cmpp (version 0.0.1)

Cmpp_CIF: Compute and Plot Cumulative Incidence Functions (CIF) for Competing Risks

Description

This function computes and plots the cumulative incidence functions (CIF) for competing risks using three parametric models: Generalized odds rate (GOR), Proportional Odds Model (POM), and Proportional Hazards Model (PHM). It allows for adjusted CIFs based on specific covariate values and provides visualizations for all models.

Usage

Cmpp_CIF(
  featureID = NULL,
  featureValue = NULL,
  RiskNames = NULL,
  TypeMethod = "GOR",
  predTime = NULL
)

Value

A list containing:

Time

A list with the input time points, time points for adjusted plots, and time points for null plots.

CIF

A list with the following elements:

• CIFNULL: A data frame containing the CIFs for the null model (not adjusted by covariates).

• CIFAdjusted: A data frame containing the CIFs adjusted by covariates.

Plot

A list with the following elements:

• PlotNull_AllModels: A ggplot object showing the CIFs for all models (not adjusted by covariates).

• PlotAdjusted_AllModels: A ggplot object showing the adjusted CIFs for all models.

• Plot_InputModel: A ggplot object showing the CIFs for the specified model (TypeMethod).

Arguments

featureID

A numeric vector of indices specifying the features to adjust. Default is NULL.

featureValue

A numeric vector of values corresponding to the features specified in featureID. Default is NULL.

RiskNames

A character vector specifying the names of the competing risks. Default is NULL, which assigns names as "Risk1" and "Risk2".

TypeMethod

A character string specifying the model to use for plotting. Must be one of "GOR", "POM", or "PHM". Default is "GOR".

predTime

A numeric vector of time points for which CIFs are computed. Default is NULL, which uses the failure times from the initialized data.

Details

This function performs the following steps:

• Estimates the model parameters for GOR, POM, and PHM using the estimate_parameters_GOR, estimate_parameters_POM, and estimate_parameters_PHM functions.

• Computes the CIFs for the specified time points and covariate values.

• Generates plots for the CIFs, including adjusted CIFs based on specific covariate values.

• Provides separate plots for each model and a combined plot for all models.

If featureID and featureValue are provided, the function adjusts the CIFs based on the specified covariate values. If RiskNames is not provided, the default names "Risk1" and "Risk2" are used. The TypeMethod parameter determines which model's CIF plot is returned in the output.

Examples

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$`Event:3`
feat <- datt2[c('age', 'Primary_Secondary', 'Higher_Education')] |> 
   data.matrix()
timee <- datt2[['time']]
Initialize(feat, timee, d1, d2, 1e-10)
result <- Cmpp_CIF(
  featureID = c(1, 2),
  featureValue = c(0.5, 1.2),
  RiskNames = c("Event1", "Event2"),
  TypeMethod = "GOR",
  predTime = seq(0, 10, by = 0.5)
)
print(result$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