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 many of the key techniques used to solve commonly occurring data analytical problems in molecular biology, and Jemonstrate how these methods can help identify new markers for exposure to a risk factor, or identify a particular disease outcome. Major areas of application include microarray analysis, proteomic studies, image quantitation, genetic susceptibility and association, evaluation of new biomarkers, and power analysis and sample size. 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 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 evaluate the clinical utility of tumor makers. There are also useful recommendations for statistical and data management software (S-Plus, STATA, SAS, Oracle, JAVA).
Accessible, state-of-the-art, and highly practical,biostatistical methods provide 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.
• Applies biostatistics to the study of genetics and the causes and treatment of disease
• Covers many recent developments in image quantitation, microarrays, and proteomics
• Describes advanced statistical analysis techniques and applies them to real data
• Discusses the use of data management software: S-Plus, STATA,SAS,Oracle, and JAVA
• Enables molecular biologists to carry out advanced biostatistical analyses
• Includes numerous data sets and software recommendations
Statistical Contributions to Molecular Biology. Linking Image Quantitation and Data Analysis. Introduction 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. Index