Book•
Applied Longitudinal Analysis
01 Jul 2004-
TL;DR: In this article, the authors present an overview of linear models for long-term continuous-time data and compare them with generalized linear mixed effects models for estimating the covariance and the mean.
Abstract: Preface.Acknowledgments.PART I: INTRODUCTION TO LONGITUDINAL AND CLUSTERED DATA.1. Longitudinal and Clustered Data.2. Longitudinal Data: Basic Concepts.PART II: LINEAR MODELS FOR LONGITUDINAL CONTINUOUS DATA.3. Overview of Linear Models for Longitudinal Data.4. Estimation and Statistical Inference.5. Modelling the Mean: Analyzing Response Profiles.6. Modelling the Mean: Parametric Curves.7. Modelling the Covariance.8. Linear Mixed Effects Models.9. Residual Analyses and Diagnostics.PART III: GENERALIZED LINEAR MODELS FOR LONGITUDINAL DATA.10. Review of Generalized Linear Models.11. Marginal Models: Generalized Estimating Equations (GEE).12. Generalized Linear Mixed Effects Models.13. Contrasting Marginal and Mixed Effects Models.PART IV: ADVANCED TOPICS FOR LONGITUDINAL AND CLUSTERED DATA.14. Missing Data and Dropout.15. Some Aspects of the Design of Longitudinal Studies.16. Repeated Measures and Related Designs.17. Multilevel Models.Appendix A: Gentle Introduction to Vectors and Matrices.Appendix B: Properties of Expectations and Variances.Appendix C: Critical Points for a 50:50 Mixture of Chi-Squared Distributions.References.Index.
Citations
More filters
TL;DR: A protocol for data exploration is provided; current tools to detect outliers, heterogeneity of variance, collinearity, dependence of observations, problems with interactions, double zeros in multivariate analysis, zero inflation in generalized linear modelling, and the correct type of relationships between dependent and independent variables are discussed; and advice on how to address these problems when they arise is provided.
Abstract: Summary 1. While teaching statistics to ecologists, the lead authors of this paper have noticed common statistical problems. If a random sample of their work (including scientific papers) produced before doing these courses were selected, half would probably contain violations of the underlying assumptions of the statistical techniques employed. 2. Some violations have little impact on the results or ecological conclusions; yet others increase type I or type II errors, potentially resulting in wrong ecological conclusions. Most of these violations can be avoided by applying better data exploration. These problems are especially troublesome in applied ecology, where management and policy decisions are often at stake. 3. Here, we provide a protocol for data exploration; discuss current tools to detect outliers, heterogeneity of variance, collinearity, dependence of observations, problems with interactions, double zeros in multivariate analysis, zero inflation in generalized linear modelling, and the correct type of relationships between dependent and independent variables; and provide advice on how to address these problems when they arise. We also address misconceptions about normality, and provide advice on data transformations. 4. Data exploration avoids type I and type II errors, among other problems, thereby reducing the chance of making wrong ecological conclusions and poor recommendations. It is therefore essential for good quality management and policy based on statistical analyses.
5,894 citations
TL;DR: Progression-free survival was longer and response rates were higher in patients with metastatic renal-cell cancer who received sunitinib than in those receiving interferon alfa.
Abstract: Background Since sunitinib malate has shown activity in two uncontrolled studies in patients with metastatic renal-cell carcinoma, a comparison of the drug with interferon alfa in a phase 3 trial is warranted. Methods We enrolled 750 patients with previously untreated, metastatic renal-cell carcinoma in a multicenter, randomized, phase 3 trial to receive either repeated 6-week cycles of sunitinib (at a dose of 50 mg given orally once daily for 4 weeks, followed by 2 weeks without treatment) or interferon alfa (at a dose of 9 MU given subcutaneously three times weekly). The primary end point was progression-free survival. Secondary end points included the objective response rate, overall survival, patient-reported outcomes, and safety. Results The median progression-free survival was significantly longer in the sunitinib group (11 months) than in the interferon alfa group (5 months), corresponding to a hazard ratio of 0.42 (95% confidence interval, 0.32 to 0.54; P<0.001). Sunitinib was also associated with a higher objective response rate than was interferon alfa (31% vs. 6%, P<0.001). The proportion of patients with grade 3 or 4 treatment-related fatigue was significantly higher in the group treated with interferon alfa, whereas diarrhea was more frequent in the sunitinib group (P<0.05). Patients in the sunitinib group reported a significantly better quality of life than did patients in the interferon alfa group (P<0.001). Conclusions Progression-free survival was longer and response rates were higher in patients with metastatic renal-cell cancer who received sunitinib than in those receiving interferon alfa (ClinicalTrials.gov numbers, NCT00098657 and NCT00083889).
5,244 citations
Book•
03 May 2007
TL;DR: In this paper, the effects of rice farming on aquatic birds with mixed modelling were investigated using additive and generalised additive modeling and univariate methods to analyse abundance of decapod larvae.
Abstract: Introduction.- Data management and software.- Advice for teachers.- Exploration.- Linear regression.- Generalised linear modelling.- Additive and generalised additive modelling.- Introduction to mixed modelling.- Univariate tree models.- Measures of association.- Ordination--first encounter.- Principal component analysis and redundancy analysis.- Correspondence analysis and canonical correspondence analysis.- Introduction to discriminant analysis.- Principal coordinate analysis and non-metric multidimensional scaling.- Time series analysis--Introduction.- Common trends and sudden changes.- Analysis and modelling lattice data.- Spatially continuous data analysis and modelling.- Univariate methods to analyse abundance of decapod larvae.- Analysing presence and absence data for flatfish distribution in the Tagus estuary, Portugual.- Crop pollination by honeybees in an Argentinean pampas system using additive mixed modelling.- Investigating the effects of rice farming on aquatic birds with mixed modelling.- Classification trees and radar detection of birds for North Sea wind farms.- Fish stock identification through neural network analysis of parasite fauna.- Monitoring for change: using generalised least squares, nonmetric multidimensional scaling, and the Mantel test on western Montana grasslands.- Univariate and multivariate analysis applied on a Dutch sandy beach community.- Multivariate analyses of South-American zoobenthic species--spoilt for choice.- Principal component analysis applied to harbour porpoise fatty acid data.- Multivariate analysis of morphometric turtle data--size and shape.- Redundancy analysis and additive modelling applied on savanna tree data.- Canonical correspondence analysis of lowland pasture vegetation in the humid tropics of Mexico.- Estimating common trends in Portuguese fisheries landings.- Common trends in demersal communities on the Newfoundland-Labrador Shelf.- Sea level change and salt marshes in the Wadden Sea: a time series analysis.- Time series analysis of Hawaiian waterbirds.- Spatial modelling of forest community features in the Volzhsko-Kamsky reserve.
1,788 citations
TL;DR: In a population-based sample of middle-aged adults, subjective reports of habitual sleep are moderately correlated with actigraph-measured sleep, but are biased by systematic over-reporting.
Abstract: Recent epidemiologic studies have found that sleep duration is associated with obesity, diabetes, hypertension and mortality. These studies have used self-reported habitual sleep duration, which has not been well validated. We model the extent to which self-reported habitual sleep reflects average objectively measured sleep. Eligible participants at the Chicago site of Coronary Artery Risk Development in Young Adults Study were invited to participate in a 2003-2004 ancillary sleep study; 82% (n=669) agreed. Sleep measurements collected in two waves included: 3-days of wrist actigraphy, a sleep log, and standard questions about usual sleep duration. Average measured sleep was 6 hours, and subjective reports averaged 0.80 hours longer than measured sleep. Subjective reports were not well calibrated, increasing on average by 31 minutes for each additional hour of measured sleep. Our model suggests that persons sleeping 5 and 7 hours over-reported, on average, by 1.3 and 0.3 hours respectively. Overall, there was a correlation of 0.45 between reported and measured sleep duration. The extent of overestimation, calibration and correlation varied by personal and sleep characteristics. Although asking about sleep duration seems uncomplicated, the correlation between self-reported and objectively-measured sleep in this population was moderate and systematically biased.
1,177 citations
TL;DR: This meta-analysis showed that systolic blood pressure and urinary protein excretion were related to the risk for renal disease progression in patients with nondiabetic kidney disease.
Abstract: Achieving a systolic blood pressure between 110 and 129 mm Hg may slow the progression of nondiabetic kidney disease when the urinary protein excretion exceeds 1.0 g/d. Systolic blood pressure less...
1,079 citations
References
More filters
Book•
01 Jan 1994
TL;DR: In this paper, a generalized linear model for longitudinal data and transition models for categorical data are presented. But the model is not suitable for categric data and time dependent covariates are not considered.
Abstract: 1. Introduction 2. Design considerations 3. Exploring longitudinal data 4. General linear models 5. Parametric models for covariance structure 6. Analysis of variance methods 7. Generalized linear models for longitudinal data 8. Marginal models 9. Random effects models 10. Transition models 11. Likelihood-based methods for categorical data 12. Time-dependent covariates 13. Missing values in longitudinal data 14. Additional topics Appendix Bibliography Index
7,156 citations
TL;DR: Van Der Heijden et al. as discussed by the authors used correspondence analysis for the analysis of transitions between more than two time points, where the transition matrix is the product of the margins of the table divided by the total sample size.
Abstract: Correspondence analysis is an exploratory tool for the analysis of associations between categorical variables, the results of which may be displayed graphically. For longitudinal data, two types of analysis can be distinguished: the first focuses on transitions, whereas the second investigates trends. For transitional analysis with two time points, an analysis of the transition matrix (showing the relative frequencies for pairs of categories) provides insight into the structure of departures from independence in the transitions. Transitions between more than two time points can also be studied simultaneously. In trend analyses often the trajectories of different groups are compared. Examples for all these analyses are provided. Correspondence analysis is an exploratory tool for the analysis of association(s) between categorical variables. Usually, the results are displayed in a graphical way. There are many interpretations of correspondence analysis. Here, we make use of two of them. A first interpretation is that the observed categorical data are collected in a matrix, and correspondence analysis approximates this matrix by a matrix of lower rank[1]. This lower rank approximation of, say, rank M + 1 is then displayed graphically in an M-dimensional representation in which each row and each column of the matrix is displayed as a point. The difference in rank between the rank M + 1 matrix and the rank M representation is matrix of rank 1, and this matrix is the product of the marginal counts of the matrix, that is most often considered uninteresting. This brings us to the second interpretation, that is, that when the two-way matrix is a contingency table, correspondence analysis decomposes the departure from a matrix where the row and column variables are independent[2,3]. Thus, correspondence analysis is a tool for residual analysis. This interpretation holds because for a contingency table estimates under the independence model are obtained from the product of the margins of the table divided by the total sample size. Longitudinal data are data where observations (e.g., individuals) are measured at least twice using the same variables. We consider here only categorical (i.e., nominal or ordinal) variables, as only this kind of variables is analyzed in standard applications of correspondence analysis[4]. We first discuss correspondence analysis for the analysis of transitions. Thereafter, we consider analysis of trends with canonical correspondence analysis. 1 Leiden University, Leiden, The Netherlands 2 Utrecht University, Utrecht, The Netherlands Update based on original article by Peter G. M. Van Der Heijden, Wiley StatsRef: Statistics Reference Online, © 2014, John Wiley & Sons, Ltd Wiley StatsRef: Statistics Reference Online, © 2014–2015 John Wiley & Sons, Ltd. This article is © 2015 John Wiley & Sons, Ltd. DOI: 10.1002/9781118445112.stat05497.pub2 1 Correspondence Analysis of Longitudinal Data 1 Transitional Analysis
2,104 citations
Book•
01 Jan 1996
TL;DR: In this article, the authors present a method for estimating normal error distributions of continuous non-normal measures. But their method is based on a generalized linear model and Maximum Quasi-Likelihood Estimation.
Abstract: Introduction. Normal Error Distributions: Multivariate Analysis of Variance. Univariate Analysis of Variance. Regression Methods. Random Effects Models. Covariance Structures. Non-normal Error Distributions: Continuous Non-Normal Measures. Gaussian Estimation. Nonlinear Models. Generalized Linear Models and Maximum Quasi-Likelihood Estimation. Binary and Categorical Measures. Comparisons of Methods. Data Appendices. References.
229 citations