Applying Stochastic Process Model to Imputation of Censored Longitudinal Data
Longitudinal data are widely used in medicine, demography, sociology and other areas. Incomplete observations in such data often confound the results of analysis. A plethora of data imputation methods have already been proposed to alleviate this problem. The Stochastic Process Model (SPM) represents a general framework for modeling joint evolution of repeatedly measured variables and time-to-event outcome typically observed in longitudinal studies of aging, health and longevity. It is perfectly suitable for imputing missing observations in censored longitudinal data. We applied SPM to the problem of imputation of censored missing longitudinal data. This model was applied both to the Framingham Heart Study and Cardiovascular Health Study data as well as to simulated datasets. We also present an R package stpm designed for this purpose.
