific admissibility and merit of published articles Critical appraisal form Section A: Reference of Article Author(s) and Affiliation(s): Title of Article: Journal: Volume and Page Numbers:

More information

Empirical Model-Building and Response Surfaces

Empirical Model-Building and Response Surfaces Empirical Model-Building and Response Surfaces GEORGE E. P. BOX NORMAN R. DRAPER Technische Universitat Darmstadt FACHBEREICH INFORMATIK BIBLIOTHEK Invortar-Nf.-. Sachgsbiete: Standort: New York John Wiley

More information

Correctly Compute Complex Samples Statistics

Correctly Compute Complex Samples Statistics SPSS Complex Samples 16.0 Specifications Correctly Compute Complex Samples Statistics When you conduct sample surveys, use a statistics package dedicated to producing correct estimates for complex sample

More information

Basic Biostatistics for Clinical Research. Ramses F Sadek, PhD GRU Cancer Center

Basic Biostatistics for Clinical Research. Ramses F Sadek, PhD GRU Cancer Center Basic Biostatistics for Clinical Research Ramses F Sadek, PhD GRU Cancer Center 1 1. Basic Concepts 2. Data & Their Presentation Part One 2 1. Basic Concepts Statistics Biostatistics Populations and samples

More information

SPSS TRAINING SESSION 3 ADVANCED TOPICS (PASW STATISTICS 17.0) Sun Li Centre for Academic Computing lsun@smu.edu.sg

SPSS TRAINING SESSION 3 ADVANCED TOPICS (PASW STATISTICS 17.0) Sun Li Centre for Academic Computing lsun@smu.edu.sg SPSS TRAINING SESSION 3 ADVANCED TOPICS (PASW STATISTICS 17.0) Sun Li Centre for Academic Computing lsun@smu.edu.sg IN SPSS SESSION 2, WE HAVE LEARNT: Elementary Data Analysis Group Comparison & One-way

More information

fifty Fathoms Statistics Demonstrations for Deeper Understanding Tim Erickson

fifty Fathoms Statistics Demonstrations for Deeper Understanding Tim Erickson fifty Fathoms Statistics Demonstrations for Deeper Understanding Tim Erickson Contents What Are These Demos About? How to Use These Demos If This Is Your First Time Using Fathom Tutorial: An Extended Example

More information

Econometric Analysis of Cross Section and Panel Data Second Edition. Jeffrey M. Wooldridge. The MIT Press Cambridge, Massachusetts London, England

Econometric Analysis of Cross Section and Panel Data Second Edition. Jeffrey M. Wooldridge. The MIT Press Cambridge, Massachusetts London, England Econometric Analysis of Cross Section and Panel Data Second Edition Jeffrey M. Wooldridge The MIT Press Cambridge, Massachusetts London, England Preface Acknowledgments xxi xxix I INTRODUCTION AND BACKGROUND

More information

STATISTICA Formula Guide: Logistic Regression. Table of Contents

STATISTICA Formula Guide: Logistic Regression. Table of Contents : Table of Contents... 1 Overview of Model... 1 Dispersion... 2 Parameterization... 3 Sigma-Restricted Model... 3 Overparameterized Model... 4 Reference Coding... 4 Model Summary (Summary Tab)... 5 Summary

More information

Basic research methods. Basic research methods. Question: BRM.2. Question: BRM.1

Basic research methods. Basic research methods. Question: BRM.2. Question: BRM.1 BRM.1 The proportion of individuals with a particular disease who die from that condition is called... BRM.2 This study design examines factors that may contribute to a condition by comparing subjects

More information

STA-201-TE. 5. Measures of relationship: correlation (5%) Correlation coefficient; Pearson r; correlation and causation; proportion of common variance

STA-201-TE. 5. Measures of relationship: correlation (5%) Correlation coefficient; Pearson r; correlation and causation; proportion of common variance Principles of Statistics STA-201-TE This TECEP is an introduction to descriptive and inferential statistics. Topics include: measures of central tendency, variability, correlation, regression, hypothesis

More information

Web appendix: Supplementary material. Appendix 1 (on-line): Medline search strategy

Web appendix: Supplementary material. Appendix 1 (on-line): Medline search strategy Web appendix: Supplementary material Appendix 1 (on-line): Medline search strategy exp Venous Thrombosis/ Deep vein thrombosis.mp. Pulmonary embolism.mp. or exp Pulmonary Embolism/ recurrent venous thromboembolism.mp.

More information

An Introduction to Meta-analysis

An Introduction to Meta-analysis SPORTSCIENCE Perspectives / Research Resources An Introduction to Meta-analysis Will G Hopkins sportsci.org Sportscience 8, 20-24, 2004 (sportsci.org/jour/04/wghmeta.htm) Sport and Recreation, Auckland

More information

training programme in pharmaceutical medicine Clinical Data Management and Analysis

training programme in pharmaceutical medicine Clinical Data Management and Analysis training programme in pharmaceutical medicine Clinical Data Management and Analysis 19-21 may 2011 Clinical Data Management and Analysis 19 21 MAY 2011 LocaL: University of Aveiro, Campus Universitário

More information

If several different trials are mentioned in one publication, the data of each should be extracted in a separate data extraction form.

If several different trials are mentioned in one publication, the data of each should be extracted in a separate data extraction form. General Remarks This template of a data extraction form is intended to help you to start developing your own data extraction form, it certainly has to be adapted to your specific question. Delete unnecessary

More information

Personalized Predictive Medicine and Genomic Clinical Trials

Personalized Predictive Medicine and Genomic Clinical Trials Personalized Predictive Medicine and Genomic Clinical Trials Richard Simon, D.Sc. Chief, Biometric Research Branch National Cancer Institute http://brb.nci.nih.gov brb.nci.nih.gov Powerpoint presentations

More information

Overview, Strengths, and Limitations of Systematic Reviews and Meta-Analyses

Overview, Strengths, and Limitations of Systematic Reviews and Meta-Analyses Overview, Strengths, and Limitations of Systematic Reviews and Meta-Analyses 2 Alfred A. Bartolucci and William B. Hillegass Core Message It is important and timely to provide some historical context,

More information

Meniscal Tear and Osteathritis

Meniscal Tear and Osteathritis Surgery versus Physical Therapy for a Meniscal Tear and Osteathritis To assess the efficacy of arthroscopic partial meniscectomy as compared with a standardized physical-therapy regime. Group 1: Inger

More information

Perspectives on Pooled Data Analysis: the Case for an Integrated Approach

Perspectives on Pooled Data Analysis: the Case for an Integrated Approach Journal of Data Science 9(2011), 389-397 Perspectives on Pooled Data Analysis: the Case for an Integrated Approach Demissie Alemayehu Pfizer, Inc. Abstract: In the absence of definitive trials on the safety

More information

SAMPLE SIZE TABLES FOR LOGISTIC REGRESSION

SAMPLE SIZE TABLES FOR LOGISTIC REGRESSION STATISTICS IN MEDICINE, VOL. 8, 795-802 (1989) SAMPLE SIZE TABLES FOR LOGISTIC REGRESSION F. Y. HSIEH* Department of Epidemiology and Social Medicine, Albert Einstein College of Medicine, Bronx, N Y 10461,

More information

Comparative Tolerability and Harms of Individual Statins

Comparative Tolerability and Harms of Individual Statins Comparative Tolerability and Harms of Individual Statins J.J.Brugts MD PhD MSc(1); H. Naci PhD MHS(2); T. Ades PhD(2) (1) Department of Cardiology, Erasmus MC, Rotterdam, the Netherlands (2) London School

More information

Pooling and Meta-analysis. Tony O Hagan

Pooling and Meta-analysis. Tony O Hagan Pooling and Meta-analysis Tony O Hagan Pooling Synthesising prior information from several experts 2 Multiple experts The case of multiple experts is important When elicitation is used to provide expert

More information

Applied Multivariate Analysis

Applied Multivariate Analysis Neil H. Timm Applied Multivariate Analysis With 42 Figures Springer Contents Preface Acknowledgments List of Tables List of Figures vii ix xix xxiii 1 Introduction 1 1.1 Overview 1 1.2 Multivariate Models

More information

Graduate Programs in Statistics

Graduate Programs in Statistics Graduate Programs in Statistics Course Titles STAT 100 CALCULUS AND MATR IX ALGEBRA FOR STATISTICS. Differential and integral calculus; infinite series; matrix algebra STAT 195 INTRODUCTION TO MATHEMATICAL

More information

II. DISTRIBUTIONS distribution normal distribution. standard scores

II. DISTRIBUTIONS distribution normal distribution. standard scores Appendix D Basic Measurement And Statistics The following information was developed by Steven Rothke, PhD, Department of Psychology, Rehabilitation Institute of Chicago (RIC) and expanded by Mary F. Schmidt,

More information

Sample Size Planning, Calculation, and Justification

Sample Size Planning, Calculation, and Justification Sample Size Planning, Calculation, and Justification Theresa A Scott, MS Vanderbilt University Department of Biostatistics theresa.scott@vanderbilt.edu http://biostat.mc.vanderbilt.edu/theresascott Theresa

More information

Statistics for Biology and Health

Statistics for Biology and Health Statistics for Biology and Health Series Editors M. Gail, K. Krickeberg, J.M. Samet, A. Tsiatis, W. Wong For further volumes: http://www.springer.com/series/2848 David G. Kleinbaum Mitchel Klein Survival

More information

FOCUSSED CLINICAL QUESTION:

FOCUSSED CLINICAL QUESTION: The use of a multifactorial falls risk assessment and management plan reduces the risk of falling and the monthly fall rate of older adults, and is the most effective component of a falls prevention programme

More information

Understanding, appraising and reporting meta-analyses that use individual participant data

Understanding, appraising and reporting meta-analyses that use individual participant data Understanding, appraising and reporting meta-analyses that use individual participant data Jayne Tierney, Claire Vale, Maroeska Rovers, Lesley Stewart IPD Meta-analysis Methods Group 21 st Annual Cochrane

More information

Critical Appraisal of a Randomized Trial

Critical Appraisal of a Randomized Trial Critical Appraisal of a Randomized Trial Presented by Brian S. Alper, MD, MSPH, FAAFP Founder of DynaMed; Vice President of Evidence-Based Medicine Research and Development, Quality and Standards Introduction

More information

11/20/2014. Correlational research is used to describe the relationship between two or more naturally occurring variables.

11/20/2014. Correlational research is used to describe the relationship between two or more naturally occurring variables. Correlational research is used to describe the relationship between two or more naturally occurring variables. Is age related to political conservativism? Are highly extraverted people less afraid of rejection

More information

Size of a study. Chapter 15

Size of a study. Chapter 15 Size of a study 15.1 Introduction It is important to ensure at the design stage that the proposed number of subjects to be recruited into any study will be appropriate to answer the main objective(s) of

More information

Principles of Systematic Review: Focus on Alcoholism Treatment

Principles of Systematic Review: Focus on Alcoholism Treatment Principles of Systematic Review: Focus on Alcoholism Treatment Manit Srisurapanont, M.D. Professor of Psychiatry Department of Psychiatry, Faculty of Medicine, Chiang Mai University For Symposium 1A: Systematic

More information

Critical Appraisal of Article on Therapy

Critical Appraisal of Article on Therapy Critical Appraisal of Article on Therapy What question did the study ask? Guide Are the results Valid 1. Was the assignment of patients to treatments randomized? And was the randomization list concealed?

More information

Guided Reading 9 th Edition. informed consent, protection from harm, deception, confidentiality, and anonymity.

Guided Reading 9 th Edition. informed consent, protection from harm, deception, confidentiality, and anonymity. Guided Reading Educational Research: Competencies for Analysis and Applications 9th Edition EDFS 635: Educational Research Chapter 1: Introduction to Educational Research 1. List and briefly describe the

More information

META-ANALYSIS OF ECONOMICS RESEARCH REPORTING GUIDELINES

META-ANALYSIS OF ECONOMICS RESEARCH REPORTING GUIDELINES doi: 10.1111/joes.12008 META-ANALYSIS OF ECONOMICS RESEARCH REPORTING GUIDELINES T.D. Stanley Hendrix College Hristos Doucouliagos School of Accounting, Economics and Finance, Deakin University Margaret

More information

Always Start with PECO

Always Start with PECO Goals of This Course Be able to understand a study design (very basic concept) Be able to understand statistical concepts in a medical paper Be able to perform a data analysis Understanding: PECO study

More information

Advanced Topics in Statistical Process Control

Advanced Topics in Statistical Process Control Advanced Topics in Statistical Process Control The Power of Shewhart s Charts Second Edition Donald J. Wheeler SPC Press Knoxville, Tennessee Contents Preface to the Second Edition Preface The Shewhart

More information

Dealing with Missing Data

Dealing with Missing Data Dealing with Missing Data Roch Giorgi email: roch.giorgi@univ-amu.fr UMR 912 SESSTIM, Aix Marseille Université / INSERM / IRD, Marseille, France BioSTIC, APHM, Hôpital Timone, Marseille, France January

More information

Overview Classes. 12-3 Logistic regression (5) 19-3 Building and applying logistic regression (6) 26-3 Generalizations of logistic regression (7)

Overview Classes. 12-3 Logistic regression (5) 19-3 Building and applying logistic regression (6) 26-3 Generalizations of logistic regression (7) Overview Classes 12-3 Logistic regression (5) 19-3 Building and applying logistic regression (6) 26-3 Generalizations of logistic regression (7) 2-4 Loglinear models (8) 5-4 15-17 hrs; 5B02 Building and

More information

Graphical Analysis for Epidemiology, Manufacturing and Marketing Applications. Andrew Berridge, Michael O Connell TIBCO Data Science

Graphical Analysis for Epidemiology, Manufacturing and Marketing Applications. Andrew Berridge, Michael O Connell TIBCO Data Science Graphical Analysis for Epidemiology, Manufacturing and Marketing Applications Andrew Berridge, Michael O Connell TIBCO Data Science Agenda Graphical Analysis for Epidemiology, Manufacturing and Marketing

More information

Economic Order Quantity and Economic Production Quantity Models for Inventory Management

Economic Order Quantity and Economic Production Quantity Models for Inventory Management Economic Order Quantity and Economic Production Quantity Models for Inventory Management Inventory control is concerned with minimizing the total cost of inventory. In the U.K. the term often used is stock

More information

CHAPTER 3 EXAMPLES: REGRESSION AND PATH ANALYSIS

CHAPTER 3 EXAMPLES: REGRESSION AND PATH ANALYSIS Examples: Regression And Path Analysis CHAPTER 3 EXAMPLES: REGRESSION AND PATH ANALYSIS Regression analysis with univariate or multivariate dependent variables is a standard procedure for modeling relationships

More information

Secondary Data &Meta-Analysis

Secondary Data &Meta-Analysis Secondary Data &Meta-Analysis Secondary Data - Overview Secondary data: data that have already been collected for a different purpose, but may be relevant to the research problems at hand. Primary data:

More information

Department/Academic Unit: Public Health Sciences Degree Program: Biostatistics Collaborative Program

Department/Academic Unit: Public Health Sciences Degree Program: Biostatistics Collaborative Program Department/Academic Unit: Public Health Sciences Degree Program: Biostatistics Collaborative Program Department of Mathematics and Statistics Degree Level Expectations, Learning Outcomes, Indicators of

More information

Overview. EBCP Terminology

Overview. EBCP Terminology EBCP Terminology Absolute risk reduction (ARR) The absolute arithmetic difference in rates of outcomes between experimental and control groups in a trial, calculated as EER-CER. Bias A systematic tendency

More information

Improving the Performance of Data Mining Models with Data Preparation Using SAS Enterprise Miner Ricardo Galante, SAS Institute Brasil, São Paulo, SP

Improving the Performance of Data Mining Models with Data Preparation Using SAS Enterprise Miner Ricardo Galante, SAS Institute Brasil, São Paulo, SP Improving the Performance of Data Mining Models with Data Preparation Using SAS Enterprise Miner Ricardo Galante, SAS Institute Brasil, São Paulo, SP ABSTRACT In data mining modelling, data preparation

More information

Statistical methods for the comparison of dietary intake

Statistical methods for the comparison of dietary intake Appendix Y Statistical methods for the comparison of dietary intake Jianhua Wu, Petros Gousias, Nida Ziauddeen, Sonja Nicholson and Ivonne Solis- Trapala Y.1 Introduction This appendix provides an outline

More information

Types of Studies. Systematic Reviews and Meta-Analyses

Types of Studies. Systematic Reviews and Meta-Analyses Types of Studies Systematic Reviews and Meta-Analyses Important medical questions are typically studied more than once, often by different research teams in different locations. A systematic review is

More information

Mortality Assessment Technology: A New Tool for Life Insurance Underwriting

Mortality Assessment Technology: A New Tool for Life Insurance Underwriting Mortality Assessment Technology: A New Tool for Life Insurance Underwriting Guizhou Hu, MD, PhD BioSignia, Inc, Durham, North Carolina Abstract The ability to more accurately predict chronic disease morbidity

More information

Yiming Peng, Department of Statistics. February 12, 2013

Yiming Peng, Department of Statistics. February 12, 2013 Regression Analysis Using JMP Yiming Peng, Department of Statistics February 12, 2013 2 Presentation and Data http://www.lisa.stat.vt.edu Short Courses Regression Analysis Using JMP Download Data to Desktop

More information

PS 271B: Quantitative Methods II. Lecture Notes

PS 271B: Quantitative Methods II. Lecture Notes PS 271B: Quantitative Methods II Lecture Notes Langche Zeng zeng@ucsd.edu The Empirical Research Process; Fundamental Methodological Issues 2 Theory; Data; Models/model selection; Estimation; Inference.

More information

Nursing Journal Toolkit: Critiquing a Quantitative Research Article

Nursing Journal Toolkit: Critiquing a Quantitative Research Article A Virtual World Consortium: Using Second Life to Facilitate Nursing Journal Clubs Nursing Journal Toolkit: Critiquing a Quantitative Research Article 1. Guidelines for Critiquing a Quantitative Research

More information