Performs competing risk analysis using the Fine-Gray model to estimate cumulative incidence functions and subhazard ratios.
Usage
competing_risk_analysis(
data,
time_var,
event_var,
trt_var,
main_event,
competing_events,
covariates = NULL,
conf_level = 0.95,
time_points = NULL,
reference_group = NULL
)Arguments
- data
A data frame containing the survival data
- time_var
Character. Name of the time variable
- event_var
Character. Name of the event indicator variable
- trt_var
Character. Name of the treatment variable
- main_event
Integer. Code for the main event of interest
- competing_events
Integer vector. Codes for competing events
- covariates
Character vector of covariate names
- conf_level
Numeric. Confidence level for intervals (default: 0.95)
- time_points
Numeric vector of specific time points to estimate CIF. If NULL, uses automatic grid based on data.
- reference_group
Character. Reference group for treatment comparison. If NULL, uses first level of treatment variable.
Details
The Fine-Gray model estimates the cumulative incidence function (CIF): F_k(t) = P(T <= t, epsilon = k)
where T is the event time and epsilon is the event type.
The model uses a proportional subhazards approach: lambda_k(t|X) = lambda_{0k}(t) * exp(beta_k'X)
where lambda_k is the subhazard for event type k.
References
Fine, J.P. and Gray, R.J. (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association, 94(446), 496-509.
Examples
if (FALSE) { # \dontrun{
# Basic competing risk analysis
result <- competing_risk_analysis(
data = survival_data,
time_var = "time",
event_var = "event",
trt_var = "TRT01P",
main_event = 1,
competing_events = c(2, 3)
)
# With covariates and custom time points
result <- competing_risk_analysis(
data = survival_data,
time_var = "time",
event_var = "event",
trt_var = "TRT01P",
main_event = 1,
competing_events = c(2),
covariates = c("age", "sex"),
time_points = c(6, 12, 24)
)
} # }