Package index • tteICE

All functions

bmt: Data from Section 1.3 of Klein and Moeschberger (1997)
plot(<tteICE>): Plot method for 'tteICE' objects
plot_ate(): Plot estimated treatment effects
plot_inc(): Plot estimated cumulative incidence functions (CIFs)
predict(<tteICE>): Predict method for 'tteICE' objects at specific time points
print(<tteICE>): Print method for 'tteICE' objects
scr.composite(): Fit CIFs using composite variable strategy for semicompeting risks data
scr.composite.eff(): Fit CIFs using composite variable strategy for semicompeting risks data, based on efficient influence functions
scr.natural(): Fit CIFs using hypothetical strategy (I) for semicompeting risks data
scr.natural.eff(): Fit CIFs using hypothetical strategy (I) for semicompeting risks data, based on efficient influence functions
scr.principal(): Fit CIFs using principal stratum strategy for semicompeting risks data
scr.principal.eff(): Fit CIFs using principal stratum strategy for semicompeting risks data, based on efficient influence functions
scr.removed(): Fit CIFs using hypothetical strategy (II) for semicompeting risks data
scr.removed.eff(): Fit CIFs using hypothetical strategy (II) for semicompeting risks data, based on efficient influence functions
scr.treatment(): Fit CIFs using treatment policy strategy for semicompeting risks data
scr.treatment.eff(): Fit CIFs using treatment policy strategy for semicompeting risks data, based on efficient influence functions
scr.tteICE(): Fit CIFs for semicompeting risks time-to-event data with intercurrent events.
scr.whileon(): Fit CIFs using while on treatment strategy for semicompeting risks data
scr.whileon.eff(): Fit CIFs using while on treatment strategy for semicompeting risks data, based on efficient influence functions
summary(<tteICE>): Summary method for 'tteICE' objects
surv.HR(): Estimate hazard ratios
surv.boot(): Calculate standard errors for estimated CIFs and treatment effects
surv.composite(): Fit CIFs using composite variable strategy for competing risks data
surv.composite.eff(): Fit CIFs using composite variable strategy for competing risks data, based on efficient influence functions
surv.natural(): Fit CIFs using hypothetical strategy (I) for competing risks data
surv.natural.eff(): Fit CIFs using hypothetical strategy (I) for competing risks data, based on efficient influence functions
surv.principal(): Fit CIFs using principal stratum strategy for competing risks data
surv.principal.eff(): Fit CIFs using principal stratum strategy for competing risks data, based on efficient influence functions
surv.removed(): Fit CIFs using hypothetical strategy (II) for competing risks data
surv.removed.eff(): Fit CIFs using hypothetical strategy (II) for competing risks data, based on efficient influence functions
surv.treatment(): Fit CIFs using treatment policy strategy for competing risks data
surv.treatment.eff(): Fit CIFs using treatment policy strategy for competing risks data, based on efficient influence functions
surv.tteICE(): Fit CIFs for competing risks time-to-event data with intercurrent events.
surv.whileon(): Fit CIFs using while on treatment strategy for competing risks data
surv.whileon.eff(): Fit CIFs using while on treatment strategy for competing risks data, based on efficient influence functions
tteICE-package: tteICE: Treatment Effect Estimation for Time-to-Event Data with Intercurrent Events
tteICE(): Using formula to fit CIFs for time-to-event data with intercurrent events
tteICEShiny(): Shiny app for tteICE