BaselineHazard_NOIMP: BaselineHazard_NOIMP

Description Usage Arguments Value

View source: R/EstimateCoxBaselines.R


The function BaselineHazard_NOIMP is used to estimate the baseline hazard functions within the EM algorithm under COX baseline hazards


BaselineHazard_NOIMP(datWIDE, Cov, beta, alpha, TransCov, ASSUME, p)



A data frame with the following columns:

  • Y_R, the recurrence event/censoring time

  • delta_R, the recurrence event/censoring indicator

  • Y_D, the death event/censoring time

  • delta_D, the death event/censoring indicator

  • G, the cure status variable. This takes value 1 for known non-cured, 0 for "known" cured and NA for unknown cure status


matrix of covariates used in MultiCure (may have missingness)


Current estimate of beta


Current estimate of alpha


a list with elements: Trans13, Trans24, Trans14, Trans34, PNonCure. Each list element is a vector containing the names of the variables in Cov to be used in the model for the corresponding transition. 13 is NonCured -> Recurrence, 24 is Cured -> Death, 14 is NonCured -> Death, 34 is Recurrence -> Death. PNonCure contains the names of the covariates for the logistic regression for P(NonCure).


This variables indicates what equality assumptions we are making regarding the 24 and 14 transitions. The possible options are:

  • 'SameHazard': Lambda_14(t) = Lambda_24(t)

  • 'AllSeparate': No restrictions on Lambda_14(t) and Lambda_24(t)

  • 'ProportionalHazard': Lambda_14(t) = Lambda_24(t) exp(Beta0)

  • 'SameBaseHaz': Lambda^0_14(t) = Lambda^0_24(t), No restrictions on beta_14 and beta_24


The current estimate of the E-step weights


EST a list containing a step function estimate for the CUMULATIVE baseline hazard function for each transition in the following order: 1->3, 2->4, 1->4, 3->4

lbeesleyBIOSTAT/MultiCure documentation built on July 10, 2019, 5:27 a.m.