| Type: | Package |
| Title: | Censored Data Imputation for Direct Modeling |
| Version: | 0.1.2 |
| Description: | Impute the survival times for censored observations based on their conditional survival distributions derived from the Kaplan-Meier estimator. 'CondiS' can replace the censored observations with the best approximations from the statistical model, allowing for direct application of machine learning-based methods. When covariates are available, 'CondiS' is extended by incorporating the covariate information through machine learning-based regression modeling ('CondiS_X'), which can further improve the imputed survival time. |
| License: | GPL-2 |
| Encoding: | UTF-8 |
| Depends: | R (≥ 3.6) |
| Imports: | caret, survival, kernlab, purrr, tidyverse, survminer |
| NeedsCompilation: | no |
| Suggests: | rmarkdown, knitr |
| VignetteBuilder: | knitr |
| RoxygenNote: | 7.1.2 |
| Packaged: | 2022-04-17 02:48:46 UTC; YWang70 |
| Author: | Yizhuo Wang  [aut,
cre],
Ziyi Li [aut],
Xuelin Huang [aut],
Christopher Flowers [ctb] |
| Maintainer: | Yizhuo Wang <ywang70@mdanderson.org> |
| Repository: | CRAN |
| Date/Publication: | 2022-04-17 03:12:29 UTC |
CondiS Function
Description
This function allows you to impute survival time.
Usage
CondiS(time, status, tmax)
Arguments
time |
The follow up time for right-censored data. |
status |
The censoring indicator, normally 0=right censored, 1=event at time. |
tmax |
A self-defined time-of-interest point; if left undefined, then it is defaulted as the maximum follow up time. |
CondiS-X Function
Description
This function allows you to improve the imputed survival time by incorporating covariate information.
Usage
CondiS_X(pred_time, status, covariates, method)
Arguments
pred_time |
The imputed follow up time for right-censored data. |
status |
The censoring indicator, normally 0=right censored, 1=event at time. |
covariates |
The additional patient data that is presumably associated with the survival time. |
method |
Choose from 8 machine learning algorithms; the default is "glm". |