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Principal Investigator:  Zhaohai Li, Sc.D.

This research project was 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 was proposed. Two types of random effects models based on whether outcomes are continuous or binary (yes/no) were developed theoretically and numerically. These statistical methods were compared with the existing methods through analysis and simulation. These methods were applied to epidemiological and clinical oncology research data sets.

Grant from NIH/NCI 5-R29-CA64363, 1997-2000.)