Humana Biostatistical Methods: 184 (Methods in Molecular Biology, 184)

Leading biostatisticians and biomedical researchers describe many of the key techniques used to solve commonly occurring data analytic problems in molecular biology, and demonstrate how these methods can be used in the development of new markers for exposure to a risk factor or for disease outcomes. 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. Review "The book presents the reader with a representative sampling of biostatistical applications that are used in the study of molecular biology with an emphasis on the development of biomarkers for clinical applications." - Biotech Software & Internet Report "In 10 chapters the authors fulfill the aim to provide sufficient information to cover area of both biostatistics and molecular biology...authors pay attention to give accurate explanation of basic biological and statistical terms. This is worthwhile for statisticians who want to work in molecular biology and for molecular biologists to be in touch with recent advances in comprehensive high-throughput laboratory methods." -Neoplasma "...an excellent overview of the most promising statistical approaches for analyzing microarray data...recommended to be read both by molecular biologists and biostatisticians." - Statist. Med. From the Back Cover 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.