SemiCompRisks (version 3.4)

FreqID_HReg: The function to fit parametric Weibull models for the frequentist anlaysis of semi-competing risks data.

Description

Independent semi-competing risks data can be analyzed using hierarchical models. Markov or semi-Markov assumption can be adopted for the conditional hazard function for time to the terminal event given time to non-terminal event.

Usage

FreqID_HReg(Formula, data, model="semi-Markov", frailty = TRUE, 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 non-terminal event + corresponding censoring indicator | time to terminal event + corresponding censoring indicator \(\sim\) covariates for \(h_1\) | covariates for \(h_2\) | covariates for \(h_3\): i.e., \(y_1\)+\(\delta_1\) | \(y_2\)+\(\delta_2\) ~ \(x_1\) | \(x_2\) | \(x_3\).

data

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

model

a character value that specifies the type of a model based on the assumption on \(h_3\): "semi-Markov" or "Markov".

frailty

a logical value to determine whether to include the subject-specific shared frailty term, \(\gamma\), into the model.

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

FreqID_HReg returns an object of class Freq_HReg.

Details

See BayesID_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, BayesID_HReg.

Examples

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

form <- Formula(time1 + event1 | time2 + event2 ~ x1 + x2 + x3 | x1 + x2 | x1 + x2)

fit_WB	<- FreqID_HReg(form, data=scrData, model="semi-Markov")

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")
# }