Performs direct binomial regression modeling for cumulative incidence functions in competing risks analysis using the timereg package. This method provides direct modeling of cumulative incidence without proportional subdistribution hazards assumptions.
Usage
directbinomial(
data,
time,
event,
covs,
eventOfInterest = 1,
times = "1, 3, 5",
conf = 0.95,
residuals = FALSE,
predictions = TRUE,
bootstrap = FALSE,
nboot = 500,
showModel = TRUE,
showEducational = TRUE,
plotCIF = TRUE,
plotResiduals = FALSE
)Arguments
- data
The data as a data frame.
- time
Follow-up time variable
- event
Event type variable (0=censored, 1=event of interest, 2=competing event, etc.)
- covs
Covariates to include in the model
- eventOfInterest
Numeric code for the event of interest (primary event)
- times
Comma-separated list of time points for cumulative incidence prediction
- conf
Confidence level for confidence intervals
- residuals
Display model residuals and goodness-of-fit statistics
- predictions
Display predicted cumulative incidence at specified time points
- bootstrap
Use bootstrap methods for confidence interval estimation
- nboot
Number of bootstrap samples (when bootstrap is enabled)
- showModel
Display detailed model summary and parameter estimates
- showEducational
Display educational information about direct binomial regression
- plotCIF
Display cumulative incidence function plots
- plotResiduals
Display residual plots for model diagnostics
Value
A results object containing:
results$todo | a html | ||||
results$modelSummary | a table | ||||
results$educationalInfo | a html | ||||
results$coefficientsTable | a table | ||||
results$cumulativeIncidenceTable | a table | ||||
results$covariateEffectsTable | a table | ||||
results$goodnessOfFit | a table | ||||
results$residualAnalysis | a html | ||||
results$methodsInfo | a html | ||||
results$cifPlot | an image | ||||
results$residualPlot | an image |
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$modelSummary$asDF
as.data.frame(results$modelSummary)