Anders Skrondal

Bio: Anders Skrondal is an academic researcher from Norwegian Institute of Public Health. The author has contributed to research in topics: Generalized linear mixed model & Population. The author has an hindex of 53, co-authored 113 publications receiving 15075 citations. Previous affiliations of Anders Skrondal include London School of Economics and Political Science & University of London.

Multilevel and Longitudinal Modeling Using Stata

Abstract: Preface LINEAR VARIANCE-COMPONENTS MODELS Introduction How reliable are expiratory flow measurements? The variance-components model Modeling the Mini Wright measurements Estimation methods Assigning values to the random intercepts Summary and further reading Exercises LINEAR RANDOM-INTERCEPT MODELS Introduction Are tax preparers useful? The longitudinal data structure Panel data and correlated residuals The random-intercept model Different kinds of effects in panel models Endogeneity and between-taxpayer effects Residual diagnostics Summary and further reading Exercises LINEAR RANDOM-COEFFICIENT AND GROWTH-CURVE MODELS Introduction How effective are different schools? Separate linear regressions for each school The random-coefficient model How do children grow? Growth-curve modeling Two-stage model formulation Prediction of trajectories for individual children Complex level-1 variation or heteroskedasticity Summary and further reading Exercises DICHOTOMOUS OR BINARY RESPONSES Models for dichotomous responses Which treatment is best for toenail infection? The longitudinal data structure Population-averaged or marginal probabilities Random-intercept logistic regression Subject-specific vs. population-averaged relationships Maximum likelihood estimation using adaptive quadrature Empirical Bayes (EB) predictions Other approaches to clustered dichotomous data Summary and further reading Exercises ORDINAL RESPONSES Introduction Cumulative models for ordinal responses Are antipsychotic drugs effective for patients with schizophrenia? Longitudinal data structure and graphs A proportional-odds model A random-intercept proportional-odds model A random-coefficient proportional-odds model Marginal and patient-specific probabilities Do experts differ in their grading of student essays? A random-intercept model with grader bias Including grader-specific measurement error variances Including grader-specific thresholds Summary and further reading Exercises COUNTS Introduction Types of counts Poisson model for counts Did the German health-care reform reduce the number of doctor visits? Longitudinal data structure Poisson regression ignoring overdispersion and clustering Poisson regression with overdispersion but ignoring clustering Random-intercept Poisson regression Random-coefficient Poisson regression Other approaches to clustered counts Which Scottish countries have a high risk of lip cancer? Standardized mortality ratios Random-intercept Poisson regression Nonparametric maximum likelihood estimation Summary and further reading Exercises HIGHER LEVEL MODELS AND NESTED RANDOM EFFECTS Introduction Which method is best for measuring expiratory flow? Two-level variance-components models Three-level variance-components models Did the Guatemalan immunization campaign work? A three-level logistic random-intercept model Summary and further reading Exercises CROSSED RANDOM EFFECTS Introduction How does investment depend on expected profit and capital stock? A two-way error-components model How much do primary and secondary schools affect attainment at age 16? An additive crossed random-effects model Including a random interaction A trick requiring fewer random effects Summary and further reading Exercises APPENDIX A: Syntax for gllamm, eq, and gllapred APPENDIX B: Syntax for gllamm APPENDIX C: Syntax for gllapred APPENDIX D: Syntax for gllasim References Author Index Subject Index

Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models

Abstract: METHODOLOGY THE OMNI-PRESENCE OF LATENT VARIABLES Introduction 'True' variable measured with error Hypothetical constructs Unobserved heterogeneity Missing values and counterfactuals Latent responses Generating flexible distributions Combining information Summary MODELING DIFFERENT RESPONSE PROCESSES Introduction Generalized linear models Extensions of generalized linear models Latent response formulation Modeling durations or survival Summary and further reading CLASSICAL LATENT VARIABLE MODELS Introduction Multilevel regression models Factor models and item response models Latent class models Structural equation models with latent variables Longitudinal models Summary and further reading GENERAL MODEL FRAMEWORK Introduction Response model Structural model for the latent variables Distribution of the disturbances Parameter restrictions and fundamental parameters Reduced form of the latent variables and linear predictor Moment structure of the latent variables Marginal moment structure of observed and latent responses Reduced form distribution and likelihood Reduced form parameters Summary and further reading IDENTIFICATION AND EQUIVALENCE Introduction Identification Equivalence Summary and further reading ESTIMATION Introduction Maximum likelihood: Closed form marginal likelihood Maximum likelihood: Approximate marginal likelihood Maximizing the likelihood Nonparametric maximum likelihood estimation Restricted/Residual maximum likelihood (REML) Limited information methods Maximum quasi-likelihood Generalized Estimating Equations (GEE) Fixed effects methods Bayesian methods Summary Appendix: Some software and references ASSIGNING VALUES TO LATENT VARIABLES Introduction Posterior distributions Empirical Bayes (EB) Empirical Bayes modal (EBM) Maximum likelihood Relating the scoring methods in the 'linear case' Ad hoc scoring methods Some uses of latent scoring and classification Summary and further reading Appendix: Some software MODEL SPECIFICATION AND INFERENCE Introduction Statistical modeling Inference (likelihood based) Model selection: Relative fit criteria Model adequacy: Global absolute fit criteria Model diagnostics: Local absolute fit criteria Summary and further reading APPLICATIONS DICHOTOMOUS RESPONSES Introduction Respiratory infection in children: A random intercept model Diagnosis of myocardial infarction: A latent class model Arithmetic reasoning: Item response models Nicotine gum and smoking cessation: A meta-analysis Wives' employment transitions: Markov models with unobserved heterogeneity Counting snowshoe hares: Capture-recapture models with heterogeneity Attitudes to abortion: A multilevel item response model Summary and further reading ORDINAL RESPONSES Introduction Cluster randomized trial of sex education: Latent growth curve model Political efficacy: Factor dimensionality and item-bias Life satisfaction: Ordinal scaled probit factor models Summary and further reading COUNTS Introduction Prevention of faulty teeth in children: Modeling overdispersion Treatment of epilepsy: A random coefficient model Lip cancer in Scotland: Disease mapping Summary and further reading DURATIONS AND SURVIVAL Introduction Modeling multiple events clustered duration data Onset of smoking: Discrete time frailty models Exercise and angina: Proportional hazards random effects and factor models Summary and further reading COMPARATIVE RESPONSES Introduction Heterogeneity and 'Independence from Irrelevant Alternatives' Model structure British general elections: Multilevel models for discrete choice and rankings Post-materialism: A latent class model for rankings Consumer preferences for coffee makers: A conjoint choice model Summary and further reading MULTIPLE PROCESSES AND MIXED RESPONSES Introduction Diet and heart disease: A covariate measurement error model Herpes and cervical cancer: A latent class covariate measurement error model for a case-control study Job training and depression: A complier average causal effect model Physician advice and drinking: An endogenous treatment model Treatment of liver cirrhosis: A joint survival and marker model Summary and further reading REFERENCES INDEX AUTHOR INDEX

Generalized multilevel structural equation modeling

Abstract: A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models (GLLAMM), combine features of generalized linear mixed models (GLMM) and structural equation models (SEM) and consist of a response model and a structural model for the latent variables. The response model generalizes GLMMs to incorporate factor structures in addition to random intercepts and coefficients. As in GLMMs, the data can have an arbitrary number of levels and can be highly unbalanced with different numbers of lower-level units in the higher-level units and missing data. A wide range of response processes can be modeled including ordered and unordered categorical responses, counts, and responses of mixed types. The structural model is similar to the structural part of a SEM except that it may include latent and observed variables varying at different levels. For example, unit-level latent variables (factors or random coefficients) can be regressed on cluster-level latent variables. Special cases of this framework are explored and data from the British Social Attitudes Survey are used for illustration. Maximum likelihood estimation and empirical Bayes latent score prediction within the GLLAMM framework can be performed using adaptive quadrature in gllamm, a freely available program running in Stata.

Reliable estimation of generalized linear mixed models using adaptive quadrature.

Abstract: Generalized linear mixed models or multilevel regression models have become increasingly popular. Several methods have been proposed for estimating such models. However, to date there is no single method that can be assumed to work well in all circumstances in terms of both parameter recovery and com- putational efficiency. Stata's xt commands for two-level generalized linear mixed models (e.g., xtlogit) employ Gauss-Hermite quadrature to evaluate and maxi- mize the marginal log likelihood. The method generally works very well, and often better than common contenders such as MQL and PQL, but there are cases where quadrature performs poorly. Adaptive quadrature has been suggested to overcome these problems in the two-level case. We have recently implemented a multilevel version of this method ingllamm, a program that fits a large class of multilevel latent variable models including multilevel generalized linear mixed models. As far as we know, this is the first time that adaptive quadrature has been proposed for multilevel models. We show that adaptive quadrature works well in problems where ordinary quadrature fails. Furthermore, even when ordinary quadrature works, adaptive quadrature is often computationally more efficient since it requires fewer quadrature points to achieve the same precision.

lavaan: An R Package for Structural Equation Modeling

Abstract: Structural equation modeling (SEM) is a vast field and widely used by many applied researchers in the social and behavioral sciences. Over the years, many software packages for structural equation modeling have been developed, both free and commercial. However, perhaps the best state-of-the-art software packages in this field are still closed-source and/or commercial. The R package lavaan has been developed to provide applied researchers, teachers, and statisticians, a free, fully open-source, but commercial-quality package for latent variable modeling. This paper explains the aims behind the development of the package, gives an overview of its most important features, and provides some examples to illustrate how lavaan works in practice.

Human biochemical genetics

Abstract: So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

Generalized linear mixed models: a practical guide for ecology and evolution

Abstract: How should ecologists and evolutionary biologists analyze nonnormal data that involve random effects? Nonnormal data such as counts or proportions often defy classical statistical procedures. Generalized linear mixed models (GLMMs) provide a more flexible approach for analyzing nonnormal data when random effects are present. The explosion of research on GLMMs in the last decade has generated considerable uncertainty for practitioners in ecology and evolution. Despite the availability of accurate techniques for estimating GLMM parameters in simple cases, complex GLMMs are challenging to fit and statistical inference such as hypothesis testing remains difficult. We review the use (and misuse) of GLMMs in ecology and evolution, discuss estimation and inference and summarize 'best-practice' data analysis procedures for scientists facing this challenge.

An intervention to decrease catheter-related bloodstream infections in the ICU

Abstract: A b s t r ac t A total of 108 ICUs agreed to participate in the study, and 103 reported data. The analysis included 1981 ICU-months of data and 375,757 catheter-days. The median rate of catheter-related bloodstream infection per 1000 catheter-days decreased from 2.7 infections at baseline to 0 at 3 months after implementation of the study intervention (P≤0.002), and the mean rate per 1000 catheter-days decreased from 7.7 at baseline to 1.4 at 16 to 18 months of follow-up (P<0.002). The regression model showed a significant decrease in infection rates from baseline, with incidence-rate ratios continuously decreasing from 0.62 (95% confidence interval (CI), 0.47 to 0.81) at 0 to 3 months after implementation of the intervention to 0.34 (95% CI, 0.23 to 0.50) at 16 to 18 months. Conclusions An evidence-based intervention resulted in a large and sustained reduction (up to 66%) in rates of catheter-related bloodstream infection that was maintained throughout the 18-month study period.

A Practitioner’s Guide to Cluster-Robust Inference

Abstract: We consider statistical inference for regression when data are grouped into clus- ters, with regression model errors independent across clusters but correlated within clusters. Examples include data on individuals with clustering on village or region or other category such as industry, and state-year dierences-in-dierences studies with clustering on state. In such settings default standard errors can greatly overstate es- timator precision. Instead, if the number of clusters is large, statistical inference after OLS should be based on cluster-robust standard errors. We outline the basic method as well as many complications that can arise in practice. These include cluster-specic �xed eects, few clusters, multi-way clustering, and estimators other than OLS.