Advances In Latent Class Analysis

What is latent class analysis? If you asked that question thirty or forty years ago you would have gotten a different answer than you would today. Closer to its time of inception, latent class analysis was viewed primarily as a categorical data analysis technique, often framed as a factor analysis model where both the measured variable indicators and underlying latent variables are categorical. Today, however, it rests within much broader mixture and diagnostic modeling framework, integrating measured and latent variables that may be categorical and/or continuous, and where latent classes serve to define the subpopulations for whom many aspects of the focal measured and latent variable model may differ. For latent class analysis to take these developmental leaps required contributions that were methodological, certainly, as well as didactic. Among the leaders on both fronts was C. Mitchell “Chan” Dayton, at the University of Maryland, whose work in latent class analysis spanning several decades helped the method to expand and reach its current potential. The current volume in the Center for Integrated Latent Variable Research (CILVR) series reflects the diversity that is latent class analysis today, celebrating work related to, made possible by, and inspired by Chan’s noted contributions, and signaling the even more exciting future yet to come.

A modern, comprehensive treatment of latent class and latent transition analysis for categorical data On a daily basis, researchers in the social, behavioral, and health sciences collect information and fit statistical models to the gathered empirical data with the goal of making significant advances in these fields. In many cases, it can be useful to identify latent, or unobserved, subgroups in a population, where individuals' subgroup membership is inferred from their responses on a set of observed variables. Latent Class and Latent Transition Analysis provides a comprehensive and unified introduction to this topic through one-of-a-kind, step-by-step presentations and coverage of theoretical, technical, and practical issues in categorical latent variable modeling for both cross-sectional and longitudinal data. The book begins with an introduction to latent class and latent transition analysis for categorical data. Subsequent chapters delve into more in-depth material, featuring: A complete treatment of longitudinal latent class models Focused coverage of the conceptual underpinnings of interpretation and evaluationof a latent class solution Use of parameter restrictions and detection of identification problems Advanced topics such as multi-group analysis and the modeling and interpretation of interactions between covariates The authors present the topic in a style that is accessible yet rigorous. Each method is presented with both a theoretical background and the practical information that is useful for any data analyst. Empirical examples showcase the real-world applications of the discussed concepts and models, and each chapter concludes with a "Points to Remember" section that contains a brief summary of key ideas. All of the analyses in the book are performed using Proc LCA and Proc LTA, the authors' own software packages that can be run within the SAS® environment. A related Web site houses information on these freely available programs and the book's data sets, encouraging readers to reproduce the analyses and also try their own variations. Latent Class and Latent Transition Analysis is an excellent book for courses on categorical data analysis and latent variable models at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners in the social, behavioral, and health sciences who conduct latent class and latent transition analysis in their everyday work.

Applied Latent Class Analysis introduces several innovations in latent class analysis to a wider audience of researchers. Many of the world's leading innovators in the field of latent class analysis contributed essays to this volume, each presenting a key innovation to the basic latent class model and illustrating how it can prove useful in situations typically encountered in actual research.

"The Handbook of Methodological Approaches to Community-Based Research is intended to aid the community-oriented researcher in learning about and applying cutting-edge quantitative, qualitative, and mixed methods approaches"--

The Oxford Handbook of Quantitative Methods in Psychology provides an accessible and comprehensive review of the current state-of-the-science and a one-stop source for learning and reviewing current best-practices in a quantitative methods across the social, behavioral, and educational sciences.

The current volume, Advances in Latent Variable Mixture Models, contains chapters by all of the speakers who participated in the 2006 CILVR conference, providing not just a snapshot of the event, but more importantly chronicling the state of the art in latent variable mixture model research. The volume starts with an overview chapter by the CILVR conference keynote speaker, Bengt Muthén, offering a “lay of the land” for latent variable mixture models before the volume moves to more specific constellations of topics. Part I, Multilevel and Longitudinal Systems, deals with mixtures for data that are hierarchical in nature either due to the data’s sampling structure or to the repetition of measures (of varied types) over time. Part II, Models for Assessment and Diagnosis, addresses scenarios for making judgments about individuals’ state of knowledge or development, and about the instruments used for making such judgments. Finally, Part III, Challenges in Model Evaluation, focuses on some of the methodological issues associated with the selection of models most accurately representing the processes and populations under investigation. It should be stated that this volume is not intended to be a first exposure to latent variable methods. Readers lacking such foundational knowledge are encouraged to consult primary and/or secondary didactic resources in order to get the most from the chapters in this volume. Once armed with the basic understanding of latent variable methods, we believe readers will find this volume incredibly exciting.

This new handbook is the definitive resource on advanced topics related to multilevel analysis. The editors assembled the top minds in the field to address the latest applications of multilevel modeling as well as the specific difficulties and methodological problems that are becoming more common as more complicated models are developed. Each chapter features examples that use actual datasets. These datasets, as well as the code to run the models, are available on the book’s website http://www.hlm-online.com . Each chapter includes an introduction that sets the stage for the material to come and a conclusion. Divided into five sections, the first provides a broad introduction to the field that serves as a framework for understanding the latter chapters. Part 2 focuses on multilevel latent variable modeling including item response theory and mixture modeling. Section 3 addresses models used for longitudinal data including growth curve and structural equation modeling. Special estimation problems are examined in section 4 including the difficulties involved in estimating survival analysis, Bayesian estimation, bootstrapping, multiple imputation, and complicated models, including generalized linear models, optimal design in multilevel models, and more. The book’s concluding section focuses on statistical design issues encountered when doing multilevel modeling including nested designs, analyzing cross-classified models, and dyadic data analysis. Intended for methodologists, statisticians, and researchers in a variety of fields including psychology, education, and the social and health sciences, this handbook also serves as an excellent text for graduate and PhD level courses in multilevel modeling. A basic knowledge of multilevel modeling is assumed.

This unique volume provides self-contained accounts of some recent trends in Biostatistics methodology and their applications. It includes state-of-the-art reviews and original contributions. The articles included in this volume are based on a careful selection of peer-reviewed papers, authored by eminent experts in the field, representing a well balanced mix of researchers from the academia, R&D sectors of government and the pharmaceutical industry. The book is also intended to give advanced graduate students and new researchers a scholarly overview of several research frontiers in biostatistics, which they can use to further advance the field through development of new techniques and results. Contents:False Discovery Rates:A New Adaptive Method to Control the False Discovery Rate (F Liu & S K Sarkar)Adaptive Multiple Testing Procedures Under Positive Dependence (W-G Guo et al.)A False Discovery Rate Procedure for Categorical Data (J F Heyse)Survival Analysis:Conditional Nelson-Aalen and Kaplan-Meier Estimators with the Müller–Wang Boundary Kernel (X-D Luo & W-Y Tsai)Regression Analysis in Failure Time Mixture Models with Change Points According to Thresholds of a Covariate (J-M Lee et al.)Modeling Survival Data Using the Piecewise Exponential Model with Random Time Grid (F N Demarqui et al.)Proportional Rate Models for Recurrent Time Event Data Under Dependent Censoring: A Comparative Study (L D A F Amorim et al.)Efficient Algorithms for Bayesian Binary Regression Model with Skew-Probit Link (R B A Farias & M D Branco)M-Estimation Methods in Heteroscedastic Nonlinear Regression Models (C Lim et al.)The Inverse Censoring Weighted Approach for Estimation of Survival Functions from Left and Right Censored Data (S Subramanian & P-X Zhang)Analysis and Design of Competing Risks Data in Clinical Research (H T Kimn)Related Topics: Genomics/Bioinformatics, Medical Imaging and Diagnosis, Clinical Trials:Comparative Genomic Analysis Using Information Theory (S N Fatakia et al.)Statistical Modeling for Data of Positron Emission Tomography in Depression (C Chang & R T Ogden)The Use of Latent Class Analysis in Medical Diagnosis (D Rindskopf)Subset Selection in Comparative Selection Trials (C-S Leu et al.) Readership: Advanced Graduate students; active researchers in universities, research labs in government and industry engaged in and concerned with modeling and data analysis in biostatistics; R&D managers and directors of biostatistics / public health research in government and industry. Keywords:False Discovery Rate;Adaptive Multiple Testing;Survival Analysis;Censoring;Nelson–Aalen Estimator;Kaplan–Meier Estimator;Recurrent Time-to-Event Data Under Dependent Censoring;Bayesian Binary Regression;M-Estimation;Heteroscedastic Nonlinear Regression;Failure Time Mixture Models with Change Points;Genomic Analysis Using Information Theory;Modeling Positron Emission Tomography;Competing Risks;Latent Class Analysis;Comparative Selection TrialsKey Features:Includes a treatment of current research on “False Discovery Methods”, a topic of high relevance and interest in gene-expression/microarray studiesIncludes new methods for regression analysis of recurrent and censored time-to-event data with dependent censoring, innovative estimation methods for unconditional and conditional survival distributions from censored data including double censoring, novel applications in medical imaging and diagnosis, information theory and comparative genomicsContributors are prominent experts in their fields