Normal view MARC view

Multilevel and longitudinal modeling using Stata

Author: Robe-Hesketh, Sophia Publisher: Stata Press, 2005.Language: EnglishDescription: 312 p. ; 24 cm.ISBN: 1597180084Type of document: BookBibliography/Index: Includes bibliographical references and index
Tags: No tags from this library for this title. Log in to add tags.
Item type Current location Collection Call number Status Date due Barcode Item holds
Book Europe Campus
Main Collection
Print HA29 .R33 2005
(Browse shelf)
001157639
Available 001157639
Total holds: 0

Includes bibliographical references and index

Digitized

Multilevel and Longitudinal Modeling Using Stata Contents Preface 1 Linear variance-components models 1.1 xxi 1 Introduction ............................................................................................................................. 1 1.2 How reliable are expiratory flow measurements? ............................................................. 1 1.3 The variance-components model ........................................................................................... 4 1.3.1 Model specification and path diagram .................................................................... 4 1.3.2 Error components, variance components, and reliability .................................. 7 1.3.3 Intraclass correlation .................................................................................................... 8 1.4 Modeling the Mini Wright measurements ........................................................................... 8 1.4.1 Estimation using xtreg ................................................................................................. 9 1.4.2 Estimation using xtmixed ........................................................................................... 10 1.4.3 Estimation using gllamm ............................................................................................ 11 1.4.4 Relative and absolute agreement ............................................................... 14 1.5 Estimation methods ............................................................................................ 15 1.6 Assigning values to the random intercepts ......................................................................... 16 1.6.1 Maximum likelihood estimation .................................................................. 17 Implementation via OLS regression .......................................................... 17 Implementation via the mean total residual ........................................... 19 1.6.2 Empirical Bayes prediction ........................................................................................ 19 1.6.3 · Empirical Bayes variances ...................................................................... 24 1.7 Summary and further reading ............................................................................. 25 1.8 Exercises .................................................................................................................................. 26 viii 2 Linear random-intercept models 2.1 Contents 31 Introduction ........................................................................................................................ 31 2.2 Are tax preparers useful?......................................................................................................................................................................................... 31 2.3 The longitudinal data structure ....................................................................... 32 2.4 Panel data and correlated residuals ................................................................ 32 2.5 The random-intercept model ............................................................................. 34 2.5.1 Estimation using xtreg ......................................................................... 36 2.5.2 Estimation using xtmixed .................................................................... 38 2.6 Different kinds of effects in panel models ..................................................... 39 2.6.1 Between-taxpayer effects ........................................................................ 39 2.6.2 Within-taxpayer effects ........................................................................... 40 2.6.3 Relations among the estimators ........................................................... 42 2.7 Endogeneity and between-taxpayer effects ........................................................ 42 2.8 Residual diagnostics ................................................................................... 45 2.9 Summary and further reading ......................................................................... 50 2.10 Exercises ......................................................................................................... 51 3 Linear random-coefficient and growth-curve models 3.1 3.2 3.3 57 Introduction ............................................................................................... 57 How effective are different schools? ................................................................ 57 Separate linear regressions for each school ................................................. 58 3.4 The random-coefficient model ............................................................................ 61 3.4.1 Specification and interpretation of a random-coefficient model . .......... 61 3.4.2 Estimation and prediction using xtmixed ............................................ 68 Estimation of random-intercept model ............................................... 68 Estimation of random-coefficient model .............................................. 69 Empirical Bayes prediction using xtmixed .......................................... 72 3.4.3 Estimation and prediction using gllamm ............................................. 74 Estimation of random-intercept model ............................................... 75 Estimation of random-coefficient model .............................................. 75 Empirical Bayes prediction ................................................................. 77 Contents 3.5 How do children grow? ........................................................................................ 78 3.6 Growth-curve modeling ....................................................................................... 79 3.6.1 Observed growth trajectories ................................................................. 79 3.6.2 Estimation using xtmixed .................................................................... 80 Quadratic growth model with random intercept ................................. 80 Quadratic growth model with random intercept and random slope .. 80 Including a child-level covariate ........................................................................ 83 3.6.3 Estimation using gllamm ..................................................................... 84 Quadratic growth model with random intercept ................................. 84 Quadratic growth model with random intercept and random slope 85 Including a child-level covariate ........................................................... 86 3.7 Two-stage model formulation ............................................................................. 87 3.7.1 Model specification .................................................................................. 88 3.7.2 Estimation ........................................................................................... 88 3.8 3.9 Prediction of trajectories for individual children ...................................................... 91 · Complex level-1 variation or heteroskedasticity .......................................... 92 ix 3.10 Summary and further reading ....................................................................... 94 3.11 Exercises .................................................................................................................................. 95 4 Dichotomous or binary responses 4.1 101 Models for dichotomous responses .............................................................. 101 4.1.1 Generalized linear model formulation .................................................. 101 4.1.2 Latent-response formulation ............................................................... 107 Logistic regression .............................................................................. 108 Probit regression ............................................................................... 109 4.2 4.3 Which treatment is best for toenail infection? ............................................. 111 The longitudinal data structure ................................................................ 112 4.4 Population-averaged or marginal probabilities ................................................. 113 4.5 4.6 Random-intercept logistic regression........................................................... 116 Subject-specific vs. population-averaged relationships ............................... 120 x Contents 4.7 Maximum likelihood estimation using adaptive quadrature ..................... 124 4.7.1 Some practical considerations ................................................................... 128 4.8 Empirical Bayes (EB) predictions ........................................................................... 129 4.8.1 EB prediction of random effects ................................................................ 129 4.8.2 EB prediction of response probabilities ................................................ 130 4.9 Other approaches to clustered dichotomous data .......................................... 131 4.9.1 Conditional logistic regression ................................................................... 131 4.9.2 Generalized estimating equations (GEE) ............................................... 132 4.10 Summary and further reading .............................................................................. 134 4.11 Exercises ......................................................................................................................... 134 5 Ordinal responses 5.1 143 Introduction ................................................................................................................. 143 5.2 Cumulative models for ordinal responses .......................................................... 143 5.2.1 Generalized linear model formulation ..................................................... 143 5.2.2 Latent-response formulation ...................................................................... 144 5.2.3 Proportional odds ............................................................................................. 147 5.2.4 Identification ...................................................................................................... 148 5.3 Are antipsychotic drugs effective for patients with schizophrenia? . . .. 149 5.4 Longitudinal data structure and graphs ............................................................. 150 5.4.1 The longitudinal data structure ................................................................ 150 5.4.2 Plotting cumulative proportions ................................................................ 151 5.4.3 Plotting cumulative logits and transforming the time scale . . . 152 5.5 A proportional-odds model ........................................................................................ 154 5.5.1 Model specification .......................................................................................... 154 5.5.2 Estimation ........................................................................................................... 155 5.6 A random-intercept proportional-odds model .................................................. 157 5.6.1 Model specification .......................................................................................... 157 5.6.2 Estimation ........................................................................................................... 158 5.7 A random-coefficient proportional-odds model ................................................ 159 Contents 5.7.1 Model specification ................................................................................................. 159 5.7.2 Estimation ................................................................................................................ 159 5.8 Marginal and patient-specific probabilities .................................................................... 161 5.8.1 Marginal probabilities ............................................................................................ 161 5.8.2 Patient-specific cumulative response probabilities ........................................ 163 5.9 Do experts differ in their grading of student essays?................................................................................................ 165 5.10 A random-intercept model with grader bias ................................................................ 165 5.10.1 Model specification .............................................................................................. 165 5.10.2 Estimation .............................................................................................................. 166 5.11 v Including grader-specific measurement error variances ...................................... 167 5.11.1 Model specification .............................................................................................. 167 5.11.2 Estimation .............................................................................................................. 167 5.12 · Including grader-specific thresholds .......................................................................... 169 5.12.1 Model specification .............................................................................................. 169 5.12.2 Estimation .............................................................................................................. 170 5.13 Summary and further reading ........................................................................................ 175 5.14 Exercises ............................................................................................................................... 175 6 Counts 6.1 181 xi Introduction ..................................................................................................................... 181 6.2 Types of counts ...................................................................................................................... 181 6.3 Poisson models for counts .................................................................................................. 182 6.4 Did the German health-care reform reduce the number of doctor visits? 184 6.5 6.6 Longitudinal data structure ......................................................................................... 184 Poisson regression ignoring overdispersion and clustering ................................. 185 6.6.1 Model specification ................................................................................................. 185 6.6.2 Estimation ................................................................................................................ 185 6.7 Poisson regression with overdispersion but ignoring clustering ............................... 187 6.7.1 Using a level-1 random intercept ....................................................................... 187 Model specification ............................................................................................. 187 Estimation ............................................................................................................ 187 xii Contents 6.7.2 Quasilikelihood ................................................................................................. 188 Specification ..................................................................................................... 188 Estimation ......................................................................................................... 189 6.8 Random-intercept Poisson regression .................................................................. 190 6.8.1 Model specification .......................................................................................... 190 6.8.2 Estimation ........................................................................................................... 191 6.9 Random-coefficient Poisson regression ................................................................ 193 6.9.1 Model specification .......................................................................................... 193 6.9.2 Estimation ........................................................................................................... 194 6.10 Other approaches to clustered counts .............................................................. 196 6.10.1 Conditional Poisson regression ............................................................... 196 6.10.2 Generalized estimating equations (GEE) ............................................ 197 6.11 Which Scottish counties have a high risk of lip cancer? .......................... 197 6.12 Standardized mortality ratios ................................................................................ 198 6.13 Random-intercept Poisson regression ................................................................ 201 6.13.1 Model specification ....................................................................................... 201 6.13.2 Estimation ......................................................................................................... 201 6.13.3 Introducing a county-level covariate ..................................................... 202 6.13.4 Prediction .......................................................................................................... 203 6.14 · Nonparametric maximum likelihood estimation ........................................ 206 6.14.1 Specification .................................................................................................... 206 6.14.2 Estimation ......................................................................................................... 206 6.14.3 Prediction .......................................................................................................... 210 6.15 Summary and further reading .............................................................................. 211 6.16 Exercises ......................................................................................................................... 212 7 Higher level models and nested random effects 7.1 217 Introduction ................................................................................................................. 217 7.2 Which method is best for measuring expiratory flow? ................................. 218 7.3 Two-level variance-components models .............................................................. 219 Contents xiii 7.3.1 Model specification .................................................................................................. 219 7.3.2 Estimation ................................................................................................................. 219 7.4 Three-level variance-components models ...................................................................... 222 7.4.1 Model specification .................................................................................................. 222 7.4.2 Different types of intraclass correlation ............................................................ 224 7.4.3 Three-stage formulation ........................................................................................ 225 7.4.4 Estimation using xtmixed ..................................................................................... 225 7.4.5 Prediction using xtmixed ....................................................................................... 228 7.5 Did the Guatemalan immunization campaign work?.................................................... 229 7.6 A three-level logistic random-intercept model ................................................................ 230 7.6.1 Model specification .................................................................................................. 230 7.6.2 Different types of intraclass correlations for the latent responses ............. 230 7.6.3 Three-stage formulation ........................................................................................ 231 7.6.4 Estimation ................................................................................................................. 231 7.6.5 Introducing a random coefficient at level 3 ...................................................... 235 7.6.6 Prediction .................................................................................................................. 239 7.7 Summary and further reading ........................................................................................... 240 7.8 Exercises ............................................................................................................................. 241 249 8 Crossed random effects 8.1 Introduction ....................................................................................................................... 249 8.2 How does investment depend on expected profit and capital stock? . . 250 8.3 A two-way error-components model .................................................................................. 251 8.3.1 Model specification .................................................................................................. 251 8.3.2 Intraclass correlations ........................................................................................... 252 8.3.3 Estimation ................................................................................................................. 252 8.3.4 Prediction .................................................................................................................. 254 8.4 How much do primary and secondary schools affect attainment at age 16? 257 8.5 An additive crossed random-effects model .................................................................... 259 8.5.1 Specification .............................................................................................................. 259 xiv Contents 8.5.2 Estimation .............................................................................................................. 259 8.6 Including a random interaction .................................................................................... 260 8.6.1 Model specification .............................................................................................. 260 8.6.2 Intraclass correlations ........................................................................................ 261 8.6.3 Estimation .............................................................................................................. 262 8.6.4 Some diagnostics .................................................................................................. 264 8.7 · A trick requiring fewer random effects .................................................................... 266 8.8 Summary and further reading ...................................................................................... 269 8.9 Exercises .............................................................................................................................. 269 A Syntax for gllamm, eq, and gllapred B Syntax for gllamm C Syntax for gllapred D Syntax for gllasim References Author index Subject index 275 281 293 297 301 309 313

There are no comments for this item.

Log in to your account to post a comment.
Koha 18.11 - INSEAD Catalogue
Home | Contact Us | What's Koha?