Nothing
R/Data.R
In survkl: Estimate Survival Data with Data Integration
#' Example high-dimensional survival data
#'
#' A simulated survival dataset in a high-dimensional linear setting
#' with 50 covariates (6 signals + 44 AR(1) noise), Weibull baseline
#' hazard, and controlled censoring. Includes internal train/test sets,
#' and an external-data–estimated coefficient vector.
#'
#' @name ExampleData_highdim
#' @docType data
#' @usage data(ExampleData_highdim)
#'
#' @format A list containing the following elements:
#' \describe{
#' \item{train}{A list with components:
#' \describe{
#' \item{z}{Data frame of size \eqn{n_\mathrm{train}\times 50} with covariates \code{Z1}–\code{Z50}.}
#' \item{status}{Vector of event indicators (\code{1}=event, \code{0}=censored).}
#' \item{time}{Numeric vector of observed times \eqn{\min(T, C)}.}
#' \item{stratum}{Vector of stratum labels (here all \code{1}).}
#' }
#' }
#' \item{test}{A list with the same structure as \code{train}, with size \eqn{n_\mathrm{test}\times 50} for \code{z}.}
#' \item{beta_external}{Numeric vector (length 50, named \code{Z1}–\code{Z50}) of Cox coefficients
#' estimated on an external dataset using only \code{Z1}–\code{Z6} and expanded to length 50
#' (zeros for \code{Z7}–\code{Z50}).}
#' }
#'
#' @details Data-generating mechanism:
#' \itemize{
#' \item Covariates: 50 variables with signals \code{Z1}–\code{Z6} and noise \code{Z7}–\code{Z50}.
#' \itemize{
#' \item \code{Z1}, \code{Z2} ~ bivariate normal with AR(1) correlation \eqn{\rho=0.5}.
#' \item \code{Z3}, \code{Z4} ~ independent Bernoulli(0.5).
#' \item \code{Z5} ~ \eqn{N(2,1)}, \code{Z6} ~ \eqn{N(-2,1)} (group indicator fixed at 1).
#' \item \code{Z7}–\code{Z50} ~ multivariate normal with AR(1) correlation \eqn{\rho=0.5}.
#' }
#' \item True coefficients: \eqn{\beta = (0.3,-0.3,0.3,-0.3,0.3,-0.3,0,\ldots,0)} (length 50).
#' \item Event times: Weibull baseline hazard
#' \eqn{h_0(t)=\lambda\nu\, t^{\nu-1}} with \eqn{\lambda=1}, \eqn{\nu=2}.
#' Given linear predictor \eqn{\eta = Z^\top \beta}, draw \eqn{U\sim\mathrm{Unif}(0,1)} and set
#' \deqn{T = \left(\frac{-\log U}{\lambda\, e^{\eta}}\right)^{1/\nu}.}
#' \item Censoring: \eqn{C\sim \mathrm{Unif}(0,\text{ub})} with \code{ub} tuned iteratively to
#' achieve the target censoring rate (internal: \code{0.70}; external: \code{0.50}).
#' Observed time is \eqn{\min(T,C)}, status is \eqn{\mathbf{1}\{T \le C\}}.
#' \item External coefficients: Fit a Cox model
#' \code{Surv(time, status) ~ Z1 + ... + Z6} on the external data (Breslow ties),
#' then place the estimated coefficients into a length-50 vector (zeros elsewhere).
#' }
#'
#' @examples
#' data(ExampleData_highdim)
#'
#' head(ExampleData_highdim$train$z)
#' table(ExampleData_highdim$train$status)
#' summary(ExampleData_highdim$train$time)
#'
#' head(ExampleData_highdim$test$z)
#' table(ExampleData_highdim$test$status)
#' summary(ExampleData_highdim$test$time)
#'
"ExampleData_highdim"
#' Example low-dimensional survival data
#'
#' A simulated survival dataset in a low-dimensional linear setting
#' with 6 covariates (2 correlated continuous, 2 binary, 2 mean-shifted normals),
#' Weibull baseline hazard, and controlled censoring. Includes internal train/test sets,
#' and three external-quality coefficient vectors.
#'
#' @name ExampleData_lowdim
#' @docType data
#' @usage data(ExampleData_lowdim)
#'
#' @format A list containing the following elements:
#' \describe{
#' \item{train}{A list with components:
#' \describe{
#' \item{z}{Data frame of size \eqn{n_\mathrm{train}\times 6} with covariates \code{Z1}–\code{Z6}.}
#' \item{status}{Vector of event indicators (\code{1}=event, \code{0}=censored).}
#' \item{time}{Numeric vector of observed times \eqn{\min(T, C)}.}
#' \item{stratum}{Vector of stratum labels (here all \code{1}).}
#' }
#' }
#' \item{test}{A list with the same structure as \code{train}, with size \eqn{n_\mathrm{test}\times 6} for \code{z}.}
#' \item{beta_external_good}{Numeric vector (length 6; named \code{Z1}–\code{Z6}) of Cox coefficients estimated on a
#' "Good" external dataset using all \code{Z1}–\code{Z6}.}
#' \item{beta_external_fair}{Numeric vector (length 6; names \code{Z1}–\code{Z6}) of Cox coefficients estimated on a
#' "Fair" external dataset using a reduced subset \code{Z1}, \code{Z3}, \code{Z5}, \code{Z6};
#' coefficients for variables not used are \code{0}.}
#' \item{beta_external_poor}{Numeric vector (length 6; names \code{Z1}–\code{Z6}) of Cox coefficients estimated on a
#' "Poor" external dataset using \code{Z1} and \code{Z5} only; remaining entries are \code{0}.}
#' }
#'
#' @details Data-generating mechanism:
#' \itemize{
#' \item Covariates: 6 variables \code{Z1}–\code{Z6}.
#' \itemize{
#' \item \code{Z1}, \code{Z2} ~ bivariate normal with AR(1) correlation \eqn{\rho=0.5}.
#' \item \code{Z3}, \code{Z4} ~ independent Bernoulli(0.5).
#' \item \code{Z5} ~ \eqn{N(2,1)}, \code{Z6} ~ \eqn{N(-2,1)} (group indicator fixed at 1 for internal train/test).
#' }
#' \item True coefficients: \eqn{\beta = (0.3,-0.3,0.3,-0.3,0.3,-0.3)} (length 6).
#' \item Event times: Weibull baseline hazard
#' \eqn{h_0(t)=\lambda\nu \, t^{\nu-1}} with \eqn{\lambda=1}, \eqn{\nu=2}.
#' Given linear predictor \eqn{\eta = Z^\top \beta}, draw \eqn{U\sim\mathrm{Unif}(0,1)} and set
#' \deqn{T = \left(\frac{-\log U}{\lambda \, e^{\eta}}\right)^{1/\nu}.}
#' \item Censoring: \eqn{C\sim \mathrm{Unif}(0,\text{ub})} with \code{ub} tuned iteratively to
#' achieve the target censoring rate (internal: \code{0.70}; external: \code{0.50}).
#' Observed time is \eqn{\min(T,C)}, status is \eqn{\mathbf{1}\{T \le C\}}.
#' \item External coefficients: For each quality level ("Good", "Fair", "Poor"), fit a Cox model
#' \code{Surv(time, status) ~ Z1 + ...} on the corresponding external data (Breslow ties)
#' using the specified covariate subset; place estimates into a length-6 vector named \code{Z1}–\code{Z6}
#' with zeros for variables not included.
#' }
#'
#' @examples
#' data(ExampleData_lowdim)
#'
#' head(ExampleData_lowdim$train$z)
#' table(ExampleData_lowdim$train$status)
#' summary(ExampleData_lowdim$train$time)
#'
#' head(ExampleData_lowdim$test$z)
#' table(ExampleData_lowdim$test$status)
#' summary(ExampleData_lowdim$test$time)
#'
"ExampleData_lowdim"
#' Study to Understand Prognoses Preferences Outcomes and Risks of Treatment
#' @name support
#' @docType data
#' @usage data(support)
#'
#' @description The \code{support} dataset tracks five response variables: hospital
#' death, severe functional disability, hospital costs, and time until death
#' and death itself. The patients are followed for up to 5.56 years. See Bhatnagar et al. (2020) for details.
#'
#' @details Some of the original data was missing. Before imputation, there were
#' a total of 9,104 individuals and 47 variables. Following Bhatnagar et al. (2020), a few variables
#' were removed. Three response variables were removed:
#' hospital charges, patient ratio of costs to charges and patient
#' micro-costs. Hospital death was also removed as it was directly informative
#' of the event of interest, namely death. Additionally, functional disability and
#' income were removed as they are ordinal covariates. Finally, 8
#' covariates were removed related to the results of previous findings: SUPPORT
#' day 3 physiology score (\code{sps}), APACHE III day 3 physiology score
#' (\code{aps}), SUPPORT model 2-month survival estimate, SUPPORT model
#' 6-month survival estimate, Physician's 2-month survival estimate for pt.,
#' Physician's 6-month survival estimate for pt., Patient had Do Not
#' Resuscitate (DNR) order, and Day of DNR order (<0 if before study). Of
#' these, \code{sps} and \code{aps} were added on after imputation, as they
#' were missing only 1 observation. First the imputation is done manually using the normal
#' values for physiological measures recommended by Knaus et al. (1995). Next,
#' a single dataset was imputed using \pkg{mice} with default settings. After
#' imputation, the covariate for surrogate activities of daily
#' living was not imputed. This is due to collinearity between the other two
#' covariates for activities of daily living. Therefore, surrogate activities
#' of daily living were removed. See details in the R package (casebase) by Bhatnagar et al. (2020).
#'
#' @format A data frame with 9,104 observations and 34 variables after imputation
#' and the removal of response variables like hospital charges, patient ratio
#' of costs to charges and micro-costs following Bhatnagar et al. (2020).
#' Ordinal variables, namely functional disability and income, were also removed.
#' Finally, Surrogate activities of daily living were removed due to sparsity.
#' There were 6 other model scores in the data-set and they were removed; only aps and sps were kept.
#' \describe{
#' \item{age}{ stores a double representing age. }
#' \item{death}{
#' death at any time up to NDI (National Death Index) date: 12/31/1994. }
#' \item{sex}{ 0=female, 1=male. }
#' \item{slos}{ days from study entry to discharge. }
#' \item{d.time}{ days of
#' follow-up. }
#' \item{dzgroup}{ each level of dzgroup: ARF/MOSF w/Sepsis,
#' COPD, CHF, Cirrhosis, Coma, Colon Cancer, Lung Cancer, MOSF with
#' malignancy. }
#' \item{dzclass}{ ARF/MOSF, COPD/CHF/Cirrhosis, Coma and cancer disease classes. }
#' \item{num.co}{ the number of comorbidities. }
#' \item{edu}{ years of education of patients. }
#' \item{scoma}{ the SUPPORT coma score based on Glasgow D3. }
#' \item{avtisst}{ average TISS, days 3-25. }
#' \item{race}{ indicates race: White, Black, Asian, Hispanic or other. }
#' \item{hday}{ day in Hospital at Study Admit.}
#' \item{diabetes}{diabetes (Com27-28, Dx 73).}
#' \item{dementia}{dementia (Comorbidity 6).}
#' \item{ca}{cancer state. }
#' \item{meanbp}{ mean arterial blood pressure day 3. }
#' \item{wblc}{ white blood cell count on day 3. }
#' \item{hrt}{ heart rate day 3. }
#' \item{resp}{ respiration rate day 3. }
#' \item{temp}{ temperature, in Celsius, on day 3. }
#' \item{pafi}{ PaO2/(0.01*FiO2) day 3. }
#' \item{alb}{serum albumin day 3. }
#' \item{bili}{ bilirubin day 3. }
#' \item{crea}{ serum creatinine day 3. }
#' \item{sod}{ serum sodium day 3. }
#' \item{ph}{ serum pH (in arteries) day 3. }
#' \item{glucose}{ serum glucose day 3. }
#' \item{bun}{ bun day 3. }
#' \item{urine}{ urine output day 3. }
#' \item{adlp}{ adl patient day 3. }
#' \item{adlsc}{ imputed adl calibrated to surrogate, if a surrogate was used for a follow up.}
#' \item{sps}{SUPPORT physiology score.}
#' \item{aps}{apache III physiology score.} }
#'
#' @source Available at the following website:
#' \url{https://archive.ics.uci.edu/dataset/880/support2}.
#'
#' @references
#'
#' Bhatnagar, S., Turgeon, M., Islam, J., Hanley, J. A., and Saarela, O. (2020) casebase: Fitting Flexible Smooth-in-Time
#' Hazards and Risk Functions via Logistic and Multinomial Regression.
#' \emph{R package version 0.9.0},
#' <https://CRAN.R-project.org/package=casebase>.
#'
#' Knaus, W. A., Harrell, F. E., Lynn, J., Goldman, L., Phillips, R. S., Connors, A. F., et al. (1995)
#' The SUPPORT prognostic model: Objective estimates of survival for seriously ill hospitalized adults.
#' \emph{Annals of Internal Medicine}, \strong{122(3)}: 191-203.
#' \cr
#'
#'
#' @examples
#' if (requireNamespace("survival", quietly = TRUE)) {
#' data(support)
#' set.seed(123)
#'
#' support <- support[support$ca %in% "metastatic", ]
#' time <- support$d.time
#' death <- support$death
#' diabetes <- model.matrix(~ factor(support$diabetes))[, -1]
#' # sex: female as the reference group
#' sex <- model.matrix(~ support$sex)[, -1]
#' # age: continuous variable
#' age <- support$age
#' age[support$age <= 50] <- "<50"
#' age[support$age > 50 & support$age <= 60] <- "50-59"
#' age[support$age > 60 & support$age < 70] <- "60-69"
#' age[support$age >= 70] <- "70+"
#' age <- factor(age, levels = c("60-69", "<50", "50-59", "70+"))
#' z_age <- model.matrix(~ age)[, -1]
#' z <- data.frame(z_age, sex, diabetes)
#' colnames(z) <- c("age_50", "age_50_59", "age_70", "diabetes", "male")
#' dat <- data.frame(time, death, z)
#'
#' n <- nrow(dat)
#' n_ext <- floor(0.87 * n)
#' n_int <- floor(0.03 * n)
#' n_test <- n - n_ext - n_int
#' idx <- sample(seq_len(n))
#' idx_ext <- idx[1:n_ext]
#' idx_int <- idx[(n_ext + 1):(n_ext + n_int)]
#' idx_test <- idx[(n_ext + n_int + 1):n]
#'
#' external_data <- dat[idx_ext, ]
#' internal_data <- dat[idx_int, ]
#' test_data <- dat[idx_test, ]
#'
#' ext_cox <- survival::coxph(
#' survival::Surv(time, death) ~ age_50 + age_50_59 + age_70 + diabetes + male,
#' data = external_data
#' )
#' beta_external <- coef(ext_cox)
#'
#' result1 <- cv.coxkl(
#' z = internal_data[, c("age_50", "age_50_59", "age_70", "diabetes", "male")],
#' delta = internal_data$death,
#' time = internal_data$time,
#' beta = beta_external,
#' stratum = NULL,
#' etas = generate_eta(method = "exponential", n = 50, max_eta = 50)
#' )
#' cv.plot(result1)
#' }
#'
"support"
Try the survkl package in your browser
Any scripts or data that you put into this service are public.
survkl documentation built on April 22, 2026, 1:08 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#' Example high-dimensional survival data
#'
#' A simulated survival dataset in a high-dimensional linear setting
#' with 50 covariates (6 signals + 44 AR(1) noise), Weibull baseline
#' hazard, and controlled censoring. Includes internal train/test sets,
#' and an external-data–estimated coefficient vector.
#'
#' @name ExampleData_highdim
#' @docType data
#' @usage data(ExampleData_highdim)
#'
#' @format A list containing the following elements:
#' \describe{
#' \item{train}{A list with components:
#' \describe{
#' \item{z}{Data frame of size \eqn{n_\mathrm{train}\times 50} with covariates \code{Z1}–\code{Z50}.}
#' \item{status}{Vector of event indicators (\code{1}=event, \code{0}=censored).}
#' \item{time}{Numeric vector of observed times \eqn{\min(T, C)}.}
#' \item{stratum}{Vector of stratum labels (here all \code{1}).}
#' }
#' }
#' \item{test}{A list with the same structure as \code{train}, with size \eqn{n_\mathrm{test}\times 50} for \code{z}.}
#' \item{beta_external}{Numeric vector (length 50, named \code{Z1}–\code{Z50}) of Cox coefficients
#' estimated on an external dataset using only \code{Z1}–\code{Z6} and expanded to length 50
#' (zeros for \code{Z7}–\code{Z50}).}
#' }
#'
#' @details Data-generating mechanism:
#' \itemize{
#' \item Covariates: 50 variables with signals \code{Z1}–\code{Z6} and noise \code{Z7}–\code{Z50}.
#' \itemize{
#' \item \code{Z1}, \code{Z2} ~ bivariate normal with AR(1) correlation \eqn{\rho=0.5}.
#' \item \code{Z3}, \code{Z4} ~ independent Bernoulli(0.5).
#' \item \code{Z5} ~ \eqn{N(2,1)}, \code{Z6} ~ \eqn{N(-2,1)} (group indicator fixed at 1).
#' \item \code{Z7}–\code{Z50} ~ multivariate normal with AR(1) correlation \eqn{\rho=0.5}.
#' }
#' \item True coefficients: \eqn{\beta = (0.3,-0.3,0.3,-0.3,0.3,-0.3,0,\ldots,0)} (length 50).
#' \item Event times: Weibull baseline hazard
#' \eqn{h_0(t)=\lambda\nu\, t^{\nu-1}} with \eqn{\lambda=1}, \eqn{\nu=2}.
#' Given linear predictor \eqn{\eta = Z^\top \beta}, draw \eqn{U\sim\mathrm{Unif}(0,1)} and set
#' \deqn{T = \left(\frac{-\log U}{\lambda\, e^{\eta}}\right)^{1/\nu}.}
#' \item Censoring: \eqn{C\sim \mathrm{Unif}(0,\text{ub})} with \code{ub} tuned iteratively to
#' achieve the target censoring rate (internal: \code{0.70}; external: \code{0.50}).
#' Observed time is \eqn{\min(T,C)}, status is \eqn{\mathbf{1}\{T \le C\}}.
#' \item External coefficients: Fit a Cox model
#' \code{Surv(time, status) ~ Z1 + ... + Z6} on the external data (Breslow ties),
#' then place the estimated coefficients into a length-50 vector (zeros elsewhere).
#' }
#'
#' @examples
#' data(ExampleData_highdim)
#'
#' head(ExampleData_highdim$train$z)
#' table(ExampleData_highdim$train$status)
#' summary(ExampleData_highdim$train$time)
#'
#' head(ExampleData_highdim$test$z)
#' table(ExampleData_highdim$test$status)
#' summary(ExampleData_highdim$test$time)
#'
"ExampleData_highdim"
#' Example low-dimensional survival data
#'
#' A simulated survival dataset in a low-dimensional linear setting
#' with 6 covariates (2 correlated continuous, 2 binary, 2 mean-shifted normals),
#' Weibull baseline hazard, and controlled censoring. Includes internal train/test sets,
#' and three external-quality coefficient vectors.
#'
#' @name ExampleData_lowdim
#' @docType data
#' @usage data(ExampleData_lowdim)
#'
#' @format A list containing the following elements:
#' \describe{
#' \item{train}{A list with components:
#' \describe{
#' \item{z}{Data frame of size \eqn{n_\mathrm{train}\times 6} with covariates \code{Z1}–\code{Z6}.}
#' \item{status}{Vector of event indicators (\code{1}=event, \code{0}=censored).}
#' \item{time}{Numeric vector of observed times \eqn{\min(T, C)}.}
#' \item{stratum}{Vector of stratum labels (here all \code{1}).}
#' }
#' }
#' \item{test}{A list with the same structure as \code{train}, with size \eqn{n_\mathrm{test}\times 6} for \code{z}.}
#' \item{beta_external_good}{Numeric vector (length 6; named \code{Z1}–\code{Z6}) of Cox coefficients estimated on a
#' "Good" external dataset using all \code{Z1}–\code{Z6}.}
#' \item{beta_external_fair}{Numeric vector (length 6; names \code{Z1}–\code{Z6}) of Cox coefficients estimated on a
#' "Fair" external dataset using a reduced subset \code{Z1}, \code{Z3}, \code{Z5}, \code{Z6};
#' coefficients for variables not used are \code{0}.}
#' \item{beta_external_poor}{Numeric vector (length 6; names \code{Z1}–\code{Z6}) of Cox coefficients estimated on a
#' "Poor" external dataset using \code{Z1} and \code{Z5} only; remaining entries are \code{0}.}
#' }
#'
#' @details Data-generating mechanism:
#' \itemize{
#' \item Covariates: 6 variables \code{Z1}–\code{Z6}.
#' \itemize{
#' \item \code{Z1}, \code{Z2} ~ bivariate normal with AR(1) correlation \eqn{\rho=0.5}.
#' \item \code{Z3}, \code{Z4} ~ independent Bernoulli(0.5).
#' \item \code{Z5} ~ \eqn{N(2,1)}, \code{Z6} ~ \eqn{N(-2,1)} (group indicator fixed at 1 for internal train/test).
#' }
#' \item True coefficients: \eqn{\beta = (0.3,-0.3,0.3,-0.3,0.3,-0.3)} (length 6).
#' \item Event times: Weibull baseline hazard
#' \eqn{h_0(t)=\lambda\nu \, t^{\nu-1}} with \eqn{\lambda=1}, \eqn{\nu=2}.
#' Given linear predictor \eqn{\eta = Z^\top \beta}, draw \eqn{U\sim\mathrm{Unif}(0,1)} and set
#' \deqn{T = \left(\frac{-\log U}{\lambda \, e^{\eta}}\right)^{1/\nu}.}
#' \item Censoring: \eqn{C\sim \mathrm{Unif}(0,\text{ub})} with \code{ub} tuned iteratively to
#' achieve the target censoring rate (internal: \code{0.70}; external: \code{0.50}).
#' Observed time is \eqn{\min(T,C)}, status is \eqn{\mathbf{1}\{T \le C\}}.
#' \item External coefficients: For each quality level ("Good", "Fair", "Poor"), fit a Cox model
#' \code{Surv(time, status) ~ Z1 + ...} on the corresponding external data (Breslow ties)
#' using the specified covariate subset; place estimates into a length-6 vector named \code{Z1}–\code{Z6}
#' with zeros for variables not included.
#' }
#'
#' @examples
#' data(ExampleData_lowdim)
#'
#' head(ExampleData_lowdim$train$z)
#' table(ExampleData_lowdim$train$status)
#' summary(ExampleData_lowdim$train$time)
#'
#' head(ExampleData_lowdim$test$z)
#' table(ExampleData_lowdim$test$status)
#' summary(ExampleData_lowdim$test$time)
#'
"ExampleData_lowdim"
#' Study to Understand Prognoses Preferences Outcomes and Risks of Treatment
#' @name support
#' @docType data
#' @usage data(support)
#'
#' @description The \code{support} dataset tracks five response variables: hospital
#' death, severe functional disability, hospital costs, and time until death
#' and death itself. The patients are followed for up to 5.56 years. See Bhatnagar et al. (2020) for details.
#'
#' @details Some of the original data was missing. Before imputation, there were
#' a total of 9,104 individuals and 47 variables. Following Bhatnagar et al. (2020), a few variables
#' were removed. Three response variables were removed:
#' hospital charges, patient ratio of costs to charges and patient
#' micro-costs. Hospital death was also removed as it was directly informative
#' of the event of interest, namely death. Additionally, functional disability and
#' income were removed as they are ordinal covariates. Finally, 8
#' covariates were removed related to the results of previous findings: SUPPORT
#' day 3 physiology score (\code{sps}), APACHE III day 3 physiology score
#' (\code{aps}), SUPPORT model 2-month survival estimate, SUPPORT model
#' 6-month survival estimate, Physician's 2-month survival estimate for pt.,
#' Physician's 6-month survival estimate for pt., Patient had Do Not
#' Resuscitate (DNR) order, and Day of DNR order (<0 if before study). Of
#' these, \code{sps} and \code{aps} were added on after imputation, as they
#' were missing only 1 observation. First the imputation is done manually using the normal
#' values for physiological measures recommended by Knaus et al. (1995). Next,
#' a single dataset was imputed using \pkg{mice} with default settings. After
#' imputation, the covariate for surrogate activities of daily
#' living was not imputed. This is due to collinearity between the other two
#' covariates for activities of daily living. Therefore, surrogate activities
#' of daily living were removed. See details in the R package (casebase) by Bhatnagar et al. (2020).
#'
#' @format A data frame with 9,104 observations and 34 variables after imputation
#' and the removal of response variables like hospital charges, patient ratio
#' of costs to charges and micro-costs following Bhatnagar et al. (2020).
#' Ordinal variables, namely functional disability and income, were also removed.
#' Finally, Surrogate activities of daily living were removed due to sparsity.
#' There were 6 other model scores in the data-set and they were removed; only aps and sps were kept.
#' \describe{
#' \item{age}{ stores a double representing age. }
#' \item{death}{
#' death at any time up to NDI (National Death Index) date: 12/31/1994. }
#' \item{sex}{ 0=female, 1=male. }
#' \item{slos}{ days from study entry to discharge. }
#' \item{d.time}{ days of
#' follow-up. }
#' \item{dzgroup}{ each level of dzgroup: ARF/MOSF w/Sepsis,
#' COPD, CHF, Cirrhosis, Coma, Colon Cancer, Lung Cancer, MOSF with
#' malignancy. }
#' \item{dzclass}{ ARF/MOSF, COPD/CHF/Cirrhosis, Coma and cancer disease classes. }
#' \item{num.co}{ the number of comorbidities. }
#' \item{edu}{ years of education of patients. }
#' \item{scoma}{ the SUPPORT coma score based on Glasgow D3. }
#' \item{avtisst}{ average TISS, days 3-25. }
#' \item{race}{ indicates race: White, Black, Asian, Hispanic or other. }
#' \item{hday}{ day in Hospital at Study Admit.}
#' \item{diabetes}{diabetes (Com27-28, Dx 73).}
#' \item{dementia}{dementia (Comorbidity 6).}
#' \item{ca}{cancer state. }
#' \item{meanbp}{ mean arterial blood pressure day 3. }
#' \item{wblc}{ white blood cell count on day 3. }
#' \item{hrt}{ heart rate day 3. }
#' \item{resp}{ respiration rate day 3. }
#' \item{temp}{ temperature, in Celsius, on day 3. }
#' \item{pafi}{ PaO2/(0.01*FiO2) day 3. }
#' \item{alb}{serum albumin day 3. }
#' \item{bili}{ bilirubin day 3. }
#' \item{crea}{ serum creatinine day 3. }
#' \item{sod}{ serum sodium day 3. }
#' \item{ph}{ serum pH (in arteries) day 3. }
#' \item{glucose}{ serum glucose day 3. }
#' \item{bun}{ bun day 3. }
#' \item{urine}{ urine output day 3. }
#' \item{adlp}{ adl patient day 3. }
#' \item{adlsc}{ imputed adl calibrated to surrogate, if a surrogate was used for a follow up.}
#' \item{sps}{SUPPORT physiology score.}
#' \item{aps}{apache III physiology score.} }
#'
#' @source Available at the following website:
#' \url{https://archive.ics.uci.edu/dataset/880/support2}.
#'
#' @references
#'
#' Bhatnagar, S., Turgeon, M., Islam, J., Hanley, J. A., and Saarela, O. (2020) casebase: Fitting Flexible Smooth-in-Time
#' Hazards and Risk Functions via Logistic and Multinomial Regression.
#' \emph{R package version 0.9.0},
#' <https://CRAN.R-project.org/package=casebase>.
#'
#' Knaus, W. A., Harrell, F. E., Lynn, J., Goldman, L., Phillips, R. S., Connors, A. F., et al. (1995)
#' The SUPPORT prognostic model: Objective estimates of survival for seriously ill hospitalized adults.
#' \emph{Annals of Internal Medicine}, \strong{122(3)}: 191-203.
#' \cr
#'
#'
#' @examples
#' if (requireNamespace("survival", quietly = TRUE)) {
#' data(support)
#' set.seed(123)
#'
#' support <- support[support$ca %in% "metastatic", ]
#' time <- support$d.time
#' death <- support$death
#' diabetes <- model.matrix(~ factor(support$diabetes))[, -1]
#' # sex: female as the reference group
#' sex <- model.matrix(~ support$sex)[, -1]
#' # age: continuous variable
#' age <- support$age
#' age[support$age <= 50] <- "<50"
#' age[support$age > 50 & support$age <= 60] <- "50-59"
#' age[support$age > 60 & support$age < 70] <- "60-69"
#' age[support$age >= 70] <- "70+"
#' age <- factor(age, levels = c("60-69", "<50", "50-59", "70+"))
#' z_age <- model.matrix(~ age)[, -1]
#' z <- data.frame(z_age, sex, diabetes)
#' colnames(z) <- c("age_50", "age_50_59", "age_70", "diabetes", "male")
#' dat <- data.frame(time, death, z)
#'
#' n <- nrow(dat)
#' n_ext <- floor(0.87 * n)
#' n_int <- floor(0.03 * n)
#' n_test <- n - n_ext - n_int
#' idx <- sample(seq_len(n))
#' idx_ext <- idx[1:n_ext]
#' idx_int <- idx[(n_ext + 1):(n_ext + n_int)]
#' idx_test <- idx[(n_ext + n_int + 1):n]
#'
#' external_data <- dat[idx_ext, ]
#' internal_data <- dat[idx_int, ]
#' test_data <- dat[idx_test, ]
#'
#' ext_cox <- survival::coxph(
#' survival::Surv(time, death) ~ age_50 + age_50_59 + age_70 + diabetes + male,
#' data = external_data
#' )
#' beta_external <- coef(ext_cox)
#'
#' result1 <- cv.coxkl(
#' z = internal_data[, c("age_50", "age_50_59", "age_70", "diabetes", "male")],
#' delta = internal_data$death,
#' time = internal_data$time,
#' beta = beta_external,
#' stratum = NULL,
#' etas = generate_eta(method = "exponential", n = 50, max_eta = 50)
#' )
#' cv.plot(result1)
#' }
#'
"support"
Try the survkl package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.