dropout: Prostate Cancer Data: Part 2 - a simulated example of study drop-out process

Description

A simulated data set used in Kim et al. (2016) to illustrate the JPLM method. This data set was generated under settings mimicking the prostate cancer study. For detailed data generation settings, see Kim et al. (2016).

Arguments

Format

A data frame with 100 observations (n = 100 patients) on the following 4 variables.

ID2: a numeric vector of patient ID.
Status: a numeric (binary) vector indicating whether the study drop-out time (end of follow-up) was informative.
DropTime: a numeric vector of the end of follow-up.
logPSA.base2: a numeric vector of log(baseline PSA + 0.1).

Source

The prostate cancer data have been previously studied by Proust-Lima et al. (2008) and Taylor et al. (2013), among others.

References

Kim, S., Zeng, D., Taylor, J. M. G. (2016) Joint partially linear model for longitudinal data with informative drop-outs. Under revision 0, 000-000.

Proust-Lima, C., Taylor, J. M. G., Williams, S. G., Ankerst, D. P., Liu, N., Kestin, L. L., Bae, K., and Sandler, H. M. (2008) Determinants of change in prostate-specific antigen over time and its association with recurrence after external beam radiation therapy for prostate cancer in five large cohorts. International Journal of Radiation Oncology Biology Physics 72, 782-791.

Taylor, J. M. G., Part, Y., Ankerst, D. P., Proust-Lima, C., Williams, S., Kestin, L., Bae, K., Pickles, T., and Sandler, H. (2013) Real-time individual predictions of prostate cancer recurrence using joint models. Biometrics 69, 206-213.