Analysis of Longitudinal Data

Clicks: 3
ID: 289981
2002
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #600 of 1,518 articles by views in Oxford University Press eBooks

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 1,518 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract The first edition of Analysis for Longitudinal Data has become a classic. Describing the statistical models and methods for the analysis of longitudinal data, it covers both the underlying statistical theory of each method, and its application to a range of examples from the agricultural and biomedical sciences. The main topics discussed are design issues, exploratory methods of analysis, linear models for continuous data, general linear models for discrete data, and models and methods for handling data and missing values. Under each heading, worked examples are presented in parallel with the methodological development, and sufficient detail is given to enable the reader to reproduce the author's results using the data-sets as an appendix. This new edition of Analysis for Longitudinal Data provides a thorough and expanded revision of this important text. It includes two new chapters; the first discusses fully parametric models for discrete repeated measures data, and the second explores statistical models for time-dependent predictors.
Reference Key
openalex_W4388319941 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Peter J. Diggle, Patrick J. Heagerty, Kung‐Yee Liang, Scott L. Zeger
Journal Oxford University Press eBooks
Year 2002
DOI
10.1093/oso/9780198524847.001.0001
URL
https://doi.org/10.1093/oso/9780198524847.001.0001
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.