Colin O. Wu, Office of Biostatistics Research, National Heart, Lung and Blood Institute National Institutes of Health Bethesda, MD 20892, wuc@nhlbi.nih.gov
An important objective of longitudinal analysis is to estimate the conditional distributions of an outcome variable through a regression model. The approaches based on modeling the conditional means are not appropriate for this task when the conditional distributions are skewed or can not be approximated by a normal distribution through a known transformation.
We study a class of time-varying transformation models and a two-step smoothing method for the estimation of the conditional distribution functions. Based our models, we propose a rank-tracking probability and a rank-tracking probability ratio to measure the strength of tracking ability of an outcome variable at two different time points. Our models and estimation method can be applied to a wide range of scientific objectives that can not be evaluated by the conditional mean based models.
We derive the asymptotic properties for the two-step local polynomial estimators of the conditional distribution functions. Finite sample properties of our procedures are investigated through a simulation study. Application of our models and estimation method is demonstrated through a large epidemiological study of childhood growth and blood pressure.
*This is the joint work with Xin Tian (OBR/NHLBI)
