Download or read book Analysis of Clustered Data when the Cluster Size is Informative written by M. Pavlou and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Clustered data arise in many scenarios. We may wish to fit a marginal regression model relating outcome measurements to covariates for cluster members. Often the cluster size, the number of members, varies. Informative cluster size (ICS) has been defined to arise when the outcome depends on the cluster size conditional on covariates. If the clusters are considered complete then the population of all cluster members and the population of typical cluster members have been proposed as suitable targets for inference, which will differ between these populations under ICS. However if the variation in cluster size arises from missing data then the clusters are considered incomplete and we seek inference for the population of all members of all complete clusters. We define informative covariate structure to arise when for a particular member the outcome is related to the covariates for other members in the cluster, conditional on the covariates for that member and the cluster size. In this case the proposed populations for inference may be inappropriate and, just as under ICS, standard estimation methods are unsuitable. We propose two further populations and weighted independence estimating equations (WIEE) for estimation. An adaptation of GEE was proposed to provide inference for the population of typical cluster members and increase efficiency, relative to WIEE, by incorporating the intra-cluster correlation. We propose an alternative adaptation which can provide superior efficiency. For each adaptation we explain how bias can arise. This bias was not clearly described when the first adaptation was originally proposed. Several authors have vaguely related ICS to the violation of the 'missing completely at random' assumption. We investigate which missing data mechanisms can cause ICS, which might lead to similar inference for the populations of typical cluster members and all members of all complete clusters, and we discuss implications for estimation.
Download or read book Analysis of Clustered Data when the Cluster Size is Informative written by Menelaos Pavlou and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Advances in Cluster Correlated Data Analysis when Cluster Size Is Informative written by Samuel Anyaso-Samuel and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: associated with the outcomes. We develop a rank-based statistic to test the marginal effect of the continuous covariate under this complex form of informativeness. Existing statistical methods based on ranks are inadequate in the presence of such informative latent groups. Through detailed simulation studies, we demonstrate the superior performance of our new test statistic compared to parametric or semiparametric methods. Collectively, these projects contribute to the understanding and handling of ICS in clustered data analysis. The proposed methodologies and guidelines provide valuable tools for researchers dealing with informative cluster sizes in the context of survival analysis and extend the applicability of existing techniques to more complex scenarios. The findings from this dissertation enhance the accuracy and reliability of statistical analyses in the presence of ICS, ultimately improving the validity of inferences drawn from clustered data.
Download or read book Clustered Longitudinal Data Analysis written by Ming Wang and published by . This book was released on 2008 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: Clustered longitudinal data is often collected as repeated measurements on subjects over time arising in the clusters. Examples include longitudinal community intervention studies, or family studies with repeated measures on each member. Meanwhile, cluster size is sometime informative, which means that the risk for the outcomes is related to the cluster size. Under this situation, generalized estimating equations (GEE) will lead to invalid inferences because GEE assumes that the cluster size is non-informative. In this study, we investigated the performances of generalized estimating equations (GEE), cluster-weighted generalized estimating equations (CWGEE), and within-cluster resampling (WCR) on clustered longitudinal data. Based on our extensive simulation studies, we conclude that all three methods provide comparable estimates when the cluster size is non-informative. But when cluster size is informative, GEE gives biased estimates, while WCR and CWGEE still provide unbiased and consistent estimates under different "working correlation structures" within-subject. However, WCR is a computationally intensive approach, so CWGEE is the best choice for clustered longitudinal data due to its solving only one estimating equation, which is asymptotically equivalent to WCR.
Download or read book Regression Analysis of Clustered Interval censored Failure Time Data with Informative Cluster Size written by Xinyan Zhang and published by . This book was released on 2010 with total page 79 pages. Available in PDF, EPUB and Kindle. Book excerpt: Correlated or clustered failure time data often occur in many research fields including epidemiological, geographical, sociological and medical studies. Sometimes such data arise together with interval censoring and the failure time of interest may be related to the cluster size. Various approaches have been proposed to analyze failure time data with interval censoring. However, these approaches ignore the informativeness of the cluster size. Due to the lack of proper inference procedures for direct analysis, these methods merely simplified or converted interval-censored data into right-censored data, which inevitably resulted in biased parameter estimates. In this dissertation, both parametric and semiparametric approaches are presented for regression analyses of clustered failure time data that allow both interval-censoring and informative cluster size. We further validate these approaches by conducting various simulation studies and apply them to a lymphatic filariasis example.
Download or read book Hierarchical Linear Models written by Stephen W. Raudenbush and published by SAGE. This book was released on 2002 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt: New edition of a text in which Raudenbush (U. of Michigan) and Bryk (sociology, U. of Chicago) provide examples, explanations, and illustrations of the theory and use of hierarchical linear models (HLM). New material in Part I (Logic) includes information on multivariate growth models and other topics.
Download or read book Methodologies for Longitudinal Data Adjusting for Informative Cluster Size with Non ignorable Zeros written by Biyi Shen and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Clustered and longitudinal data are commonly encountered in clinical trials and observational studies. Often, the data are collected through a real-time monitoring scheme associated with some specific event, such as disease recurrence, hospitalization, or emergency room visit. In these contexts, the cluster size can be informative because of its potential correlation with disease progression, since recurrence may represent a worsening of health. However, for some subjects, there are no relevant medical records. Depending on the disease, some of these subjects may have a lower risk of a recurrent event but none were observed during the follow-up, whereas others may not be susceptible to recurrent events at all, indicating a non-ignorable zero cluster size. There is a substantial body of literature using only observations from those subjects with a non-zero informative cluster size, but the existing works rarely discuss informative non-ignorable zero-sized clusters. To utilize the information from both event-free and event-occurring participants, we first propose a weighted within-cluster resampling (WWCR) method and its asymptotically equivalent method, dual-weighted GEE (WWGEE) by adopting the inverse probability weighting technique. The asymptotic properties are rigorously presented theoretically. Extensive simulations and an illustrative example of the Assessment, Serial Evaluation, and Subsequent Sequelae of Acute Kidney Injury (ASSESS-AKI) study are performed to analyze the finite-sample behavior of our methods and to show their advantageous performance compared to existing approaches. On the other hand, there often exists a terminal event that may be correlated with the recurrent events. Previous work in this area suffered from the limitation that not all these issues were handled simultaneously. To address this deficiency, we also propose a novel joint modeling approach for longitudinal data adjusting for zero-inflated and informative cluster size as well as a terminal event. A three-stage semiparametric likelihood-based approach is applied for parameter estimation and inference. Extensive simulations are conducted to evaluate the performance of our proposal. Finally, we again utilize the ASSESS-AKI study for illustration.
Download or read book Clustered Longitudinal Data Analysis written by Ming Wang and published by LAP Lambert Academic Publishing. This book was released on 2009-09 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt: Clustered longitudinal data is often collected as repeated measurements on subjects over time arising in the clusters. Examples include longitudinal community intervention studies, or family studies with repeated measures on each member. Meanwhile, cluster size is sometime informative, which means that the risk for the outcomes is related to the cluster size. Under this situation, generalized estimating equations (GEE) will lead to invalid inferences because GEE assumes that the cluster size is non-informative. In this study, we investigated the performances of generalized estimating equations (GEE), cluster-weighted generalized estimating equations (CWGEE), and within-cluster resampling (WCR) on clustered longitudinal data. Based on our extensive simulation studies, we conclude that all three methods provide comparable estimates when the cluster size is non-informative. But when cluster size is informative, GEE gives biased estimates, while WCR and CWGEE still provide unbiased and consistent estimates under different "working correlation structures" within-subject. However, WCR is a computationally intensive approach, so CWGEE is the best choice for clustered longitudinal data due to its solving only one estimating equation, which is asymptotically equivalent to WCR.
Download or read book Topics in Modelling of Clustered Data written by Marc Aerts and published by CRC Press. This book was released on 2002-05-29 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many methods for analyzing clustered data exist, all with advantages and limitations in particular applications. Compiled from the contributions of leading specialists in the field, Topics in Modelling of Clustered Data describes the tools and techniques for modelling the clustered data often encountered in medical, biological, environmental, and s
Download or read book Modern Nonparametric Robust and Multivariate Methods written by Klaus Nordhausen and published by Springer. This book was released on 2015-10-05 with total page 513 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by leading experts in the field, this edited volume brings together the latest findings in the area of nonparametric, robust and multivariate statistical methods. The individual contributions cover a wide variety of topics ranging from univariate nonparametric methods to robust methods for complex data structures. Some examples from statistical signal processing are also given. The volume is dedicated to Hannu Oja on the occasion of his 65th birthday and is intended for researchers as well as PhD students with a good knowledge of statistics.
Download or read book Generalized Linear and Mixed Models written by Charles E. McCulloch and published by John Wiley & Sons. This book was released on 2011-09-20 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt: An accessible and self-contained introduction to statistical models-now in a modernized new edition Generalized, Linear, and Mixed Models, Second Edition provides an up-to-date treatment of the essential techniques for developing and applying a wide variety of statistical models. The book presents thorough and unified coverage of the theory behind generalized, linear, and mixed models and highlights their similarities and differences in various construction, application, and computational aspects. A clear introduction to the basic ideas of fixed effects models, random effects models, and mixed models is maintained throughout, and each chapter illustrates how these models are applicable in a wide array of contexts. In addition, a discussion of general methods for the analysis of such models is presented with an emphasis on the method of maximum likelihood for the estimation of parameters. The authors also provide comprehensive coverage of the latest statistical models for correlated, non-normally distributed data. Thoroughly updated to reflect the latest developments in the field, the Second Edition features: A new chapter that covers omitted covariates, incorrect random effects distribution, correlation of covariates and random effects, and robust variance estimation A new chapter that treats shared random effects models, latent class models, and properties of models A revised chapter on longitudinal data, which now includes a discussion of generalized linear models, modern advances in longitudinal data analysis, and the use between and within covariate decompositions Expanded coverage of marginal versus conditional models Numerous new and updated examples With its accessible style and wealth of illustrative exercises, Generalized, Linear, and Mixed Models, Second Edition is an ideal book for courses on generalized linear and mixed models at the upper-undergraduate and beginning-graduate levels. It also serves as a valuable reference for applied statisticians, industrial practitioners, and researchers.
Download or read book Easy Statistics for Food Science with R written by Abbas F.M. Alkarkhi and published by Academic Press. This book was released on 2018-09-18 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: Easy Statistics for Food Science with R presents the application of statistical techniques to assist students and researchers who work in food science and food engineering in choosing the appropriate statistical technique. The book focuses on the use of univariate and multivariate statistical methods in the field of food science. The techniques are presented in a simplified form without relying on complex mathematical proofs. This book was written to help researchers from different fields to analyze their data and make valid decisions. The development of modern statistical packages makes the analysis of data easier than before. The book focuses on the application of statistics and correct methods for the analysis and interpretation of data. R statistical software is used throughout the book to analyze the data. Contains numerous step-by-step tutorials help the reader to learn quickly Covers the theory and application of the statistical techniques Shows how to analyze data using R software Provides R scripts for all examples and figures
Download or read book Handbook of Cluster Analysis written by Christian Hennig and published by CRC Press. This book was released on 2015-12-16 with total page 753 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Cluster Analysis provides a comprehensive and unified account of the main research developments in cluster analysis. Written by active, distinguished researchers in this area, the book helps readers make informed choices of the most suitable clustering approach for their problem and make better use of existing cluster analysis tools.The
Download or read book Cluster Analysis written by Brian S. Everitt and published by . This book was released on 1977 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Data Clustering Theory Algorithms and Applications Second Edition written by Guojun Gan and published by SIAM. This book was released on 2020-11-10 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data clustering, also known as cluster analysis, is an unsupervised process that divides a set of objects into homogeneous groups. Since the publication of the first edition of this monograph in 2007, development in the area has exploded, especially in clustering algorithms for big data and open-source software for cluster analysis. This second edition reflects these new developments, covers the basics of data clustering, includes a list of popular clustering algorithms, and provides program code that helps users implement clustering algorithms. Data Clustering: Theory, Algorithms and Applications, Second Edition will be of interest to researchers, practitioners, and data scientists as well as undergraduate and graduate students.
Download or read book Clustering Stability written by Ulrike Von Luxburg and published by Now Publishers Inc. This book was released on 2010 with total page 53 pages. Available in PDF, EPUB and Kindle. Book excerpt: A popular method for selecting the number of clusters is based on stability arguments: one chooses the number of clusters such that the corresponding clustering results are most stable. In recent years, a series of papers has analyzed the behavior of this method from a theoretical point of view. However, the results are very technical and difficult to interpret for non-experts. In this paper we give a high-level overview about the existing literature on clustering stability. In addition to presenting the results in a slightly informal but accessible way, we relate them to each other and discuss their different implications.
Download or read book Analysis of Longitudinal Data with Example written by You-Gan Wang and published by CRC Press. This book was released on 2022-01-28 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: Development in methodology on longitudinal data is fast. Currently, there are a lack of intermediate /advanced level textbooks which introduce students and practicing statisticians to the updated methods on correlated data inference. This book will present a discussion of the modern approaches to inference, including the links between the theories of estimators and various types of efficient statistical models including likelihood-based approaches. The theory will be supported with practical examples of R-codes and R-packages applied to interesting case-studies from a number of different areas. Key Features: •Includes the most up-to-date methods •Use simple examples to demonstrate complex methods •Uses real data from a number of areas •Examples utilize R code
