cif_est {cmprskcoxmsm} | R Documentation |
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)
data |
The dataset, output of |
time |
See |
time2 |
See |
Event.var |
The variable name for the event indicator which typically has at least 3 levels. |
Events |
The vector of all the event name, the rest of levels in the |
cif.event |
Value of event of interest for the CIF. |
weight.type |
See |
ties |
See |
risktab |
Indicator whether the risk ratio and risk difference table should be returned. |
risk.time |
If |
After estimating the parameters in the cause-specific hazard λ_{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{Λ}_{j}^a(u),
where \hat{S}^a is the estimated overall survial function for T^a, \hat{S}^a(u) = e^{-∑_j\hat{Λ}_{j}^a(u)}, \hatΛ_{j}^a(u) = \hatΛ_{0j}(u)e^{\hatβ*a}, and \hatΛ_{0j}(u) is a Breslow-type estimator of the baseline cumulative hazard.
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.
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.