SemiCompRisks (version 3.4)

FreqSurv_HReg: The function to fit parametric Weibull models for the frequentist analysis of univariate survival data.

Description

Independent univariate right-censored survival data can be analyzed using hierarchical models.

Usage

FreqSurv_HReg(Formula, data, na.action = "na.fail", subset=NULL)

Arguments

Formula

a Formula object, with the outcome on the left of a \(\sim\), and covariates on the right. It is of the form, time to event + censoring indicator \(\sim\) covariates: i.e., \(y\)+\(\delta\) ~ \(x\).

data

a data.frame in which to interpret the variables named in Formula.

na.action

how NAs are treated. See model.frame.

subset

a specification of the rows to be used: defaults to all rows. See model.frame.

Value

FreqSurv_HReg returns an object of class Freq_HReg.

Details

See BayesSurv_HReg for a detailed description of the models.

References

Lee, K. H., Haneuse, S., Schrag, D., and Dominici, F. (2015), Bayesian semiparametric analysis of semicompeting risks data: investigating hospital readmission after a pancreatic cancer diagnosis, Journal of the Royal Statistical Society: Series C, 64, 2, 253-273. Alvares, D., Haneuse, S., Lee, C., Lee, K. H. (2019), SemiCompRisks: An R package for the analysis of independent and cluster-correlated semi-competing risks data, The R Journal, 11, 1, 376-400.

See Also

print.Freq_HReg, summary.Freq_HReg, predict.Freq_HReg, BayesSurv_HReg.

Examples

# NOT RUN {
# loading a data set	
data(survData)

form <- Formula(time + event ~ cov1 + cov2)

fit_WB <- FreqSurv_HReg(form, data=survData)
fit_WB
summ.fit_WB <- summary(fit_WB); names(summ.fit_WB)
summ.fit_WB
pred_WB <- predict(fit_WB, tseq=seq(from=0, to=30, by=5))
plot(pred_WB, plot.est="Haz")
plot(pred_WB, plot.est="Surv")
# }