SurvLongData: Generated Sparse Longitudinal Data

In SurvLong: Analysis of Proportional Hazards Model with Sparse Longitudinal Covariates

Generated Sparse Longitudinal Data

Description

For the purposes of the package examples, the dataset was adapted from the numerical simulations of the original manuscript.

Format

X is a data frame with 400 observations on the following 3 variables.

ID

patient identifier, there are 400 patients.

Time

the time to event or censoring

Delta

a numeric vector with 0 denoting censoring and 1 event

Z is a data frame with 3237 observations on the following 3 variables.

ID

patient identifier, there are 400 patients.

obsTime

the covariate observation times.

x1

the covariate generated through a piecewise constant function.

Details

Data was generated for 400 subjects. The total number of covariate observation times was Poisson distributed with intensity rate 8. The covariate observation times are generated from a uniform distribution Unif(0,1) independently. The covariate process is piecewise constant, with values being multivariate normal with mean 0, variance 1 and correlation \exp(-|i - j|/20). The survival time were generated from the Cox model \lambda(t | Z(r), r \le t) = \lambda_0 \exp(\beta Z(t)), where \beta = 1.5, and \lambda_0 = 1.0. Covariates are dataset Z. Event times and indicators are dataset X.

References

Cao H., Churpek M. M., Zeng D., Fine J. P. (2015). Analysis of the proportional hazards model with sparse longitudinal covariates. Journal of the American Statistical Association, 110, 1187-1196.

SurvLong documentation built on Oct. 1, 2023, 1:06 a.m.