Tutorials in Biostatistics, Volume 2, Tutorials in Biostatistics: Statistical Modelling of Complex Medical Data

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).