initial_fit_em_weights: Fit the MSM Cox Model with IP Weights

In semicmprskcoxmsm: Use Inverse Probability Weighting to Estimate Treatment Effect for Semi Competing Risks Data

Fit the MSM Cox Model with IP Weights

Description

Fit the MSM cox model with IPW as the initial value for EM algorithm to fit the illness-death general Markov model

Usage

initial_fit_em_weights(data,X1,X2,event1,event2,w,Trt)

Arguments

data	The dataset, includes non-terminal events, terminal events as well as event indicator.
X1	Time to non-terminal event, could be censored by terminal event or lost to follow up.
X2	Time to terminal event, could be censored by lost to follow up.
event1	Event indicator for non-terminal event.
event2	Event indicator for terminal event.
w	IP weights.
Trt	Treatment variable.

Details

As initial values we use for β_j, j=1, 2, 3, the estimates from IP weighted Cox regression without the offsets, i.e. from the usual Markov model.

Value

A list of objects from survival package:

event1	An object of class Surv for non-terminal event.
event2	An object of class Surv for terminal event without non-terminal event.
event3	An object of class Surv for terminal event following non-terminal event.
fit1	An object of class coxph representing the fit for time to non-terminal event. See coxph.object for details.
fit2	An object of class coxph representing the fit for time to terminal event without non-terminal event.
fit3	An object of class coxph representing the fit for time to terminal event following non-terminal event.

See Also

Surv, coxph

semicmprskcoxmsm documentation built on April 30, 2022, 1:08 a.m.