asynchDataTI {AsynchLong}

Generated Asynchronous Longitudinal Data with Time-Invariant Coefficients

Description

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 = 1.5, and \epsilon(t) is Gaussian with mean 0, variance 1 and \mathrm{cov}(\epsilon(s),\epsilon(t)) = 2^{-|t-s|}. Covariates are stored as TI.x. Responses are stored as TI.y.

Format

TI.x is a data frame with 2014 observations on the following 3 variables.

ID

patient identifier, there are 400 patients.

t

the covariate observation times

X1

the covariate measured at observation time t

TI.y is a data frame with 2101 observations on the following 3 variables.

ID

patient identifier, there are 400 patients.

t

the response observation times.

Y

the response measured at time t.

Source

Generated by Shannon T. Holloway in R.

References

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.