Package index • tteICE

All functions
`bmt`	Data from Section 1.3 of Klein and Moeschberger (1997)
`plot(<tteICE>)`	Graphical results of tteICE
`plot_ate()`	Plot the estimated treatment effect
`plot_inc()`	Plot the estimated cumulative incidence function (CIF)
`print(<tteICE>)`	Print a short summary of the estimated treatment effect
`riskpredict()`	Risk prediction at specific time points
`scr.composite()`	Fit the CIF using composite variable strategy for semicompeting risks data
`scr.composite.eff()`	Fit the CIF using composite variable strategy for semicompeting risks data, based on efficient influence functions
`scr.natural()`	Fit the CIF using hypothetical strategy (I) for semicompeting risks data
`scr.natural.eff()`	Fit the CIF using hypothetical strategy (I) for semicompeting risks data, based on efficient influence functions
`scr.principal()`	Fit the CIF using principal stratum strategy for semicompeting risks data
`scr.principal.eff()`	Fit the CIF using principal stratum strategy for semicompeting risks data, based on efficient influence functions
`scr.removed()`	Fit the CIF using hypothetical strategy (II) for semicompeting risks data
`scr.removed.eff()`	Fit the CIF using hypothetical strategy (II) for semicompeting risks data, based on efficient influence functions
`scr.treatment()`	Fit the CIF using treatment policy strategy for semicompeting risks data
`scr.treatment.eff()`	Fit the CIF using treatment policy strategy for semicompeting risks data, based on efficient influence functions
`scr.tteICE()`	Fit the CIF for time-to-event data with intercurrent events for semicompeting risks data
`scr.whileon()`	Fit the CIF using while on treatment strategy for semicompeting risks data
`scr.whileon.eff()`	Fit the CIF using while on treatment strategy for semicompeting risks data, based on efficient influence functions
`surv.HR()`	Estimate the hazard ratio with intercurrent events
`surv.boot()`	Calculate the standard error for the estimated CIF and treatment effect
`surv.composite()`	Fit the CIF using composite variable strategy for competing risks data
`surv.composite.eff()`	Fit the CIF using composite variable strategy for competing risks data, based on efficient influence functions
`surv.natural()`	Fit the CIF using hypothetical strategy (I) for competing risks data
`surv.natural.eff()`	Fit the CIF using hypothetical strategy (I) for competing risks data, based on efficient influence functions
`surv.principal()`	Fit the CIF using principal stratum strategy for competing risks data
`surv.principal.eff()`	Fit the CIF using principal stratum strategy for competing risks data, based on efficient influence functions
`surv.removed()`	Fit the CIF using hypothetical strategy (II) for competing risks data
`surv.removed.eff()`	Fit the CIF using hypothetical strategy (II) for competing risks data, based on efficient influence functions
`surv.treatment()`	Fit the CIF using treatment policy strategy for competing risks data
`surv.treatment.eff()`	Fit the CIF using treatment policy strategy for competing risks data, based on efficient influence functions
`surv.tteICE()`	Fit the CIF for time-to-event with intercurrent events for competing risks data
`surv.whileon()`	Fit the CIF using while on treatment strategy for competing risks data
`surv.whileon.eff()`	Fit the CIF using while on treatment strategy for competing risks data, based on efficient influence functions
`tteICEShiny()`	Shiny app for tteICE

Reference

All functions