Tutorials in Biostatistics series has become a very popular feature of the journal,
Statistics in Medicine. The tutorials are accessible, self–contained and of a uniformly high quality. This book is the second of two volumes presenting the best tutorials published in the journal, focusing on statistical modelling of complex medical data. Topics include clustered data, hierarchical models, mixed models, genetic modelling, and meta–analysis. Each tutorial has been fully peer–reviewed and edited, and authored by leading researchers in biostatistics.
- Presents a collection of tutorials in biostatistics, taken from the prestigious Wiley journal, Statistics in Medicine.
- Focuses on modelling of complex medical data, including clustered data, hierarchical models, mixed models, genetic modelling, and meta–analysis.
- Each tutorial has been fully peer reviewed and edited to ensure a high level of quality and to maintain the practical focus.
- Suitable for medical practitioners with a good understanding of statistics.
- Each tutorial has been authored by leading researchers in the field.
The Tutorials in Biostatistics volumes provide an authoritative and readable introduction to a wide range of topics suitable for statisticians working in medical research, as well as statistically–minded clinicians, biologists, epidemiologists, and geneticists. Graduate students of biostatistics will find they make excellent reference volumes to support their study.
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).