BaselineHazard_NOIMP: BaselineHazard_NOIMP

In lbeesleyBIOSTAT/MultiCure: EM Algorithms for Fitting Multistate Cure Models

Description

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

Usage

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

Arguments

datWIDE	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
Cov	matrix of covariates used in MultiCure (may have missingness)
beta	Current estimate of beta
alpha	Current estimate of alpha
TransCov	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).
ASSUME	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
p	The current estimate of the E-step weights

Value

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.