cmpp is an R package designed to facilitate parametric
modeling and inference for cumulative incidence functions (CIFs) in
competing risks scenarios. The package implements methods discussed in
seminal works by Jeong and Fine (2006, 2007), with efficient computation
powered by Rcpp for high-performance applications.
Competing risks occur when multiple types of events prevent the observation of a particular event of interest. Traditional survival analysis often misrepresents such data, as it fails to account for competing risks. This package provides parametric methods for cumulative incidence functions (CIFs), offering a more direct and interpretable analysis than traditional cause-specific hazard models.
To install the package:
# Install from GitHub
devtools::install_github("stats9/cmpp")
# for windows
## after download cmpp zip file
install.packages('cmpp_0.0.1.zip', repos = NULL, type = "win-binary")
# for linux or mac
## after download cmpp tar.gz file
install.packages('cmpp_0.0.1.tar.gz', repos = NULL, type = "source")library(cmpp)
# Example data
features <- matrix(rnorm(300, 1, 2), nrow = 100, ncol = 3)
delta1 <- sample(c(0, 1), 100, replace = TRUE)
delta2 <- 1 - delta1
time <- rexp(100, rate = 1/10)
# Initialize the Cmpp model
Initialize(features, time, delta1, delta2, h = 1e-5) library(cmpp)
data("fertility_data")
Nam <- names(fertility_data)
fertility_data$Education
datt <- make_Dummy(fertility_data, features = c("Education"))
datt <- datt$New_Data
datt['Primary_Secondary'] <- datt$`Education:2`
datt['Higher_Education'] <- datt$`Education:3`
datt$`Education:2` <- datt$`Education:3` <- NULL
datt2 <- make_Dummy(datt, features = 'Event')$New_Data
d1 <- datt2$`Event:2`
d2 <- datt2$`Event:3`
feat <- datt2[c('age', 'Primary_Secondary', 'Higher_Education')] |>
data.matrix()
timee <- datt2[['time']]
Initialize(feat, timee, d1, d2, 1e-10)
# Estimate parameters using the Generalized odds rate (GOR)
initial_params <- rep(0.001, 2 * (ncol(features) + 3))
initial_params2 <- rep(0.001, 2 * (ncol(features) + 2))
result <- estimate_parameters_GOR(initial_params)
print(result)
# Estimate parameters using the Proportional Odds Model (POM)
result_pom <- estimate_parameters_POM(initial_params2)
print(result_pom)
# Estimate parameters using the Proportional Hazards Model (PHM)
result_phm <- estimate_parameters_PHM(initial_params2)
print(result_phm) library(cmpp)
data("fertility_data")
Nam <- names(fertility_data)
fertility_data$Education
datt <- make_Dummy(fertility_data, features = c("Education"))
datt <- datt$New_Data
datt['Primary_Secondary'] <- datt$`Education:2`
datt['Higher_Education'] <- datt$`Education:3`
datt$`Education:2` <- datt$`Education:3` <- NULL
datt2 <- make_Dummy(datt, features = 'Event')$New_Data
d1 <- datt2$`Event:2`
d2 <- datt2$`Event:3`
feat <- datt2[c('age', 'Primary_Secondary', 'Higher_Education')] |>
data.matrix()
timee <- datt2[['time']]
Initialize(feat, timee, d1, d2, 1e-10)
# Compute CIFs for competing risks
cif_results <- CIF_res1(rep(0.001, 4))
print(cif_results)
# Plot CIFs with confidence intervals
Res <- CIF_Figs(rep(0.001, 4), timee)
print(Res) library(cmpp)
data("fertility_data")
Nam <- names(fertility_data)
fertility_data$Education
datt <- make_Dummy(fertility_data, features = c("Education"))
datt <- datt$New_Data
datt['Primary_Secondary'] <- datt$`Education:2`
datt['Higher_Education'] <- datt$`Education:3`
datt$`Education:2` <- datt$`Education:3` <- NULL
datt2 <- make_Dummy(datt, features = 'Event')$New_Data
d1 <- datt2$`Event:2`
d2 <- datt2$`Event:3`
feat <- datt2[c('age', 'Primary_Secondary', 'Higher_Education')] |>
data.matrix()
timee <- datt2[['time']]
Initialize(feat, timee, d1, d2, 1e-10)
# Fit a Fine-Gray model
result_fg <- FineGray_Model(
CovarNames = c("Covar1", "Covar2", "Covar3"),
Failcode = 1,
RiskNames = c("Event1", "Event2")
)
print(result_fg$Results) # Summary of the Fine-Gray model
print(result_fg$Plot) # Plot of the CIFs library(cmpp)
data("fertility_data")
Nam <- names(fertility_data)
fertility_data$Education
datt <- make_Dummy(fertility_data, features = c("Education"))
datt <- datt$New_Data
datt['Primary_Secondary'] <- datt$`Education:2`
datt['Higher_Education'] <- datt$`Education:3`
datt$`Education:2` <- datt$`Education:3` <- NULL
datt2 <- make_Dummy(datt, features = 'Event')$New_Data
d1 <- datt2$`Event:2`
d2 <- datt2$`Event:3`
feat <- datt2[c('age', 'Primary_Secondary', 'Higher_Education')] |>
data.matrix()
timee <- datt2[['time']]
Initialize(feat, timee, d1, d2, 1e-10)
result_cif <- Cmpp_CIF(
featureID = c(1, 2),
featureValue = c(0.5, 1.2),
RiskNames = c("Event1", "Event2"),
TypeMethod = "GOR",
predTime = seq(0, 10, by = 0.5)
)
print(result_cif$Plot$Plot_InputModel) # Plot for the specified model
print(result_cif$CIF$CIFAdjusted) # Adjusted CIF values library(cmpp)
data("fertility_data")
Nam <- names(fertility_data)
fertility_data$Education
datt <- make_Dummy(fertility_data, features = c("Education"))
datt <- datt$New_Data
datt['Primary_Secondary'] <- datt$`Education:2`
datt['Higher_Education'] <- datt$`Education:3`
datt$`Education:2` <- datt$`Education:3` <- NULL
datt2 <- make_Dummy(datt, features = 'Event')$New_Data
d1 <- datt2$`Event:2`
d2 <- datt2$`Event:3`
feat <- datt2[c('age', 'Primary_Secondary', 'Higher_Education')] |>
data.matrix()
timee <- datt2[['time']]
Initialize(feat, timee, d1, d2, 1e-10)
datt <- GetData()
# Estimate variance of parameters using bootstrap
results <- bootstrap_variance(datt$features, datt$timee,
datt$delta1, datt$delta2, rep(0.001, 4), n_bootstrap = 500)
print(results$variances)
print(results$bootstrap_estimates)library(cmpp)
# Load example data
features <- matrix(rnorm(300, 1, 2), nrow = 100, ncol = 3)
delta1 <- sample(c(0, 1), 100, replace = TRUE)
delta2 <- 1 - delta1
time <- rexp(100, rate = 1/10)
# Initialize the Cmpp model
Initialize(features, time, delta1, delta2, h = 1e-5)
# Estimate parameters
initial_params <- rep(0.001, 2 * (ncol(features) + 3))
params <- estimate_parameters_GOR(initial_params)
print(params)
# Compute CIFs
cif_results <- CIF_res1(rep(0.001, 4))
print(cif_results)
# Plot CIFs
Res <- CIF_Figs(rep(0.01, 4), time)
print(Res) library(cmpp)
data("fertility_data")
Nam <- names(fertility_data)
fertility_data$Education
datt <- make_Dummy(fertility_data, features = c("Education"))
datt <- datt$New_Data
datt['Primary_Secondary'] <- datt$`Education:2`
datt['Higher_Education'] <- datt$`Education:3`
datt$`Education:2` <- datt$`Education:3` <- NULL
datt2 <- make_Dummy(datt, features = 'Event')$New_Data
d1 <- datt2$`Event:2`
d2 <- datt2$`Event:3`
feat <- datt2[c('age', 'Primary_Secondary', 'Higher_Education')] |>
data.matrix()
timee <- datt2[['time']]
Initialize(feat, timee, d1, d2, 1e-10)
data <- GetData()
print(data$features) # Feature matrix
print(data$timee) # Failure times
print(data$delta1) # Indicator for the first competing event
print(data$delta2) # Indicator for the second competing event
result <- Cmpp_CIF(
featureID = c(1, 2),
featureValue = c(0.5, 1.2),
RiskNames = c("Event1", "Event2"),
TypeMethod = "GOR",
predTime = seq(0, 10, by = 0.5)
)
print(result$Plot$Plot_InputModel) # Plot for the specified model
print(result$Plot$PlotAdjusted_AllModels) # Adjusted CIFs for all models
print(result$CIF$CIFAdjusted) # Adjusted CIF values