Title: Model Averaging Prediction of Personalized Survival Probabilities
Version: 1.6.8
Author: Mengyu Li ORCID iD [aut, cre], Jie Ding ORCID iD [aut], Xiaoguang Wang ORCID iD [aut]
Maintainer: Mengyu Li <mylilucky@163.com>
Description: Provide model averaging-based approaches that can be used to predict personalized survival probabilities. The key underlying idea is to approximate the conditional survival function using a weighted average of multiple candidate models. Two scenarios of candidate models are allowed: (Scenario 1) partial linear Cox model and (Scenario 2) time-varying coefficient Cox model. A reference of the underlying methods is Li and Wang (2023) <doi:10.1016/j.csda.2023.107759>.
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Encoding: UTF-8
Depends: R (≥ 3.5.0)
Imports: survival, maxLik, pec, quadprog, splines, methods
LazyData: TRUE
RoxygenNote: 7.2.3
URL: <https://github.com/Stat-WangXG/SurvMA>
NeedsCompilation: no
Packaged: 2024-09-22 08:14:28 UTC; biost
Repository: CRAN
Date/Publication: 2024-09-23 13:40:43 UTC

RealData.ROT: A simulated dataset based on a pre-specified time-varying coefficients Cox model.

Description

RealData.ROT

Usage

RealData.ROT

Format

An object of class data.frame with 444 rows and 8 columns.

Examples


# 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.

Description

SimData.APL: A simulated dataset based on a pre-specified partly linear additive Cox model.

Usage

SimData.APL

Format

An object of class data.frame with 200 rows and 9 columns.

Examples


# 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.

Description

SimData.TVC: A simulated dataset based on a pre-specified time-varying coefficients Cox model.

Usage

SimData.TVC

Format

An object of class data.frame with 150 rows and 8 columns.

Examples


# An example of illustrating this dataset can be found in the help page of our
#    function \code{SurvMA.Fit()} by typing \code{?SurvMA.Fit()}.

SurvMA: Model Averaging Prediction of Personalized Survival Probabilities using R Package SurvMA.

Description

Provide model averaging-based approaches that can be used to predict personalized survival probabilities. The key underlying idea is to approximate the conditional survival function using a weighted average of multiple candidate models. Two scenarios of candidate models are allowed: (Scenario 1) partial linear Cox model and (Scenario 2) time-varying coefficient Cox model. A reference of the underlying methods is Li and Wang (2023) doi:10.1016/j.csda.2023.107759.

Model averaging prediction of personalized survival probabilities (model fitting)

Description

Model averaging prediction of personalized survival probabilities (model fitting)

Usage

SurvMA.Fit(
  formula,
  sdata,
  submodel = c("PL", "TVC"),
  continuous = NULL,
  control = list(K.set = c(5:10), criterion = "AIC", method = "KM")
)

Arguments

formula

a formula expression, of the form response ~ predictors. The response is a Surv object (from R package "survival") with right censoring. It is used to specify the included covariates (risk factors). See the documentation for survreg and Surv in R package survival for details. The expression to the right of the "~" specifies the covariates.

sdata

a survival dataset (dataframe) in which to interpret the variables named in the formula and the cureform.

submodel

a character string defining the groups of candidate models, as introduced. It can be "PL" for partial linear Cox models or "TVC" for time varying coefficient Cox models.

continuous

a vector of integers representing the positions of continuous covariates within predictors specified in formula. If submodel="TVC" is set, this argument is redundant and the default value NULL is sufficient.

control

indicates more detailed control of the underlying model averaging fitting procedures. It is a list of the following three arguments: K.set specifies the range of the number of spline basis functions, with the default being K.set=c(5:10); criterion is a character string that specifies the information criterion for choosing the optimal number of B-spline basis functions and it can be either the default Akaike Information Criterion (criterion="AIC") or the Bayesian Information Criterion (criterion = "BIC"); method determines the approach to estimate the survival function of censoring time, which can be method="KM" to estimate it via the Kaplan-Meier estimator or method = "Cox" to estimate it via the Cox proportional hazards model.

Details

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.

Value

A list of fitted results that contain not only parameter estimates for all candidate models, but also optimal averaging weights (weights).

Examples


#----------------------------------------------------------#
# 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 = 2:4
)
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)
oldpar <- 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])
)
par(oldpar)

# - 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)

Description

Model averaging prediction of personalized survival probabilities (prediction)

Usage

SurvMA.Predict(object, covariates, times)

Arguments

object

a list of all outputted results from another main function named SurvMA.Fit().

covariates

is a data.frame with rows representing individuals and columns containing the necessary covariates used in the formula argument of SurvMA.Fit().

times

specifies the time points at which survival probabilities will be calculated.

Details

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().

Value

A list of fitted results that contain, for example, predicted values of personalized survival probabilities.

Examples


# 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()}.