Statistical Contributions to Molecular Biology -- Linking Image Quantitation and Data Analysis -- to Microarray Experimentation and Analysis -- Statistical Methods for Proteomics -- Statistical Methods for Assessing Biomarkers -- Power and Sample Size Considerations in Molecular Biology -- Models for Determining Genetic Susceptibility and Predicting Outcome -- Multiple Tests for Genetic Effects in Association Studies -- Statistical Considerations in Assessing Molecular Markers for Cancer Prognosis and Treatment Efficacy -- Power of the Rank Test for Multi-Strata Case-Control Studies with Ordinal Exposure Variables.

The use of biostatistical techniques in molecular biology has grown tremendously in recent years and is now essential for the correct interpretation of a wide variety of laboratory studies. In Biostatistical Methods, a panel of leading biostatisticians and biomedical researchers describe all the key techniques used to solve commonly occurring analytical problems in molecular biology, and demonstrate how these methods can identify new markers for exposure to a risk factor, or for determining disease outcomes. Major areas of application include microarray analysis, proteomic studies, image quantitation, determining new disease biomarkers, and designing studies with adequate levels of statistical power. In the case of genetic effects in human populations, the authors describe sophisticated statistical methods to control the overall false-positive rate when many statistical tests are used in linking particular alleles to the occurrence of disease. Other methods discussed are those used to validate statistical approaches for analyzing the E-D association, to study the associations between disease and the inheritance of particular genetic variants, and to examine real data sets. There are also useful recommendations for statistical and data management software (JAVA, Oracle, S-Plus, STATA, and SAS) . Accessible, state-of-the-art, and highly practical, Biostatistical Methods provides an excellent starting point both for statisticians just beginning work on problems in molecular biology, and for all molecular biologists who want to use biostatistics in genetics research designed to uncover the causes and treatments of disease.