Statistical Modelling of Complex Medical Data

Herausgegeben:D'Agostino, Ralph B.

• Gebundenes Buch

Produktbeschreibung

This book represents the second of two volumes presenting the best tutorials published in the journal Statistics in Medicine (SIM), focusing on statistical modeling of complex data. Topics include clustered data, hierarchical models, mixed models, genetic modeling, and meta analysis.

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The Tutorials in Biostatistics have become a very popular feature of the prestigious Wiley journal, Statistics in Medicine (SIM). The introductory style and practical focus make them accessible to a wide audience including medical practitioners with limited statistical knowledge. This book represents the second of two volumes presenting the best tutorials published in SIM, focusing on statistical modeling of complex data. Topics include clustered data, hierarchical models, mixed models, genetic modeling, and meta-analysis. Each tutorial is focused on a medical problem, has been fully peer-reviewed and edited, and is authored by leading researchers in biostatistics. Many articles include an appendix on the latest developments since publication in the journal and additional references.

This will appeal to statisticians working in medical research, as well as statistically-minded clinicians, biologists, epidemiologists and geneticists. It will also appeal to graduate students of biostatistics.

Produktdetails

• Verlag: Wiley & Sons
• 1. Auflage
• Seitenzahl: 496
• Erscheinungstermin: 30. November 2004
• Englisch
• Abmessung: 268mm x 210mm x 34mm
• Gewicht: 1260g
• ISBN-13: 9780470023709
• ISBN-10: 0470023708
• Artikelnr.: 13079363

Autorenporträt

Ralph D'Agostino, Boston University, USA One of the editors of Statistics in Medicine and editor of the Tutorials in Biostatistics series in the journal.

Inhaltsangabe

Preface. Preface to Volume 2. Part I: MODELLING A SINGLE DATA SET. 1.1
Clustered Data. Extending the Simple Linear Regression Model to Account for
Correlated Responses: An Introduction to Generalized Estimating Equations
and Multi-Level Mixed Modelling (Paul Burton et al). 1.2 Hierarchical
Modelling. An Introduction to Hierarchical Linear Modelling (Lisa M.
Sullivan et al). Multilevel Modelling of Medical Data (Harvey Goldstein et
al). Hierarchical Linear Models for the Development of Growth Curves: An
Example with Body Mass Index in Overweight /Obese Adults (Moonseong Heo et
al). 1.3 Mixed Models. Using the General Linear Mixed Model to Analyse
Unbalanced Repeated Measures and Longitudinal Data (Avital Cnaan et al).
Modelling Covariance Structure in the Analysis of Repeated Measures Data
(Ramon C. Littell et al). Covariance Models for Nested Repeated Measures
Data: Analysis of Ovarian Steroid Secretion Data (Taesung Park and Young
Jack Lee). 1.4 Likelihood Modelling. Likelihood Methods for Measuring
Statistical Evidence (Jeffrey D. Blume). Part II: MODELLING MULTIPLE DATA
SETS: META-ANALYSIS. Meta-Analysis: Formulating, Evaluating, Combining, and
Reporting (Sharon-Lise T. Normand ). Advanced Methods in Meta-Analysis:
Multivariate Approach and Meta-Regression (Hans C. van Houwelingen et al).
Part III: MODELLING GENETIC DATA: STATISTICAL GENETICS. Genetic
Epidemiology: A Review of the Statistical Basis (E. A. Thompson). Genetic
Mapping of Complex Traits (Jane M. Olson et al). A Statistical Perspective
on Gene Expression Data Analysis (Jaya M. Satagopan and Katherine S.
Panageas). Part IV: DATA REDUCTION OF COMPLEX DATA SETS. Statistical
Approaches to Human Brain Mapping by Functional Magnetic Resonance Imaging
(Nicholas Lange). Disease Map Reconstruction (Andrew B. Lawson). PART V:
SIMPLIFIED PRESENTATION OF MULTIVARIATE DATA. Presentation of Multivariate
Data for Clinical Use: The Framingham Study Risk Score Functions (Lisa M.
Sullivan et al).

Rezensionen

"The articles within the volume are self contained, well written, and accessible to readers of widely ranging backgrounds. These volumes should be extremely valuable to practitioners " ( Journal of the American Statistical Association , December 2005) "The biostatistical practitioners who use a broad range of statistical methods would definitely find beneficial and useful to have Tutorials in Biostatistics " ( E STREAMS , September 2005)