Analysis of Longitudinal Data with Unequally Spaced Observations and Time- Dependent Correlated Errors

Abstract

A linear model for repeated measurements is proposed in which the correlation structure includes a transformation of the time scale. This transformation can produce nonstationary covariance structures within subjects with stationarity as a special case. Restricted maximum likelihood methods for parameter estimation are discussed. The method is applied to simulated data as well as speech recognition data from the Iowa Cochlear Implant Project. The growth curve for this audiologic performance measure is shown together with estimates of the standard errors of predictions at given months.