| Title: | Model Averaging Prediction of Personalized Survival Probabilities |
|---|---|
| Description: | Provide methods for model averaging prediction of personalized survival probabilities. |
| Authors: | Mengyu Li [aut, cre] (ORCID: <https://orcid.org/0009-0008-4024-7573>), Jie Ding [aut] (ORCID: <https://orcid.org/0000-0002-6083-7529>), Xiaoguang Wang [aut] (ORCID: <https://orcid.org/0000-0001-7391-9788>) |
| Maintainer: | Mengyu Li <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.6.8 |
| Built: | 2025-02-21 05:31:18 UTC |
| Source: | https://github.com/stat-wangxg/survma |
RealData.ROT
RealData.ROTRealData.ROT
An object of class data.frame with 444 rows and 8 columns.
# An example of illustrating this dataset can be found in the help page of our # function \code{SurvMA.Fit()} by typing \code{?SurvMA.Fit()}. # It was originally extracted from the dataset rotteram in R package survival # The specific extractions can be done using the following R commands library(survival) RealData.ROT <- na.omit(rotterdam[ rotterdam$year %in% c(1992,1993),-c(1,2,5,6,7,12,13) ]) rownames(RealData.ROT) <- NULL colnames(RealData.ROT)[c(7,8)] <- c("time","delta")# An example of illustrating this dataset can be found in the help page of our # function \code{SurvMA.Fit()} by typing \code{?SurvMA.Fit()}. # It was originally extracted from the dataset rotteram in R package survival # The specific extractions can be done using the following R commands library(survival) RealData.ROT <- na.omit(rotterdam[ rotterdam$year %in% c(1992,1993),-c(1,2,5,6,7,12,13) ]) rownames(RealData.ROT) <- NULL colnames(RealData.ROT)[c(7,8)] <- c("time","delta")
SimData.APL: A simulated dataset based on a pre-specified partly linear additive Cox model.
SimData.APLSimData.APL
An object of class data.frame with 200 rows and 9 columns.
# An example of illustrating this dataset can be found in the help page of our # function \code{SurvMA.Fit()} by typing \code{?SurvMA.Fit()}.# An example of illustrating this dataset can be found in the help page of our # function \code{SurvMA.Fit()} by typing \code{?SurvMA.Fit()}.
SimData.TVC: A simulated dataset based on a pre-specified time-varying coefficients Cox model.
SimData.TVCSimData.TVC
An object of class data.frame with 150 rows and 8 columns.
# An example of illustrating this dataset can be found in the help page of our # function \code{SurvMA.Fit()} by typing \code{?SurvMA.Fit()}.# An example of illustrating this dataset can be found in the help page of our # function \code{SurvMA.Fit()} by typing \code{?SurvMA.Fit()}.
This package provides model averaging-based approaches that can be used to predict personalized survival probabilities.
Model averaging prediction of personalized survival probabilities (model fitting)
SurvMA.Fit( formula, sdata, submodel = c("PL", "TVC"), continuous = NULL, control = list(K.set = c(5:10), criterion = "AIC", method = "KM") )SurvMA.Fit( formula, sdata, submodel = c("PL", "TVC"), continuous = NULL, control = list(K.set = c(5:10), criterion = "AIC", method = "KM") )
formula |
a formula expression, of the form |
sdata |
a survival dataset (dataframe) in which to interpret the variables named in the |
submodel |
a character string defining the groups of candidate submodels, as introduced.
It can be |
continuous |
a vector of integers representing the positions of continuous covariates within |
control |
indicates more detailed control of the underlying model averaging fitting procedures.
It is a list of the following three arguments:
|
This is a function used to conduct model averaging prediction (model fitting) of personalized survival probabilities.
For obtaining specific predictions of personalized survival probabilities, see another function SurvMA.Predict().
The underlying methods are based on the paper titled "Semiparametric model averaging method for survival probability predictions of patients", which has been published in Mengyu Li and Xiaoguang Wang (2023) doi:10.1016/j.csda.2023.107759.
A list of fitted results that contain not only parameter estimates for all candidate submodels, but also optimal averaging weights (weights).
#----------------------------------------------------------# # Basic preparations before running subsequent examples #### #----------------------------------------------------------# rm(list=ls(all=TRUE)) ## library necessary packages library(SurvMA) library(survival) #' #--------------------------------------------------------------# # Simulated dataset: from partial linear additive Cox model #### #--------------------------------------------------------------# ## Pre-process the dataset # - load the dataset data(SimData.APL) head(SimData.APL,2) # - split the data into training and test datasets set.seed(1) train.index <- sort(sample(1:200,0.75*200)) sdata.train <- SimData.APL[train.index,] sdata.test <- SimData.APL[-train.index,] ## Fit the dataset via our model averaging method # - fit the data using provided R function SurvMA.Fit set.seed(1) sol.SurvMA.PL <- SurvMA.Fit( formula = Surv(time,delta) ~ X + U1 + U2 + U3 + U4 + U5 + U6, sdata = SimData.APL, submodel = "PL", continuous = 2:7 ) print(sol.SurvMA.PL$weights) # - do prediction using provided R function SurvMA.Predict predict.SurvMA.PL <- SurvMA.Predict( object = sol.SurvMA.PL, covariates = sdata.test[,-c(1,2)], times = round(quantile(sdata.test$time,c(0.25,0.50,0.75)),2) ) head(predict.SurvMA.PL$sprobs,2) #-----------------------------------------------------------# # Real dataset: using time-varying coefficient Cox model #### # - the breast cancer data originally from survival package #-----------------------------------------------------------# ## Pre-process the dataset # - load the dataset data(RealData.ROT) summary(RealData.ROT$time) table(RealData.ROT$delta) # - plot the Kaplan-Meier curve plot( survfit(Surv(time,delta) ~ 1, data = RealData.ROT), mark.time = TRUE, conf.int = TRUE, lwd=2, xlim = c(0,3200), ylim=c(0.4,1), xlab="Time (in Days)", ylab="Estimated Survival Probability" ) # - test time-varying effects TVC.Test <- cox.zph(coxph(Surv(time, delta)~., data = RealData.ROT)) print(TVC.Test) par(mfrow=c(2,3)) plot( TVC.Test, resid = FALSE, lwd = 2, xlab = "Time (in Days)", ylab = paste("Coefficient for",colnames(RealData.ROT)[1:6]) ) # - split the data into training and test datasets set.seed(1) n <- nrow(RealData.ROT) train.index <- sort(sample(1:n,0.75*n)) sdata.train <- RealData.ROT[train.index,] sdata.test <- RealData.ROT[-train.index,] ## Fit the dataset via our model averaging method # - fit the data using provided R function SurvMA.Fit set.seed(1) sol.SurvMA.ROT <- SurvMA.Fit( formula = Surv(time, delta) ~ age + meno + pgr + er + hormon + chemo, sdata = sdata.train, submodel = "TVC", continuous = NULL ) print(sol.SurvMA.ROT$weights) # - do prediction using provided R function SurvMA.Predict predict.SurvMA.ROT <- SurvMA.Predict( object = sol.SurvMA.ROT, covariates = sdata.test[,!(colnames(sdata.test) %in% c("time","delta"))], times = round(quantile(sdata.test$time,c(0.25,0.50,0.75))) ) head(predict.SurvMA.ROT$sprobs,2) #----------------------------------------------------------------# # Simulated dataset: from time-varying coefficients Cox model #### #----------------------------------------------------------------# ## Pre-process the dataset # - load the dataset data(SimData.TVC) head(SimData.TVC,2) # - split the data into training and test datasets set.seed(1) train.index <- sort(sample(1:150,0.75*150)) sdata.train <- SimData.TVC[train.index,] sdata.test <- SimData.TVC[-train.index,] ## Fit the dataset via our model averaging method # - fit the data using provided R function SurvMA.Fit set.seed(1) sol.SurvMA.TVC <- SurvMA.Fit( formula = Surv(time,delta) ~ Z1 + Z2 + Z3 + Z4 + Z5 + Z6, sdata = sdata.train, submodel = "TVC", continuous = NULL ) print(sol.SurvMA.TVC$weights) # - do prediction using provided R function SurvMA.Predict predict.SurvMA.TVC <- SurvMA.Predict( object = sol.SurvMA.TVC, covariates = sdata.test[,-c(1,2)], times = round(quantile(sdata.test$time,c(0.25,0.50,0.75)),2) ) head(predict.SurvMA.TVC$sprobs,2)#----------------------------------------------------------# # Basic preparations before running subsequent examples #### #----------------------------------------------------------# rm(list=ls(all=TRUE)) ## library necessary packages library(SurvMA) library(survival) #' #--------------------------------------------------------------# # Simulated dataset: from partial linear additive Cox model #### #--------------------------------------------------------------# ## Pre-process the dataset # - load the dataset data(SimData.APL) head(SimData.APL,2) # - split the data into training and test datasets set.seed(1) train.index <- sort(sample(1:200,0.75*200)) sdata.train <- SimData.APL[train.index,] sdata.test <- SimData.APL[-train.index,] ## Fit the dataset via our model averaging method # - fit the data using provided R function SurvMA.Fit set.seed(1) sol.SurvMA.PL <- SurvMA.Fit( formula = Surv(time,delta) ~ X + U1 + U2 + U3 + U4 + U5 + U6, sdata = SimData.APL, submodel = "PL", continuous = 2:7 ) print(sol.SurvMA.PL$weights) # - do prediction using provided R function SurvMA.Predict predict.SurvMA.PL <- SurvMA.Predict( object = sol.SurvMA.PL, covariates = sdata.test[,-c(1,2)], times = round(quantile(sdata.test$time,c(0.25,0.50,0.75)),2) ) head(predict.SurvMA.PL$sprobs,2) #-----------------------------------------------------------# # Real dataset: using time-varying coefficient Cox model #### # - the breast cancer data originally from survival package #-----------------------------------------------------------# ## Pre-process the dataset # - load the dataset data(RealData.ROT) summary(RealData.ROT$time) table(RealData.ROT$delta) # - plot the Kaplan-Meier curve plot( survfit(Surv(time,delta) ~ 1, data = RealData.ROT), mark.time = TRUE, conf.int = TRUE, lwd=2, xlim = c(0,3200), ylim=c(0.4,1), xlab="Time (in Days)", ylab="Estimated Survival Probability" ) # - test time-varying effects TVC.Test <- cox.zph(coxph(Surv(time, delta)~., data = RealData.ROT)) print(TVC.Test) par(mfrow=c(2,3)) plot( TVC.Test, resid = FALSE, lwd = 2, xlab = "Time (in Days)", ylab = paste("Coefficient for",colnames(RealData.ROT)[1:6]) ) # - split the data into training and test datasets set.seed(1) n <- nrow(RealData.ROT) train.index <- sort(sample(1:n,0.75*n)) sdata.train <- RealData.ROT[train.index,] sdata.test <- RealData.ROT[-train.index,] ## Fit the dataset via our model averaging method # - fit the data using provided R function SurvMA.Fit set.seed(1) sol.SurvMA.ROT <- SurvMA.Fit( formula = Surv(time, delta) ~ age + meno + pgr + er + hormon + chemo, sdata = sdata.train, submodel = "TVC", continuous = NULL ) print(sol.SurvMA.ROT$weights) # - do prediction using provided R function SurvMA.Predict predict.SurvMA.ROT <- SurvMA.Predict( object = sol.SurvMA.ROT, covariates = sdata.test[,!(colnames(sdata.test) %in% c("time","delta"))], times = round(quantile(sdata.test$time,c(0.25,0.50,0.75))) ) head(predict.SurvMA.ROT$sprobs,2) #----------------------------------------------------------------# # Simulated dataset: from time-varying coefficients Cox model #### #----------------------------------------------------------------# ## Pre-process the dataset # - load the dataset data(SimData.TVC) head(SimData.TVC,2) # - split the data into training and test datasets set.seed(1) train.index <- sort(sample(1:150,0.75*150)) sdata.train <- SimData.TVC[train.index,] sdata.test <- SimData.TVC[-train.index,] ## Fit the dataset via our model averaging method # - fit the data using provided R function SurvMA.Fit set.seed(1) sol.SurvMA.TVC <- SurvMA.Fit( formula = Surv(time,delta) ~ Z1 + Z2 + Z3 + Z4 + Z5 + Z6, sdata = sdata.train, submodel = "TVC", continuous = NULL ) print(sol.SurvMA.TVC$weights) # - do prediction using provided R function SurvMA.Predict predict.SurvMA.TVC <- SurvMA.Predict( object = sol.SurvMA.TVC, covariates = sdata.test[,-c(1,2)], times = round(quantile(sdata.test$time,c(0.25,0.50,0.75)),2) ) head(predict.SurvMA.TVC$sprobs,2)
Model averaging prediction of personalized survival probabilities (prediction)
SurvMA.Predict(object, covariates, times)SurvMA.Predict(object, covariates, times)
object |
a list of all outputted results from another main function named |
covariates |
is a data.frame with rows representing individuals and columns containing the necessary covariates used in the |
times |
specifies the time points at which survival probabilities will be calculated. |
This is a function used to conduct model averaging prediction (prediction) of personalized survival probabilities.
For preliminary model fitting process, see another function SurvMA.Fit().
A list of fitted results that contain, for example, predicted values of personalized survival probabilities.
# Examples of illustrating the usages of this function can be found in the help # page of our another function \code{SurvMA.Fit()} by typing \code{?SurvMA.Fit()}.# Examples of illustrating the usages of this function can be found in the help # page of our another function \code{SurvMA.Fit()} by typing \code{?SurvMA.Fit()}.