Computational Methods in Biomedical Research

Using real-world data from disease areas, such as cancer and HIV, this volume explores important computational statistical methods that are employed in biomedical research. It discusses microarray data analysis, machine learning techniques, and mass spectrometry-based protein profiling. The book also uses state space models to predict US cancer mortality rates and describes the application of multistate models to analyze multiple failure times. It covers various Bayesian techniques, the sequential monitoring of randomization tests, mixed-effects models, classification rules for repeated measures data, and estimation methods for analyzing longitudinal data.