Challenges in Longitudinal Data Analysis: Baseline Adjustment, Missing Data, and Drop-out
|
|
- Lynette Eaton
- 7 years ago
- Views:
Transcription
1 Challenges in Longitudinal Data Analysis: Baseline Adjustment, Missing Data, and Drop-out Sandra Taylor, Ph.D. IDDRC BBRD Core 23 April 2014
2 Objectives Baseline Adjustment Introduce approaches Guidance on when to use different approaches Missing Data/Drop-out Raise awareness regarding issues/challenges caused by missing data Importance for study design and data analysis Basic understanding of approaches to handling with missing data
3
4 In longitudinal studies, subjects typically have a baseline measurement Interest is commonly on differences in change over time between groups Does the degree of change differ between groups? Differences in starting values (i.e., baseline) important to consider when trying to assess change over time Time
5 Four options for baseline adjustment 1. Retain baseline value as outcome with no assumptions about group differences at baseline 2. Retain baseline value as outcome and assume group means are equal at baseline 3. Subtract baseline from post baseline responses and analyze differences from baseline 4. Include baseline value as a covariate.
6 Retain baseline as outcome; No assumptions at baseline Group 1 Group 2 Time Allow intercepts (baselines) to differ between groups
7 Retain baseline as outcome; Assume equal at baseline Group 1 Group 2 Time Assume same intercepts (baselines) in both groups
8 Subtract baseline from post-baseline responses Define new variable as response variable Model as before Interpretation of results a bit different Group Are there differences at time 2? Group Time Are the lines parallel from time 2 to n? Joint test of Group and Group Time required to evaluate whether the patterns of change are the same over time
9 Use Baseline as covariate Outcome becomes adjusted change scores (i.e., change over time adjusted for baseline) Similar interpretation issues as Approach 3
10 Relationship Among Approaches Retain baseline as outcome? YES NO Assume equal means at baseline? Analyze change from baseline Include baseline as covariate YES NO Approach 1 Approach 2 Approach 3 Approach 4
11 Which approach to use? Randomized or Observational Study? If randomized, reasonable to assume equal baseline values across groups Approach 2 If observational Approach 2 if reasonable to assume equal baseline values across groups Approach 1 if baseline values differ across groups Approaches 3 and 4 applicable where Approaches 1 and 2 are applicable, respectively.
12 What is it? What does it matter? What do we do about it?
13 What are missing data and drop-out? Missing Data Observations researcher was to collect but didn t Many different causes for missing data Not specific to longitudinal data but common Drop-out Subjects leave a study before the intended end Special class of missing data unique to longitudinal data
14 What does it matter? Potential for bias and incorrect inferences Bias can be severe Loss of information/power Reduced precision and efficiency of estimates relative to complete data Data are unbalanced over time Problem for some analytical methods
15 Six Cities Study of Air Pollution and Health Hypothetical Weight Loss Study Muscatine Coronary Risk Factor Study
16 Six Cities Study of Air Pollution and Health Objective: Characterize lung function growth in children Enrolled 1 st /2 nd grade, followed until graduation Annual lung function tests Wide range (1-12) of observations per child Late enrollment moved into school district after 2 nd grade Drop out moved out of school district Consider reasons for moving out of district
17 Hypothetical Weight Loss Study Objective: Determine if coached program is more effective than on-line program Randomize subjects to each program Collect weight weekly for 3 months Types of missing values Drop-out: missing all values after time t Missing observation: missing one or more observations in the middle of the study What could cause the missing values?
18 Muscatine Coronary Risk Factor Study Objective: Examine development and persistence of coronary disease risk factors Children aged 5-15 Measured height and weight biennially; classified children as obese or not Parental consent required for each measurement Less 40% of children with complete data What factors contribute to missing values? No consent form Child absent from school on day of measurements
19 Missing Data Mechanisms 3 types distinguished based on relationship between the probability of missingness and the actual values (observed or unobserved) Missing Completely at Random (MCAR) Missing at Random (MAR) Not Missing at Random (NMAR) Mechanisms have different assumptions and methods for adequately handling missing values differ among the mechanisms
20 Missing Completely at Random Probability of missing response is unrelated to The value of the response had it been obtained The value of observed responses Examples: Missed appointment due to car trouble Variables measured on a subset of subjects by study design Missingness is simply chance event unrelated to any of the data observed or unobserved Observed data can be considered random sample of the complete data
21 Missing at Random Probability of missing response depends on the set of observed responses but unrelated to the specific missing value that would have been observed Examples: Removal of subject from study once pre-specified value obtained by study design Higher educated people don t report income Observed data can NOT be considered random sample of the complete data
22 Not Missing at Random Probability of missing response is related to the specific values that would have been obtained Examples Value is below the detection limit People with higher incomes don t report income Subjects skips appointment because of weight gain Missingness is non-ignorable
23 Revisit Examples Weight Loss Study Moves out of area - MCAR Achieves goal weight MAR or MNAR Not losing weight MAR or MNAR Air Pollution and Health Study Job relocation MCAR Child developed respiratory problems MAR Avoid developing respiratory problems MNAR Coronary Risk Factor Study Forgot to sign consent - MCAR Obese child feigns illness to avoid weighing MNAR
24 Approaches to Handling Missing Data Deletion Methods Complete-case analysis (listwise deletion) Available-data analysis (pairwise deletion) Single Imputation Methods Model-Based Methods Multiple imputation Maximum likelihood
25 Deletion Methods Complete-Case Analysis Only analyze subjects with complete data Available-Data Analysis Analyzing all data that was observed Different analytical methods can handle partial data (e.g., random effect models) More efficient/power than complete case because uses more information
26 Deletion Methods Advantages and Disadvantages Advantages Simple; available-data analysis is default for statistics programs Disadvantages Reduced sample size Complete-case analysis discards data Biased estimates unless data is MCAR
27 Single Imputation Substitute missing values with an imputed value Analyze complete data using standard methods Many different approaches to single imputation
28 Single Imputation Methods Mean value imputation Substitute mean value for missing value Last value carried forward imputation Use last value observed Regression imputation Replaces missing value with value predicted from regression derived from observed data K-nearest neighbor imputation Impute value based on k most similar subjects
29 Single Imputation Methods Advantages and Disadvantages Advantages Simple to implement and understand Maintains sample size Uses all available information Disadvantages Can reduce variability in the data Can weaken correlations/covariances Reduce standard errors because it doesn t reflect the uncertainty about the predicted unknown values
30 Maximum Likelihood Parameters estimated based on maximum likelihood using available data Random effect models implement this approach Advantages Uses all available information Unbiased estimates for MCAR and MAR data Disadvantages Model must be correctly specified
31 Multiple Imputation Missing values are imputed from a model (e.g., regression model) Imputation conducted multiple times Replacing missing value with a set of plausible values Each imputed data is analyzed Results from analysis of each imputed data set are pooled into single estimate
32 Multiple Imputation Advantages and Disadvantages Advantages Better reflects data variability Considers variability due to sampling and imputation Disadvantages More time and computer intensive
33 What if I have MNAR missingness? Selection models Pattern mixture models Random effect models Shared parameter models
34 What to do study design? Carefully consider potential challenges to obtaining complete data Duration of study, number of visits/surveys, travel distance, participant characteristics/motivations Provide appropriate compensation/incentives Plan to enhance/support/encourage completion If possible, collect information about why an observation is missing
35 What to do data analysis? Evaluate missingness in data How much data is missing? Are there patterns to missingness? Are there differences between subjects with complete and incomplete data? Are there differences in missingness among experimental groups? Within experimental groups? Consider and compare alternative approaches to addressing missing data
Missing Data. A Typology Of Missing Data. Missing At Random Or Not Missing At Random
[Leeuw, Edith D. de, and Joop Hox. (2008). Missing Data. Encyclopedia of Survey Research Methods. Retrieved from http://sage-ereference.com/survey/article_n298.html] Missing Data An important indicator
More informationMissing Data: Part 1 What to Do? Carol B. Thompson Johns Hopkins Biostatistics Center SON Brown Bag 3/20/13
Missing Data: Part 1 What to Do? Carol B. Thompson Johns Hopkins Biostatistics Center SON Brown Bag 3/20/13 Overview Missingness and impact on statistical analysis Missing data assumptions/mechanisms Conventional
More informationA Basic Introduction to Missing Data
John Fox Sociology 740 Winter 2014 Outline Why Missing Data Arise Why Missing Data Arise Global or unit non-response. In a survey, certain respondents may be unreachable or may refuse to participate. Item
More informationProblem of Missing Data
VASA Mission of VA Statisticians Association (VASA) Promote & disseminate statistical methodological research relevant to VA studies; Facilitate communication & collaboration among VA-affiliated statisticians;
More informationReview of the Methods for Handling Missing Data in. Longitudinal Data Analysis
Int. Journal of Math. Analysis, Vol. 5, 2011, no. 1, 1-13 Review of the Methods for Handling Missing Data in Longitudinal Data Analysis Michikazu Nakai and Weiming Ke Department of Mathematics and Statistics
More informationMissing Data & How to Deal: An overview of missing data. Melissa Humphries Population Research Center
Missing Data & How to Deal: An overview of missing data Melissa Humphries Population Research Center Goals Discuss ways to evaluate and understand missing data Discuss common missing data methods Know
More informationUsing Medical Research Data to Motivate Methodology Development among Undergraduates in SIBS Pittsburgh
Using Medical Research Data to Motivate Methodology Development among Undergraduates in SIBS Pittsburgh Megan Marron and Abdus Wahed Graduate School of Public Health Outline My Experience Motivation for
More informationAnalyzing Structural Equation Models With Missing Data
Analyzing Structural Equation Models With Missing Data Craig Enders* Arizona State University cenders@asu.edu based on Enders, C. K. (006). Analyzing structural equation models with missing data. In G.
More informationMISSING DATA TECHNIQUES WITH SAS. IDRE Statistical Consulting Group
MISSING DATA TECHNIQUES WITH SAS IDRE Statistical Consulting Group ROAD MAP FOR TODAY To discuss: 1. Commonly used techniques for handling missing data, focusing on multiple imputation 2. Issues that could
More informationMissing Data in Longitudinal Studies: To Impute or not to Impute? Robert Platt, PhD McGill University
Missing Data in Longitudinal Studies: To Impute or not to Impute? Robert Platt, PhD McGill University 1 Outline Missing data definitions Longitudinal data specific issues Methods Simple methods Multiple
More informationImputation and Analysis. Peter Fayers
Missing Data in Palliative Care Research Imputation and Analysis Peter Fayers Department of Public Health University of Aberdeen NTNU Det medisinske fakultet Missing data Missing data is a major problem
More informationMISSING DATA IMPUTATION IN CARDIAC DATA SET (SURVIVAL PROGNOSIS)
MISSING DATA IMPUTATION IN CARDIAC DATA SET (SURVIVAL PROGNOSIS) R.KAVITHA KUMAR Department of Computer Science and Engineering Pondicherry Engineering College, Pudhucherry, India DR. R.M.CHADRASEKAR Professor,
More informationA Mixed Model Approach for Intent-to-Treat Analysis in Longitudinal Clinical Trials with Missing Values
Methods Report A Mixed Model Approach for Intent-to-Treat Analysis in Longitudinal Clinical Trials with Missing Values Hrishikesh Chakraborty and Hong Gu March 9 RTI Press About the Author Hrishikesh Chakraborty,
More informationHow to choose an analysis to handle missing data in longitudinal observational studies
How to choose an analysis to handle missing data in longitudinal observational studies ICH, 25 th February 2015 Ian White MRC Biostatistics Unit, Cambridge, UK Plan Why are missing data a problem? Methods:
More informationDealing 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 informationHandling missing data in Stata a whirlwind tour
Handling missing data in Stata a whirlwind tour 2012 Italian Stata Users Group Meeting Jonathan Bartlett www.missingdata.org.uk 20th September 2012 1/55 Outline The problem of missing data and a principled
More informationIntroduction to mixed model and missing data issues in longitudinal studies
Introduction to mixed model and missing data issues in longitudinal studies Hélène Jacqmin-Gadda INSERM, U897, Bordeaux, France Inserm workshop, St Raphael Outline of the talk I Introduction Mixed models
More informationBayesian Approaches to Handling Missing Data
Bayesian Approaches to Handling Missing Data Nicky Best and Alexina Mason BIAS Short Course, Jan 30, 2012 Lecture 1. Introduction to Missing Data Bayesian Missing Data Course (Lecture 1) Introduction to
More informationHandling attrition and non-response in longitudinal data
Longitudinal and Life Course Studies 2009 Volume 1 Issue 1 Pp 63-72 Handling attrition and non-response in longitudinal data Harvey Goldstein University of Bristol Correspondence. Professor H. Goldstein
More informationA Review of Methods for Missing Data
Educational Research and Evaluation 1380-3611/01/0704-353$16.00 2001, Vol. 7, No. 4, pp. 353±383 # Swets & Zeitlinger A Review of Methods for Missing Data Therese D. Pigott Loyola University Chicago, Wilmette,
More information2. Making example missing-value datasets: MCAR, MAR, and MNAR
Lecture 20 1. Types of missing values 2. Making example missing-value datasets: MCAR, MAR, and MNAR 3. Common methods for missing data 4. Compare results on example MCAR, MAR, MNAR data 1 Missing Data
More informationAn introduction to modern missing data analyses
Journal of School Psychology 48 (2010) 5 37 An introduction to modern missing data analyses Amanda N. Baraldi, Craig K. Enders Arizona State University, United States Received 19 October 2009; accepted
More informationDealing with Missing Data
Res. Lett. Inf. Math. Sci. (2002) 3, 153-160 Available online at http://www.massey.ac.nz/~wwiims/research/letters/ Dealing with Missing Data Judi Scheffer I.I.M.S. Quad A, Massey University, P.O. Box 102904
More informationAPPLIED MISSING DATA ANALYSIS
APPLIED MISSING DATA ANALYSIS Craig K. Enders Series Editor's Note by Todd D. little THE GUILFORD PRESS New York London Contents 1 An Introduction to Missing Data 1 1.1 Introduction 1 1.2 Chapter Overview
More informationAVOIDING BIAS AND RANDOM ERROR IN DATA ANALYSIS
AVOIDING BIAS AND RANDOM ERROR IN DATA ANALYSIS Susan Ellenberg, Ph.D. Perelman School of Medicine University of Pennsylvania School of Medicine FDA Clinical Investigator Course White Oak, MD November
More informationNonrandomly Missing Data in Multiple Regression Analysis: An Empirical Comparison of Ten Missing Data Treatments
Brockmeier, Kromrey, & Hogarty Nonrandomly Missing Data in Multiple Regression Analysis: An Empirical Comparison of Ten s Lantry L. Brockmeier Jeffrey D. Kromrey Kristine Y. Hogarty Florida A & M University
More informationA REVIEW OF CURRENT SOFTWARE FOR HANDLING MISSING DATA
123 Kwantitatieve Methoden (1999), 62, 123-138. A REVIEW OF CURRENT SOFTWARE FOR HANDLING MISSING DATA Joop J. Hox 1 ABSTRACT. When we deal with a large data set with missing data, we have to undertake
More informationStatistical modelling with missing data using multiple imputation. Session 4: Sensitivity Analysis after Multiple Imputation
Statistical modelling with missing data using multiple imputation Session 4: Sensitivity Analysis after Multiple Imputation James Carpenter London School of Hygiene & Tropical Medicine Email: james.carpenter@lshtm.ac.uk
More informationMultiple Imputation for Missing Data: A Cautionary Tale
Multiple Imputation for Missing Data: A Cautionary Tale Paul D. Allison University of Pennsylvania Address correspondence to Paul D. Allison, Sociology Department, University of Pennsylvania, 3718 Locust
More informationAnalysis of Longitudinal Data with Missing Values.
Analysis of Longitudinal Data with Missing Values. Methods and Applications in Medical Statistics. Ingrid Garli Dragset Master of Science in Physics and Mathematics Submission date: June 2009 Supervisor:
More informationMissing Data. Katyn & Elena
Missing Data Katyn & Elena What to do with Missing Data Standard is complete case analysis/listwise dele;on ie. Delete cases with missing data so only complete cases are le> Two other popular op;ons: Mul;ple
More informationGuideline on missing data in confirmatory clinical trials
2 July 2010 EMA/CPMP/EWP/1776/99 Rev. 1 Committee for Medicinal Products for Human Use (CHMP) Guideline on missing data in confirmatory clinical trials Discussion in the Efficacy Working Party June 1999/
More informationA Review of Missing Data Treatment Methods
A Review of Missing Data Treatment Methods Liu Peng, Lei Lei Department of Information Systems, Shanghai University of Finance and Economics, Shanghai, 200433, P.R. China ABSTRACT Missing data is a common
More informationData Cleaning and Missing Data Analysis
Data Cleaning and Missing Data Analysis Dan Merson vagabond@psu.edu India McHale imm120@psu.edu April 13, 2010 Overview Introduction to SACS What do we mean by Data Cleaning and why do we do it? The SACS
More informationOverview. Longitudinal Data Variation and Correlation Different Approaches. Linear Mixed Models Generalized Linear Mixed Models
Overview 1 Introduction Longitudinal Data Variation and Correlation Different Approaches 2 Mixed Models Linear Mixed Models Generalized Linear Mixed Models 3 Marginal Models Linear Models Generalized Linear
More informationSPSS 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 informationImputing Attendance Data in a Longitudinal Multilevel Panel Data Set
Imputing Attendance Data in a Longitudinal Multilevel Panel Data Set April 2015 SHORT REPORT Baby FACES 2009 This page is left blank for double-sided printing. Imputing Attendance Data in a Longitudinal
More informationMISSING DATA IN NON-PARAMETRIC TESTS OF CORRELATED DATA
MISSING DATA IN NON-PARAMETRIC TESTS OF CORRELATED DATA Annie Green Howard A dissertation submitted to the faculty of the University of North Carolina at Chapel Hill in partial fulfillment of the requirements
More informationAnalyzing Intervention Effects: Multilevel & Other Approaches. Simplest Intervention Design. Better Design: Have Pretest
Analyzing Intervention Effects: Multilevel & Other Approaches Joop Hox Methodology & Statistics, Utrecht Simplest Intervention Design R X Y E Random assignment Experimental + Control group Analysis: t
More informationPATTERN MIXTURE MODELS FOR MISSING DATA. Mike Kenward. London School of Hygiene and Tropical Medicine. Talk at the University of Turku,
PATTERN MIXTURE MODELS FOR MISSING DATA Mike Kenward London School of Hygiene and Tropical Medicine Talk at the University of Turku, April 10th 2012 1 / 90 CONTENTS 1 Examples 2 Modelling Incomplete Data
More informationHandling missing data in large data sets. Agostino Di Ciaccio Dept. of Statistics University of Rome La Sapienza
Handling missing data in large data sets Agostino Di Ciaccio Dept. of Statistics University of Rome La Sapienza The problem Often in official statistics we have large data sets with many variables and
More informationWorkpackage 11 Imputation and Non-Response. Deliverable 11.2
Workpackage 11 Imputation and Non-Response Deliverable 11.2 2004 II List of contributors: Seppo Laaksonen, Statistics Finland; Ueli Oetliker, Swiss Federal Statistical Office; Susanne Rässler, University
More informationDr James Roger. GlaxoSmithKline & London School of Hygiene and Tropical Medicine.
American Statistical Association Biopharm Section Monthly Webinar Series: Sensitivity analyses that address missing data issues in Longitudinal studies for regulatory submission. Dr James Roger. GlaxoSmithKline
More informationNCEE 2009-0049. What to Do When Data Are Missing in Group Randomized Controlled Trials
NCEE 2009-0049 What to Do When Data Are Missing in Group Randomized Controlled Trials What to Do When Data Are Missing in Group Randomized Controlled Trials October 2009 Michael J. Puma Chesapeake Research
More informationItem Imputation Without Specifying Scale Structure
Original Article Item Imputation Without Specifying Scale Structure Stef van Buuren TNO Quality of Life, Leiden, The Netherlands University of Utrecht, The Netherlands Abstract. Imputation of incomplete
More informationarxiv:1301.2490v1 [stat.ap] 11 Jan 2013
The Annals of Applied Statistics 2012, Vol. 6, No. 4, 1814 1837 DOI: 10.1214/12-AOAS555 c Institute of Mathematical Statistics, 2012 arxiv:1301.2490v1 [stat.ap] 11 Jan 2013 ADDRESSING MISSING DATA MECHANISM
More informationMissing Data: Patterns, Mechanisms & Prevention. Edith de Leeuw
Missing Data: Patterns, Mechanisms & Prevention Edith de Leeuw Thema middag Nonresponse en Missing Data, Universiteit Groningen, 30 Maart 2006 Item-Nonresponse Pattern General pattern: various variables
More informationIBM SPSS Missing Values 20
IBM SPSS Missing Values 20 Note: Before using this information and the product it supports, read the general information under Notices on p. 87. This edition applies to IBM SPSS Statistics 20 and to all
More informationMissing Data Sensitivity Analysis of a Continuous Endpoint An Example from a Recent Submission
Missing Data Sensitivity Analysis of a Continuous Endpoint An Example from a Recent Submission Arno Fritsch Clinical Statistics Europe, Bayer November 21, 2014 ASA NJ Chapter / Bayer Workshop, Whippany
More informationStudy 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 informationMissing data are ubiquitous in clinical research.
Advanced Statistics: Missing Data in Clinical Research Part 1: An Introduction and Conceptual Framework Jason S. Haukoos, MD, MS, Craig D. Newgard, MD, MPH Abstract Missing data are commonly encountered
More informationMissing Data: Our View of the State of the Art
Psychological Methods Copyright 2002 by the American Psychological Association, Inc. 2002, Vol. 7, No. 2, 147 177 1082-989X/02/$5.00 DOI: 10.1037//1082-989X.7.2.147 Missing Data: Our View of the State
More informationMethodological Challenges in Analyzing Patient-reported Outcomes
Methodological Challenges in Analyzing Patient-reported Outcomes Elizabeth A. Hahn Center on Outcomes, Research and Education (CORE), Evanston Northwestern Healthcare, Evanston, IL Dept. of Preventive
More informationApplied Missing Data Analysis in the Health Sciences. Statistics in Practice
Brochure More information from http://www.researchandmarkets.com/reports/2741464/ Applied Missing Data Analysis in the Health Sciences. Statistics in Practice Description: A modern and practical guide
More informationApplication in Predictive Analytics. FirstName LastName. Northwestern University
Application in Predictive Analytics FirstName LastName Northwestern University Prepared for: Dr. Nethra Sambamoorthi, Ph.D. Author Note: Final Assignment PRED 402 Sec 55 Page 1 of 18 Contents Introduction...
More informationMissing data in randomized controlled trials (RCTs) can
EVALUATION TECHNICAL ASSISTANCE BRIEF for OAH & ACYF Teenage Pregnancy Prevention Grantees May 2013 Brief 3 Coping with Missing Data in Randomized Controlled Trials Missing data in randomized controlled
More informationRe-analysis using Inverse Probability Weighting and Multiple Imputation of Data from the Southampton Women s Survey
Re-analysis using Inverse Probability Weighting and Multiple Imputation of Data from the Southampton Women s Survey MRC Biostatistics Unit Institute of Public Health Forvie Site Robinson Way Cambridge
More informationIBM SPSS Missing Values 22
IBM SPSS Missing Values 22 Note Before using this information and the product it supports, read the information in Notices on page 23. Product Information This edition applies to version 22, release 0,
More informationImputation of missing network data: Some simple procedures
Imputation of missing network data: Some simple procedures Mark Huisman Dept. of Psychology University of Groningen Abstract Analysis of social network data is often hampered by non-response and missing
More informationSensitivity Analysis in Multiple Imputation for Missing Data
Paper SAS270-2014 Sensitivity Analysis in Multiple Imputation for Missing Data Yang Yuan, SAS Institute Inc. ABSTRACT Multiple imputation, a popular strategy for dealing with missing values, usually assumes
More informationBest Practices for Missing Data Management in Counseling Psychology
Journal of Counseling Psychology 2010 American Psychological Association 2010, Vol. 57, No. 1, 1 10 0022-0167/10/$12.00 DOI: 10.1037/a0018082 Best Practices for Missing Data Management in Counseling Psychology
More informationTABLE OF CONTENTS ALLISON 1 1. INTRODUCTION... 3
ALLISON 1 TABLE OF CONTENTS 1. INTRODUCTION... 3 2. ASSUMPTIONS... 6 MISSING COMPLETELY AT RANDOM (MCAR)... 6 MISSING AT RANDOM (MAR)... 7 IGNORABLE... 8 NONIGNORABLE... 8 3. CONVENTIONAL METHODS... 10
More informationMaking the Most of Missing Values: Object Clustering with Partial Data in Astronomy
Astronomical Data Analysis Software and Systems XIV ASP Conference Series, Vol. XXX, 2005 P. L. Shopbell, M. C. Britton, and R. Ebert, eds. P2.1.25 Making the Most of Missing Values: Object Clustering
More informationImputation of missing data under missing not at random assumption & sensitivity analysis
Imputation of missing data under missing not at random assumption & sensitivity analysis S. Jolani Department of Methodology and Statistics, Utrecht University, the Netherlands Advanced Multiple Imputation,
More informationAnalysis of Various Techniques to Handling Missing Value in Dataset Rajnik L. Vaishnav a, Dr. K. M. Patel b a
Available online at www.ijiere.com International Journal of Innovative and Emerging Research in Engineering e-issn: 2394-3343 e-issn: 2394-5494 Analysis of Various Techniques to Handling Missing Value
More informationCritical 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 information1. I have 4 sides. My opposite sides are equal. I have 4 right angles. Which shape am I?
Which Shape? This problem gives you the chance to: identify and describe shapes use clues to solve riddles Use shapes A, B, or C to solve the riddles. A B C 1. I have 4 sides. My opposite sides are equal.
More informationA Study to Predict No Show Probability for a Scheduled Appointment at Free Health Clinic
A Study to Predict No Show Probability for a Scheduled Appointment at Free Health Clinic Report prepared for Brandon Slama Department of Health Management and Informatics University of Missouri, Columbia
More informationExploratory Data Analysis
Exploratory Data Analysis Paul Cohen ISTA 370 Spring, 2012 Paul Cohen ISTA 370 () Exploratory Data Analysis Spring, 2012 1 / 46 Outline Data, revisited The purpose of exploratory data analysis Learning
More informationCopyright 2010 The Guilford Press. Series Editor s Note
This is a chapter excerpt from Guilford Publications. Applied Missing Data Analysis, by Craig K. Enders. Copyright 2010. Series Editor s Note Missing data are a real bane to researchers across all social
More informationAn Analysis of Four Missing Data Treatment Methods for Supervised Learning
An Analysis of Four Missing Data Treatment Methods for Supervised Learning Gustavo E. A. P. A. Batista and Maria Carolina Monard University of São Paulo - USP Institute of Mathematics and Computer Science
More informationImputing Missing Data using SAS
ABSTRACT Paper 3295-2015 Imputing Missing Data using SAS Christopher Yim, California Polytechnic State University, San Luis Obispo Missing data is an unfortunate reality of statistics. However, there are
More informationFinal Report for 2006 AICPA Summer Internship: AICPA Practice Analysis Methodology for Sampling Design and Selected Topics
Final Report for 2006 AICPA Summer Internship: AICPA Practice Analysis Methodology for Sampling Design and Selected Topics Technical Report September 2007 Number W0704 Elaine M. Rodeck University of Nebraska-Lincoln
More informationMissing data and net survival analysis Bernard Rachet
Workshop on Flexible Models for Longitudinal and Survival Data with Applications in Biostatistics Warwick, 27-29 July 2015 Missing data and net survival analysis Bernard Rachet General context Population-based,
More informationDescriptive Methods Ch. 6 and 7
Descriptive Methods Ch. 6 and 7 Purpose of Descriptive Research Purely descriptive research describes the characteristics or behaviors of a given population in a systematic and accurate fashion. Correlational
More informationMissing Data. Paul D. Allison INTRODUCTION
4 Missing Data Paul D. Allison INTRODUCTION Missing data are ubiquitous in psychological research. By missing data, I mean data that are missing for some (but not all) variables and for some (but not all)
More informationM. Ehren N. Shackleton. Institute of Education, University of London. June 2014. Grant number: 511490-2010-LLP-NL-KA1-KA1SCR
Impact of school inspections on teaching and learning in primary and secondary education in the Netherlands; Technical report ISI-TL project year 1-3 data M. Ehren N. Shackleton Institute of Education,
More informationPLANNED MISSING DATA DESIGNS
PLANNED MISSING DATA DESIGNS SRCD Developmental Methodology Conference Feb 9 202 PLANNED MISSING DATA DESIGNS In planned missing data designs, participants are randomly assigned to conditions in which
More informationSoftware Cost Estimation with Incomplete Data
890 IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, VOL. 27, NO. 10, OCTOBER 2001 Software Cost Estimation with Incomplete Data Kevin Strike, Khaled El Emam, and Nazim Madhavji AbstractÐThe construction of
More informationModule 14: Missing Data Stata Practical
Module 14: Missing Data Stata Practical Jonathan Bartlett & James Carpenter London School of Hygiene & Tropical Medicine www.missingdata.org.uk Supported by ESRC grant RES 189-25-0103 and MRC grant G0900724
More informationMissing data: the hidden problem
white paper Missing data: the hidden problem Draw more valid conclusions with SPSS Missing Data Analysis white paper Missing data: the hidden problem 2 Just about everyone doing analysis has some missing
More informationCraig K. Enders Arizona State University Department of Psychology craig.enders@asu.edu
Craig K. Enders Arizona State University Department of Psychology craig.enders@asu.edu Topic Page Missing Data Patterns And Missing Data Mechanisms 1 Traditional Missing Data Techniques 7 Maximum Likelihood
More informationCHOOSING APPROPRIATE METHODS FOR MISSING DATA IN MEDICAL RESEARCH: A DECISION ALGORITHM ON METHODS FOR MISSING DATA
CHOOSING APPROPRIATE METHODS FOR MISSING DATA IN MEDICAL RESEARCH: A DECISION ALGORITHM ON METHODS FOR MISSING DATA Hatice UENAL Institute of Epidemiology and Medical Biometry, Ulm University, Germany
More informationReject Inference in Credit Scoring. Jie-Men Mok
Reject Inference in Credit Scoring Jie-Men Mok BMI paper January 2009 ii Preface In the Master programme of Business Mathematics and Informatics (BMI), it is required to perform research on a business
More informationII. 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 informationAdvances in Missing Data Methods and Implications for Educational Research. Chao-Ying Joanne Peng, Indiana University-Bloomington
Advances in Missing Data 1 Running head: MISSING DATA METHODS Advances in Missing Data Methods and Implications for Educational Research Chao-Ying Joanne Peng, Indiana University-Bloomington Michael Harwell,
More informationStudy Designs. Simon Day, PhD Johns Hopkins University
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike License. Your use of this material constitutes acceptance of that license and the conditions of use of materials on this
More informationHCUP Methods Series Missing Data Methods for the NIS and the SID Report # 2015-01
HCUP Methods Series Contact Information: Healthcare Cost and Utilization Project (HCUP) Agency for Healthcare Research and Quality 540 Gaither Road Rockville, MD 20850 http://www.hcup-us.ahrq.gov For Technical
More informationMissing Data Part 1: Overview, Traditional Methods Page 1
Missing Data Part 1: Overview, Traditional Methods Richard Williams, University of Notre Dame, http://www3.nd.edu/~rwilliam/ Last revised January 17, 2015 This discussion borrows heavily from: Applied
More informationMissing Data in Survival Analysis and Results from the MESS Trial
Missing Data in Survival Analysis and Results from the MESS Trial J. K. Rogers J. L. Hutton K. Hemming Department of Statistics University of Warwick Research Students Conference, 2008 Outline Background
More informationA Review of Methods. for Dealing with Missing Data. Angela L. Cool. Texas A&M University 77843-4225
Missing Data 1 Running head: DEALING WITH MISSING DATA A Review of Methods for Dealing with Missing Data Angela L. Cool Texas A&M University 77843-4225 Paper presented at the annual meeting of the Southwest
More informationIn almost any research you perform, there is the potential for missing or
SIX DEALING WITH MISSING OR INCOMPLETE DATA Debunking the Myth of Emptiness In almost any research you perform, there is the potential for missing or incomplete data. Missing data can occur for many reasons:
More informationElectronic Theses and Dissertations UC Riverside
Electronic Theses and Dissertations UC Riverside Peer Reviewed Title: Bayesian and Non-parametric Approaches to Missing Data Analysis Author: Yu, Yao Acceptance Date: 01 Series: UC Riverside Electronic
More informationARTICLE Methods for Handling Missing Data in the Behavioral Neurosciences: Don t Throw the Baby Rat out with the Bath Water
ARTICLE Methods for Handling Missing Data in the Behavioral Neurosciences: Don t Throw the Baby Rat out with the Bath Water Leah H. Rubin, 1,2 Katie Witkiewitz, 1 Justin St. Andre, 1 and Steve Reilly 1
More informationAuxiliary Variables in Mixture Modeling: 3-Step Approaches Using Mplus
Auxiliary Variables in Mixture Modeling: 3-Step Approaches Using Mplus Tihomir Asparouhov and Bengt Muthén Mplus Web Notes: No. 15 Version 8, August 5, 2014 1 Abstract This paper discusses alternatives
More informationDealing with missing data: Key assumptions and methods for applied analysis
Technical Report No. 4 May 6, 2013 Dealing with missing data: Key assumptions and methods for applied analysis Marina Soley-Bori msoley@bu.edu This paper was published in fulfillment of the requirements
More informationIntroduction to Longitudinal Data Analysis
Introduction to Longitudinal Data Analysis Longitudinal Data Analysis Workshop Section 1 University of Georgia: Institute for Interdisciplinary Research in Education and Human Development Section 1: Introduction
More informationThe PCORI Methodology Report. Appendix A: Methodology Standards
The Appendix A: Methodology Standards November 2013 4 INTRODUCTION This page intentionally left blank. APPENDIX A A-1 APPENDIX A: PCORI METHODOLOGY STANDARDS Cross-Cutting Standards for PCOR 1: Standards
More informationChapter 1. Longitudinal Data Analysis. 1.1 Introduction
Chapter 1 Longitudinal Data Analysis 1.1 Introduction One of the most common medical research designs is a pre-post study in which a single baseline health status measurement is obtained, an intervention
More information