FreqID_HReg: The function to fit parametric Weibull models for the...

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/FreqID_HReg.R

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

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FreqID_HReg(Formula, data, model="semi-Markov", frailty = TRUE, na.action = "na.fail",
subset=NULL)

Arguments

Formula

a Formula object of the form y_1+δ_1 | y_2+δ_2 ~ x_1 | x_2 | x_3. See Details and Examples below.

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, γ, 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.

Details

See BayesID_HReg for a detailed description of the models.

Value

FreqID_HReg returns an object of class Freq_HReg.

Author(s)

Sebastien Haneuse and Kyu Ha Lee
Maintainer: Kyu Ha Lee <[email protected]>

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. (2018+), SemiCompRisks: an R package for independent and cluster-correlated analyses of semi-competing risks data, submitted, arXiv:1801.03567.

See Also

print.Freq_HReg, summary.Freq_HReg, predict.Freq_HReg, BayesID_HReg.

Examples

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## 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")

## End(Not run)

SemiCompRisks documentation built on May 7, 2018, 9:04 a.m.