cif_est estimates the cumulative incidence function (CIF, i.e.risk) based on the cause-specific regression results with 95% confidence intervals, it also calculates the risk ratio and risk difference for the specific time point.
cif_est(data=,
time, time2 = NULL,
Event.var, Events, cif.event,
weight.type,
ties = NULL,
risktab = TRUE, risk.time = NULL)The dataset, output of doPS
See weight_cause_cox.
See weight_cause_cox.
The variable name for the event indicator which typically has at least 3 levels.
The vector of all the event name, the rest of levels in the Event.var will be treated as loss to follow up (i.e. right censoring).
Value of event of interest for the CIF.
See weight_cause_cox.
See weight_cause_cox.
Indicator whether the risk ratio and risk difference table should be returned.
If risktab, the specific time point for calculating the risk ratio and risk difference.
Returns a table containing the estimated CIF for the event of interest for control and treated group and their 95% confidence intervals.
If risktab, will return the risk ratio and risk difference at time risk.time, and their 95% confidence intervals.
After estimating the parameters in the cause-specific hazard \(\lambda_{j}^a\) using IPW, we could estimate the corresponding CIF:
$$ \hat{P}(T^a<t,J^a=j) = \int_{0}^{t} \hat{S}^a(u) d\hat{\Lambda}_{j}^a(u), $$
where \(\hat{S}^a\) is the estimated overall survial function for \(T^a\), \(\hat{S}^a(u) = e^{-\sum_j\hat{\Lambda}_{j}^a(u)}\), \(\hat\Lambda_{j}^a(u) = \hat\Lambda_{0j}(u)e^{\hat\beta*a}\), and \(\hat\Lambda_{0j}(u)\) is a Breslow-type estimator of the baseline cumulative hazard.
Hou, J., Paravati, A., Hou, J., Xu, R., & Murphy, J. (2018). “High-dimensional variable selection and prediction under competing risks with application to SEER-Medicare linked data,” Statistics in Medicine 37(24), 3486-3502.