Package 'ExtendedABSurvTDC'

Title: Survival Analysis using Indicators under Time Dependent Covariates
Description: Survival analysis is employed to model time-to-event data. This package examines the relationship between survival and one or more predictors, termed as covariates, which can include both treatment variables (e.g., season of birth, represented by indicator functions) and continuous variables. To this end, the Cox-proportional hazard (Cox-PH) model, introduced by Cox in 1972, is a widely applicable and commonly used method for survival analysis. This package enables the estimation of the effect of randomization for the treatment variable to account for potential confounders, providing adjustment when estimating the association with exposure. It accommodates both fixed and time-dependent covariates and computes survival probabilities for lactation periods in dairy animals. The package is built upon the algorithm developed by Klein and Moeschberger (2003) <DOI:10.1007/b97377>.
Authors: Dr. Himadri Ghosh [aut, cre], Mr. Saikath Das [aut], Dr. Md Yeasin [aut], Dr. Debopam Rakshit [aut]
Maintainer: Dr. Himadri Ghosh <[email protected]>
License: GPL-3
Version: 0.1.0
Built: 2026-05-23 15:11:33 UTC
Source: https://github.com/cran/ExtendedABSurvTDC

Help Index

Data Preparation

Description

Data preparation for ABCoxPH

Usage

DataPrep(data, t_int, max_lac)

Arguments

data

Raw data sets
t_int

No of days to be considered as single time interval (Default value: 90)
max_lac

Maximum no of lactation to be considered for data preparation (Default value: Max Lactation)

Value

  • wide_data - Processed data for ABCoxPH

References

  • J.D. Kalbfleisch and R.L. Prentice (1980). The statistical analysis of failure time data. John Wiley & Sons, Inc., New York, 1980. <doi:10.1002/9781118032985>

  • J.P. Klein and M L. Moeschberger (2003). Survival Analysis: Techniques for Censored and Truncated Data. Springer New York. <doi:10.1007/b97377>

Examples

library("ExtendedABSurvTDC")
load(system.file("extdata", "data.RData", package = "ExtendedABSurvTDC"))
PropData<-DataPrep(data =as.data.frame(data_test))

Extended Cox-PH Model for Animal Breeding

Description

Data preparation for ABCoxPH

Usage

ExtendedABSurvTDC(wide_data, lact)

Arguments

wide_data

Dataset from DataPrep function
lact

Number of lactation to be used for model building

Value

  • Cox_Model - ExtendedABCoxPH model

  • LongData- Long data

References

  • J.D. Kalbfleisch and R.L. Prentice (1980). The statistical analysis of failure time data. John Wiley & Sons, Inc., New York, 1980. DOI: 10.1002/9781118032985

  • J.P. Klein and M L. Moeschberger (2003). Survival Analysis: Techniques for Censored and Truncated Data. Springer New York, DOI:10.1007/b97377

Examples

library("ExtendedABSurvTDC")
load(system.file("extdata", "data.RData", package = "ExtendedABSurvTDC"))
PropData<-DataPrep(data =as.data.frame(data_test))
ExtendedABSurvTDC(PropData)

ExtendedABCoxPH Prediction

Description

Prediction for ExtendedABCoxPH model

Usage

ExtendedCoxPred(Model, NewData)

Arguments

Model

ExtendedABCoxPH model
NewData

New data

Value

  • SurvProb - Survival probabilities

References

  • J.D. Kalbfleisch and R.L. Prentice (1980). The statistical analysis of failure time data. John Wiley & Sons, Inc., New York, 1980. DOI: 10.1002/9781118032985

  • J.P. Klein and M L. Moeschberger (2003). Survival Analysis: Techniques for Censored and Truncated Data. Springer New York, DOI:10.1007/b97377

Examples

library("ExtendedABSurvTDC")
load(system.file("extdata", "data.RData", package = "ExtendedABSurvTDC"))
PropData<-DataPrep(data =as.data.frame(data_test))
model<-ExtendedABSurvTDC(PropData)
Lact_1<-c("Yes","Yes","Yes","No","No","No","No","No","No","No","No")
Lact_2<-c("No","No","No","No","Yes","Yes","No","No","No","No","No")
Lact_3<-c("No","No","No","No","No","No","No","No","Yes","Yes","Yes")
Lact_4<-c("No","No","No","No","No","No","No","No","No","No","No")
Lact_5<-c("No","No","No","No","No","No","No","No","No","No","No")
Lact_6<-c("No","No","No","No","No","No","No","No","No","No","No")
Lact_7<-c("No","No","No","No","No","No","No","No","No","No","No")
Lact_8<-c("No","No","No","No","No","No","No","No","No","No","No")
Lact_9<-c("No","No","No","No","No","No","No","No","No","No","No")
ndata<- data.frame(Lact_1,Lact_2,Lact_3,Lact_4,Lact_5,Lact_6,Lact_7,
                   Lact_8,Lact_9)
NewData<-ndata
HYS<-2033
AFC <- 1400
Y=as.factor(1)
S=as.factor(1)
H=as.factor(1)
NewData_default <- data.frame(AFC, Y, S, NewData) # Data for default argument of "factors"
ExtendedCoxPred(Model=model, NewData=NewData_default)