For the purposes of the package examples, the data set was adapted from the numerical simulations of the original manuscript. Specifically, data was generated for 400 subjects. The number of observation times for the response was Poisson distributed with intensity rate 5, and similarly for the number of observation times for the covariates. Observation times are generated from a uniform distribution Unif(0,1) independently. The covariate process is Gaussian, with values at fixed time points being multivariate normal with mean 0, variance 1 and correlation \(\exp(-|t_{ij} - t_{ik}|)\). The responses were generated from \(Y(t) = \beta_0 + X(t)*\beta_1 + \epsilon(t)\), where \(\beta_0\) = 0.5, \(\beta_1\) = 0.4t + 0.5, and \(\epsilon(t)\) is Gaussian with mean 0, variance 1 and \(\mathrm{cov}(\epsilon(s),\epsilon(t)) = 2^{-|t-s|}\). Covariates are stored as TD.x. Responses are stored as TD.y.
TD.x is a data frame with 4052 observations on the following 3 variables.
IDpatient identifier, there are 400 patients.
tthe covariate observation times
X1the covariate measured at observation time t
TD.y is a data frame with 3939 observations on the following 3 variables.
IDpatient identifier, there are 400 patients.
tthe response observation times.
Ythe response measured at time t.
Cao, H., Zeng, D., and Fine, J. P. (2015) Regression Analysis of sparse asynchronous longitudinal data. Journal of the Royal Statistical Society: Series B, 77, 755-776.