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About 10,000 papers for “Longitudinal data”

Longitudinal Data

Janelle Jones, Claire Howlin, Elizabeth Quinn · Open Science Framework · 2023

Cited by 0Open access

Longitudinal Data

Bertrand S. Clarke, Jennifer L. Clarke · Cambridge University Press eBooks · 2018

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.

Cited by 0

Longitudinal Data

A. Colin Cameron, Pravin K. Trivedi · Cambridge University Press eBooks · 2013

Longitudinal data or panel data are observations on a cross-section of individual units such as persons, households, firms, and regions that are observed over several time periods. The data structure is similar to that of multivariate data considered in Chapter 8. Analysis is simpler than for multivariate data because for each individual unit the same outcome variable is observed, rather than several different outcome variables. Analysis is more complex because this same outcome variable is observed at different points in time, introducing time series data considerations presented in Chapter 7.

Cited by 2

Longitudinal Data

Hung‐Ir Li · Encyclopedia of Statistical Sciences · 2005

Abstract Longitudinal data have been the focus in various research such as economy, social science, public health, and biomedical research in the past few decades. These data comprise outcome measures or responses prospectively collected or naturally accrued repeatedly from the same subjects across multiple time points along with subject characteristic measures. In some observational studies in epidemiology/public heath, the data accrued over a long period of time as recorded in medical charts and are retrospectively retrieved. The data are used to answer questions regarding the change or progression of the disease over time and/or potential risk factors for the disease. Although in other studies, the existing data could be used to identify subgroups with different background characteristics or traits. The identified groups are then followed up for their health conditions for a certain period of time prospectively. The data from these groups are then compared with answer questions set out for the studies. In the clinical research and development settings, most of the longitudinal data are collected via prospectively designed clinical trials in order to assess the changes in efficacy and/or safety of certain medical intervention, drug, biologics, or devices. The longitudinal data from clinical trials in phase III of product development are intended to confirm that the clinical benefit or desired changes over time are primarily due to the target product, can be maintained for an adequate amount of time, and are applicable to a well‐defined population before regulatory approval is granted and the product is registered for public use. This article provides a summary of the definitions, features, and various types of longitudinal data resulting from clinical trials in phase III medicinal product development. The respective characteristics and the related issues along with the rules of thumb in the selection of the statistical methods for the analyses will also be provided.

Cited by 0

Longitudinal Data

Janelle Jones, Claire Howlin, Elizabeth Quinn · Open MIND · 2026

Cited by 0Open access

Longitudinal Data

Peter T. Cummings · 2019

How rates can be used to evaluate policies in populations. Poisson regression, GEE, mixed models, conditional Poisson regression, negative binomial regression are discussed. Details given about model choices and model variables. Models evaluated using simulated data.

Cited by 0

Longitudinal data.

Alejandra Mondino (5149391), Chin-Chieh Yang (19541024), Katherine E. Simon (19541027) et al. · Figshare · 2024

<p>This excel file includes all the data analyzed in the longitudinal analysis.</p> <p>(XLSX)</p>

Cited by 0Open access

Longitudinal Data

Hung‐Ir Li · Encyclopedia of Statistical Sciences · 2005

Abstract Longitudinal data have been the focus in various research such as economy, social science, public health, and biomedical research in the past few decades. These data comprise outcome measures or responses prospectively collected or naturally accrued repeatedly from the same subjects across multiple time points along with subject characteristic measures. In some observational studies in epidemiology/public heath, the data accrued over a long period of time as recorded in medical charts and are retrospectively retrieved. The data are used to answer questions regarding the change or progression of the disease over time and/or potential risk factors for the disease. Although in other studies, the existing data could be used to identify subgroups with different background characteristics or traits. The identified groups are then followed up for their health conditions for a certain period of time prospectively. The data from these groups are then compared with answer questions set out for the studies. In the clinical research and development settings, most of the longitudinal data are collected via prospectively designed clinical trials in order to assess the changes in efficacy and/or safety of certain medical intervention, drug, biologics, or devices. The longitudinal data from clinical trials in phase III of product development are intended to confirm that the clinical benefit or desired changes over time are primarily due to the target product, can be maintained for an adequate amount of time, and are applicable to a well‐defined population before regulatory approval is granted and the product is registered for public use. This article provides a summary of the definitions, features, and various types of longitudinal data resulting from clinical trials in phase III medicinal product development. The respective characteristics and the related issues along with the rules of thumb in the selection of the statistical methods for the analyses will also be provided.

Cited by 0

Longitudinal Data Analysis

The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation · 2018

Preface. Acknowledgments. Acronyms. 1. Introduction. 1.1 Advantages of Longitudinal Studies. 1.2 Challenges of Longitudinal Data Analysis. 1.3 Some General Notation. 1.4 Data Layout. 1.5 Analysis Considerations. 1.6 General Approaches. 1.7 The Simplest Longitudinal Analysis. 1.8 Summary. 2. ANOVA Approaches to Longitudinal Data. 2.1Single-Sample Repeated Measures ANOVA. 2.2 Multiple-Sample Repeated Measures ANOVA. 2.3 Illustration. 2.4 Summary. 3. MANOVA Approaches to Longitudinal Data. 3.1 Data Layout for ANOVA versus MANOVA. 3.2 MANOVA for Repeated Measurements. 3.3 MANOVA of Repeated Measures-s Sample Case. 3.4 Illustration. 3.5 Summary. 4. Mixed-Effects Regression Models for Continuous Outcomes. 4.1 Introduction. 4.2 A Simple Linear Regression Model. 4.3 Random Intercept MRM. 4.4 Random Intercept and Trend MRM. 4.5 Matrix Formulation. 4.6 Estimation . 4.7 Summary. 5. Mixed-Effects Polynomial Regression Models. 5.1 Introduction. 5.2 Curvilinear Trend Model. 5.3 Orthogonal Polynomials. 5.4 Summary. 6. Covariance Pattern Models. 6.1 Introduction. 6.2 Covariance Pattern Models. 6.3 Model Selection. 6.4 Example. 6.5 Summary. 7. Mixed Regression Models with Autocorrelated Errors. 7.1 Introduction. 7.2 MRMs with AC Errors. 7.3 Model Selection. 7.4 Example. 7.5 Summary. 8. Generalized Estimating Equations (GEE) Models. 8.1 Introduction. 8.2 Generalized Linear Models (GLMs). 8.3 Generalized Estimating Equations (GEE) Models. 8.4 GEE Estimation. 8.5 Example. 8.6 Summary. 9. Mixed-Effects Regression Models for Binary Outcomes. 9.1 Introduction. 9.2 Logistic Regression Model. 9.3 Probit Regression Models. 9.4 Threshold Concept. 9.5 Mixed-Effects Logistic Regression Model. 9.6 Estimation. 9.7 Illustration. 9.8 Summary. 10. Mixed-Effects Regression Models for Ordinal Outcomes. 10.1 Introduction. 10.2 Mixed-Effects Proportional Odds Model. 10.3 Psychiatric Example. 10.4 Health Services Research Example. 10.5 Summary. 11. Mixed-Effects Regression Models for Nominal Data. 11.1 Mixed-Effects Multinomial Regression Model. 11.2 Health Services Research Example. 1 1.3 Competing Risk Survival Models. 11.4 Summary. 12. Mixed-effects Regression Models for Counts. 12.1 Poisson Regression Model. 12.2 Modified Poisson Models. 12.3 The ZIP Model. 12.4 Mixed-Effects Models for Counts. 12.5 Illustration. 12.6 Summary. 13. Mixed-Effects Regression Models for Three-Level Data. 13.1 Three-Level Mixed-Effects Linear Regression Model. 13.1.1 Illustration. 13.2 Three-Level Mixed-Effects Nonlinear Regression Models. 13.3 Summary. 14. Missing Data in Longitudinal Studies. 14.1 Introduction. 14.2 Missing Data Mechanisms. 14.3 Models and Missing Data Mechanisms. 14.4 Testing MCAR. 14.5 Models for Nonignorable Missingness. 14.6 Summary. Bibliography. Topic Index.

Cited by 1,545

Longitudinal Data Analysis

Badi H. Baltagi · Journal of the Royal Statistical Society Series A (Statistics in Society) · 2009

Cited by 398