Statistical methodology is being applied and developed for longitudinal studies and other studies of aging. The research program focuses on several types of statistical models: 1) longitudinal mixed-effects regression models which consider both within- and between-subject variation in analyzing the repeated measurements for all individuals in the study population, 2) survival analysis for studying risk factors in prospective studies, 3) multiple comparisons for testing group differences in experimental or observational designs, 4) mixture models for describing age changes in distributions of biological markers, and 5) experimental design. Other techniques used include Bayesian, maximum likelihood and numerical computing methods. A major emphasis of the research program is the development of methods which yield cogent yet easily understood results when applied to data. The effect of measurement error bias on risk factor analyses has been investigated using a Monte Carlo simulation study. Various methods of representing the baseline value of a risk factor have been examined, including the usual single baseline measurement, means of multiple baseline measurements, and predicted values from repeated measurements using mixed-effects regression analyses. Results show that individual predicted values from the mixed-effects model provide a more accurate measure of the strength of the relation between the level of a risk factor and the occurrence of an endpoint such as morbidity or mortality. In a second area, a nonlinear mixed-effects model was used to describe longitudinal data on changes in PSA level in men with prostate cancer. The model is piecewise with a linear phase and an exponential phase of PSA increases. This allows for the estimation of the time before diagnosis at which the transition between the slow linear phase and the rapid exponential phase occurs. The research program has extended earlier methods of longitudinal data analysis, introduced novel methods of describing the natural history of aging, and developed new approaches toward the use of longitudinal data in epidemiological and biomedical studies of aging and associated disease states.
