coxed.npsf.tvc {coxed}

Predict expected durations using the GAM method with time-varying covariates

Description

This function is called by coxed and is not intended to be used by itself.

Usage

coxed.npsf.tvc(cox.model, newdata = NULL, coef = NULL, b.ind = NULL)

Arguments

cox.model

The output from a Cox proportional hazards model estimated with the coxph function in the survival package or with the cph function in the rms package

newdata

An optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used

coef

A vector of new coefficients to replace the coefficients attribute of the cox.model. Used primarily for bootstrapping, to recalculate durations using new coefficients derived from a bootstrapped sample. If NULL, the original coefficients are employed

b.ind

A vector of observation numbers to pass to the estimation sample to construct the a bootstrapped sample with replacement

Details

This function employs the NPSF method of generating expected durations described in Kropko and Harden (2018). See coxed.npsf for details. This code replicates the code for cox.npsf, but works with data whose structure allows time-varying covariates, and requires using the time2 argument of the Surv function. This function requires the data to be reported as cumulative durations. The cumulative baseline hazard function model is estimated using the ending times for each interval. Then the expected durations are drawn from the Cox model and the NPSF method as with coxed.npsf.

Value

Returns a list containing the following components:

exp.dur	A vector of predicted mean durations for the estimation sample if newdata is omitted, or else for the specified new data.
baseline.functions	The estimated cumulative baseline hazard function and survivor function.

Author(s)

Jonathan Kropko <[e-mail]> and Jeffrey J. Harden <[e-mail]>

References

Kropko, J. and Harden, J. J. (2018). Beyond the Hazard Ratio: Generating Expected Durations from the Cox Proportional Hazards Model. British Journal of Political Science https://doi.org/10.1017/S000712341700045X

See Also

coxed, coxed.npsf

Examples

bs.surv <- Surv(time = boxsteffensmeier$start, time2 = boxsteffensmeier$te,
     event = boxsteffensmeier$cut_hi)
bs.cox <- coxph(bs.surv ~ ec + dem + south + iv, data = boxsteffensmeier, method = "breslow")

ed <- coxed.npsf.tvc(bs.cox)
ed$exp.dur