Estimation of variance components for censored data with applications to blood pressure variability

Ideally, the basis for estimation of variance components is large random samples selected from a well-defined reference population. Some large biomedical studies, however, consist of a random sample (S) of individuals ascertained at an initial visit, with a selected subsample from S seen on one or more follow-up visits. In this setting, the usual formulae for estimation of variance components are problematic since they do not take into account the censored nature of the data. For this purpose, we consider both maximum likelihood and moments estimation methods that take the censoring into account, and we compare their performance, in terms of bias and mean squared error, with that of the usual variance components estimators that ignore censoring. We find the maximum likelihood estimators somewhat more efficient than method of moments estimators, provided that the assumption of multivariate normality is met; furthermore, these estimators are substantially more efficient than those that ignore the censoring. It is important to record data on all individuals, even those who do not meet screening criteria; one can estimate between- and within-person variance more accurately with use of all available data. The resulting estimates are crucial in calculation of power for the design of future studies.