The Tutorials in Biostatistics have become a very popularfeature of the prestigious Wiley journal, Statistics inMedicine (SIM). The introductory style and practical focus makethem accessible to a wide audience including medical practitionerswith limited statistical knowledge. This book represents thesecond of two volumes presenting the best tutorials published inSIM, focusing on statistical modeling of complex data. Topicsinclude clustered data, hierarchical models, mixed models, geneticmodeling, and meta-analysis. Each tutorial is focused on a medicalproblem, has been fully peer-reviewed and edited, and is authoredby leading researchers in biostatistics. Many articlesinclude an appendix on the latest developments since publication inthe 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 graduatestudents of biostatistics.

Preface.Preface to Volume 2.Part I: MODELLING A SINGLE DATA SET.1.1 Clustered Data.Extending the Simple Linear Regression Model to Account forCorrelated Responses: An Introduction to Generalized EstimatingEquations 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 etal).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 UnbalancedRepeated Measures and Longitudinal Data (Avital Cnaan et al).Modelling Covariance Structure in the Analysis of RepeatedMeasures Data (Ramon C. Littell et al).Covariance Models for Nested Repeated Measures Data: Analysis ofOvarian Steroid Secretion Data (Taesung Park and Young JackLee).1.4 Likelihood Modelling.Likelihood Methods for Measuring Statistical Evidence (JeffreyD. 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 andMeta-Regression (Hans C. van Houwelingen et al).Part III: MODELLING GENETIC DATA: STATISTICALGENETICS.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 (JayaM. Satagopan and Katherine S. Panageas).Part IV: DATA REDUCTION OF COMPLEX DATA SETS.Statistical Approaches to Human Brain Mapping by FunctionalMagnetic Resonance Imaging (Nicholas Lange).Disease Map Reconstruction (Andrew B. Lawson).PART V: SIMPLIFIED PRESENTATION OF MULTIVARIATE DATA.Presentation of Multivariate Data for Clinical Use: TheFramingham Study Risk Score Functions (Lisa M. Sullivan et al).