Applied longitudinal analysis (Record no. 187309)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 08676 a2200277 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240619131404.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 240619b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9780470380277 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 519.53 |
| Item number | FIT |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Fitzmaurice, Garrett M. |
| 9 (RLIN) | 36238 |
| 245 ## - TITLE STATEMENT | |
| Title | Applied longitudinal analysis |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 2nd ed. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Name of publisher, distributor, etc. | Wiley, |
| Date of publication, distribution, etc. | 2011 |
| Place of publication, distribution, etc. | Hoboken, New Jersey : |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xxv, 701 p. : ill. ; |
| Dimensions | 25 cm. |
| 440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE | |
| Title | Wiley series in probability and statistics |
| 9 (RLIN) | 36239 |
| 500 ## - GENERAL NOTE | |
| General note | contents note: <br/><br/>pt. I Introduction to Longitudinal and Clustered Data<br/>1.Longitudinal and Clustered Data<br/>1.1.Introduction<br/>1.2.Longitudinal and Clustered Data<br/>1.3.Examples<br/>1.4.Regression Models for Correlated Responses<br/>1.5.Organization of the Book<br/>1.6.Further Reading<br/>2.Longitudinal Data: Basic Concepts<br/>2.1.Introduction<br/>2.2.Objectives of Longitudinal Analysis<br/>2.3.Defining Features of Longitudinal Data<br/>2.4.Example: Treatment of Lead-Exposed Children Trial<br/>2.5.Sources of Correlation in Longitudinal Data<br/>2.6.Further Reading<br/>Problems<br/>pt. II Linear Models for Longitudinal Continuous Data<br/>3.Overview of Linear Models for Longitudinal Data<br/>3.1.Introduction<br/>3.2.Notation and Distributional Assumptions<br/>3.3.Simple Descriptive Methods of Analysis<br/>3.4.Modeling the Mean<br/>3.5.Modeling the Covariance<br/>3.6.Historical Approaches<br/>3.7.Further Reading<br/>4.Estimation and Statistical Inference<br/>4.1.Introduction<br/>Contents note continued: 4.2.Estimation: Maximum Likelihood<br/>4.3.Missing Data Issues<br/>4.4.Statistical Inference<br/>4.5.Restricted Maximum Likelihood (REML) Estimation<br/>4.6.Further Reading<br/>5.Modeling the Mean: Analyzing Response Profiles<br/>5.1.Introduction<br/>5.2.Hypotheses Concerning Response Profiles<br/>5.3.General Linear Model Formulation<br/>5.4.Case Study<br/>5.5.One-Degree-of-Freedom Tests for Group by Time Interaction<br/>5.6.Adjustment for Baseline Response<br/>5.7.Alternative Methods of Adjusting for Baseline Response<br/>5.8.Strengths and Weaknesses of Analyzing Response Profiles<br/>5.9.Computing: Analyzing Response Profiles Using PROC MIXED in SAS<br/>5.10.Further Reading<br/>6.Modeling the Mean: Parametric Curves<br/>6.1.Introduction<br/>6.2.Polynomial Trends in Time<br/>6.3.Linear Splines<br/>6.4.General Linear Model Formulation<br/>6.5.Case Studies<br/>6.6.Computing: Fitting Parametric Curves Using PROC MIXED in SAS<br/>6.7.Further Reading<br/>Contents note continued: 7.Modeling the Covariance<br/>7.1.Introduction<br/>7.2.Implications of Correlation among Longitudinal Data<br/>7.3.Unstructured Covariance<br/>7.4.Covariance Pattern Models<br/>7.5.Choice among Covariance Pattern Models<br/>7.6.Case Study<br/>7.7.Discussion: Strengths and Weaknesses of Covariance Pattern Models<br/>7.8.Computing: Fitting Covariance Pattern Models Using PROC MIXED in SAS<br/>7.9.Further Reading<br/>8.Linear Mixed Effects Models<br/>8.1.Introduction<br/>8.2.Linear Mixed Effects Models<br/>8.3.Random Effects Covariance Structure<br/>8.4.Two-Stage Random Effects Formulation<br/>8.5.Choice among Random Effects Covariance Models<br/>8.6.Prediction of Random Effects<br/>8.7.Prediction and Shrinkage<br/>8.8.Case Studies<br/>8.9.Computing: Fitting Linear Mixed Effects Models Using PROC MIXED in SAS<br/>8.10.Further Reading<br/>9.Fixed Effects versus Random Effects Models<br/>9.1.Introduction<br/>9.2.Linear Fixed Effects Models<br/>Contents note continued: 9.3.Fixed Effects versus Random Effects: Bias-Variance Trade-off<br/>9.4.Resolving the Dilemma of Choosing Between Fixed and Random Effects Models<br/>9.5.Longitudinal and Cross-sectional Information<br/>9.6.Case Study<br/>9.7.Computing: Fitting Linear Fixed Effects Models Using PROC GLM in SAS<br/>9.8.Computing: Decomposition of Between-Subject and Within-Subject Effects Using PROC MIXED in SAS<br/>9.9.Further Reading<br/>10.Residual Analyses and Diagnostics<br/>10.1.Introduction<br/>10.2.Residuals<br/>10.3.Transformed Residuals<br/>10.4.Aggregating Residuals<br/>10.5.Semi-Variogram<br/>10.6.Case Study<br/>10.7.Summary<br/>10.8.Further Reading<br/>pt. III Generalized Linear Models for Longitudinal Data<br/>11.Review of Generalized Linear Models<br/>11.1.Introduction<br/>11.2.Salient Features of Generalized Linear Models<br/>11.3.Illustrative Examples<br/>11.4.Ordinal Regression Models<br/>11.5.Overdispersion<br/>Contents note continued: 11.6.Computing: Fitting Generalized Linear Models Using PROC GENMOD in SAS<br/>11.7.Overview of Generalized Linear Models<br/>11.8.Further Reading<br/>12.Marginal Models: Introduction and Overview<br/>12.1.Introduction<br/>12.2.Marginal Models for Longitudinal Data<br/>12.3.Illustrative Examples of Marginal Models<br/>12.4.Distributional Assumptions for Marginal Models<br/>12.5.Further Reading<br/>13.Marginal Models: Generalized Estimating Equations (GEE)<br/>13.1.Introduction<br/>13.2.Estimation of Marginal Models: Generalized Estimating Equations<br/>13.3.Residual Analyses and Diagnostics<br/>13.4.Case Studies<br/>13.5.Marginal Models and Time-Varying Covariates<br/>13.6.Computing: Generalized Estimating Equations Using PROC GENMOD in SAS<br/>13.7.Further Reading<br/>14.Generalized Linear Mixed Effects Models<br/>14.1.Introduction<br/>14.2.Incorporating Random Effects in Generalized Linear Models<br/>14.3.Interpretation of Regression Parameters<br/>Contents note continued: 14.4.Overdispersion<br/>14.5.Estimation and Inference<br/>14.6.A Note on Conditional Maximum Likelihood<br/>14.7.Case Studies<br/>14.8.Computing: Fitting Generalized Linear Mixed Models Using PROC GLIMMIX in SAS<br/>14.9.Further Reading<br/>15.Generalized Linear Mixed Effects Models: Approximate Methods of Estimation<br/>15.1.Introduction<br/>15.2.Penalized Quasi-Likelihood<br/>15.3.Marginal Quasi-Likelihood<br/>15.4.Cautionary Remarks on the Use of PQL and MQL<br/>15.5.Case Studies<br/>15.6.Computing: Fitting GLMMs Using PROC GLIMMIX in SAS<br/>15.7.Basis of PQL and MQL Approximations<br/>15.8.Further Reading<br/>16.Contrasting Marginal and Mixed Effects Models<br/>16.1.Introduction<br/>16.2.Linear Models: A Special Case<br/>16.3.Generalized Linear Models<br/>16.4.Simple Numerical Illustration<br/>16.5.Case Study<br/>16.6.Conclusion<br/>16.7.Further Reading<br/>pt. IV Missing Data and Dropout<br/>Contents note continued: 17.Missing Data and Dropout: Overview of Concepts and Methods<br/>17.1.Introduction<br/>17.2.Hierarchy of Missing Data Mechanisms<br/>17.3.Implications for Longitudinal Analysis<br/>17.4.Dropout<br/>17.5.Common Approaches for Handling Dropout<br/>17.6.Bias of Last Value Carried Forward Imputation<br/>17.7.Further Reading<br/>18.Missing Data and Dropout: Multiple Imputation and Weighting Methods<br/>18.1.Introduction<br/>18.2.Multiple Imputation<br/>18.3.Inverse Probability Weighted Methods<br/>18.4.Case Studies<br/>18.5."Sandwich" Variance Estimator Adjusting for Estimation of Weights<br/>18.6.Computing: Multiple Imputation Using PROC MI in SAS<br/>18.7.Computing: Inverse Probability Weighted (IPW) Methods in SAS<br/>18.8.Further Reading<br/>pt. V Advanced Topics for Longitudinal and Clustered Data<br/>19.Smoothing Longitudinal Data: Semiparametric Regression Models<br/>19.1.Introduction<br/>19.2.Penalized Splines for a Univariate Response<br/>19.3.Case Study<br/>Contents note continued: 19.4.Penalized Splines for Longitudinal Data<br/>19.5.Case Study<br/>19.6.Fitting Smooth Curves to Individual Longitudinal Data<br/>19.7.Case Study<br/>19.8.Computing: Fitting Smooth Curves Using PROC MIXED in SAS<br/>19.9.Further Reading<br/>20.Sample Size and Power<br/>20.1.Introduction<br/>20.2.Sample Size for a Univariate Continuous Response<br/>20.3.Sample Size for a Longitudinal Continuous Response<br/>20.4.Sample Size for a Longitudinal Binary Response<br/>20.5.Summary<br/>20.6.Computing: Sample Size Calculation Using Pseudo-Data<br/>20.7.Further Reading<br/>21.Repeated Measures and Related Designs<br/>21.1.Introduction<br/>21.2.Repeated Measures Designs<br/>21.3.Multiple Source Data<br/>21.4.Case Study 1: Repeated Measures Experiment<br/>21.5.Case Study 2: Multiple Source Data<br/>21.6.Summary<br/>21.7.Further Reading<br/>22.Multilevel Models<br/>22.1.Introduction<br/>22.2.Multilevel Data<br/>22.3.Multilevel Linear Models<br/>22.4.Multilevel Generalized Linear Models<br/>Contents note continued: 22.5.Summary<br/>22.6.Further Reading. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | <br/>Summary:<br/><br/>"Since the publication of the first edition, the authors have solicited feedback from both the instructors who use the book as a text for their courses as well as the researchers who use the book as a resource for their research. Thus, the improved Second Edition of Applied Longitudinal Analysis features many additions and revisions based on the feedback of readers, making it the go-to reference for applied use in public health, epidemiology, and pharmaceutical sciences" |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Biometry - methods |
| 9 (RLIN) | 36452 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Longitudinal methods |
| 9 (RLIN) | 36453 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Multivariate analysis |
| 9 (RLIN) | 36454 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Regression analysis |
| 9 (RLIN) | 36455 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Medical statistics |
| 9 (RLIN) | 36456 |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Laird, Nan M. |
| 9 (RLIN) | 36457 |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Ware, James H. |
| 9 (RLIN) | 36458 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Books |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Source of acquisition | Cost, normal purchase price | Full call number | Barcode | Date last seen | Cost, replacement price | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Library and Information Centre | Library and Information Centre | On Display | 11/06/2024 | 13 | 0.00 | 519.53 FIT | 30580 | 11/06/2024 | 12884.18 | 11/06/2024 | Books |