this research project proposes to develop, implement and test random effects (RE) models (continuous and binary) to improve analysis of correlated data encountered in clinical oncology research. Correlated clinical data are common in meta analyses, family studies, and risk assessments. For example two summary statistics (e.g. means) from the same study are correlated data. Measurements of cancer risk for members within a family tend to be more alike than cancer risk measurements from members of different families. Assumption of a common random component shared by outcomes of the same study or members of the same family is proposed. Two types of random effects models based on whether outcomes are continuous or binary (yes/no) will be developed theoretically and numerically. These statistical methods will be compared with the existing methods through analysis and simulation. The new methods will be applied to epidemiological and clinical oncology research data sets. Grant from NCI 5-R29-CA64363, 1997-2000.