dropout: Prostate Cancer Data: Part 2 - a simulated example of study...
In JointModel: Semiparametric Joint Models for Longitudinal and Counting Processes
Description
Format
Source
References
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).
Format
A data frame with 100 observations (n = 100 patients) on the following 4 variables.
ID2a numeric vector of patient ID.
Statusa numeric (binary) vector indicating whether the study drop-out time (end of follow-up) was informative.
DropTimea numeric vector of the end of follow-up.
logPSA.base2a 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.
JointModel documentation built on May 2, 2019, 12:40 p.m.
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Description Format Source References
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).
Format
A data frame with 100 observations (n = 100 patients) on the following 4 variables.
ID2a numeric vector of patient ID.
Statusa numeric (binary) vector indicating whether the study drop-out time (end of follow-up) was informative.
DropTimea numeric vector of the end of follow-up.
logPSA.base2a 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.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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