Fit proportional subdistribution hazards models and return publication-ready subdistribution hazard ratios.
Arguments
- data
A data frame containing follow-up time, event status, and model variables.
- time
Numeric follow-up-time variable. Quoted and bare names work.
- event
Event-status variable with one censoring code and distinct codes for the event of interest and competing events.
- event_of_interest
Observed code or label for the event being modelled.
- exposures
Variables whose effects are reported.
- censor_code
Observed code or label for censoring. Defaults to `0`.
- adjust_for
Optional adjustment variables. One adjusted model is fitted per exposure and only that exposure is reported.
- multivariable
Logical; fit one model containing and reporting all variables in `exposures`.
- multivariate
Optional alias for `multivariable`.
- format
Output table format: `"flextable"` or `"gt"`.
- theme
Table styling preset.
- model_stats
Logical; return N, event counts, pseudo-log-likelihood, and convergence status.
- show_ref
Logical; display categorical reference rows.
Value
A `gtregression` object containing the publication table, numeric estimates, fitted `crr` models, model summaries, and analysis metadata.
Details
Each model uses the rows that are complete for its own variables. The N
column and the footnote report the observations and the events of interest
used by the fitted models, with the same wording as cox_reg() and
surv_reg().
Fine-Gray regression models the subdistribution hazard for the selected event. An SHR above 1 indicates a higher cumulative incidence of that event over follow-up, accounting for the presence of competing events. It is not a cause-specific Cox hazard ratio and should not be interpreted as one.
Examples
competing_risk_reg(
data_competing_risk,
time = follow_up_months,
event = outcome,
event_of_interest = Relapse,
censor_code = Censored,
exposures = c(treatment, age, stage)
)