Package 'CondiS'

Title: Censored Data Imputation for Direct Modeling
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.
Authors: Yizhuo Wang [aut, cre] , Ziyi Li [aut], Xuelin Huang [aut], Christopher Flowers [ctb]
Maintainer: Yizhuo Wang <[email protected]>
License: GPL-2
Version: 0.1.2
Built: 2025-02-14 04:28:42 UTC
Source: https://github.com/cran/CondiS

Help Index

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".