FreqID_HReg: The function to fit parametric Weibull models for the...
In SemiCompRisks: Hierarchical Models for Parametric and Semi-Parametric Analyses of Semi-Competing Risks Data

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

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.

1 2	FreqID_HReg(Formula, data, model="semi-Markov", frailty = TRUE, na.action = "na.fail", subset=NULL)

`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+δ_1 \| y_2+δ_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, 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`.

See BayesID_HReg for a detailed description of the models.

FreqID_HReg returns an object of class Freq_HReg.

Sebastien Haneuse and Kyu Ha Lee
Maintainer: Kyu Ha Lee <klee15239@gmail.com>

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.

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

## 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)

Loading required package: MASS
Loading required package: survival
Loading required package: Formula

Analysis of independent semi-competing risks data 
semi-Markov assumption for h3
Confidence level: 0.05

Variance of frailties, theta:
 Estimate    SE    LL    UL
    0.739 0.081 0.596 0.916

Regression coefficients:
   Estimate    SE     LL     UL
x1    0.234 0.038  0.160  0.308
x2    0.604 0.041  0.523  0.685
x3   -0.129 0.037 -0.202 -0.056
x1    0.404 0.062  0.283  0.525
x2    0.723 0.066  0.593  0.853
x1    0.595 0.054  0.490  0.700
x2    0.742 0.056  0.633  0.852

Note: Covariates are arranged in order of transition number, 1->3.
[1] "coef"       "theta"      "h0"         "code"       "logLike"   
[6] "nP"         "class"      "conf.level"

Analysis of independent semi-competing risks data 
semi-Markov assumption for h3
Confidence level: 0.05

Hazard ratios:
   beta1    LL    UL beta2    LL    UL beta3    LL    UL
x1 1.264 1.174 1.361 1.498 1.327 1.691 1.813 1.632 2.014
x2 1.829 1.687 1.983 2.061 1.810 2.346 2.101 1.883 2.345
x3 0.879 0.817 0.945    NA    NA    NA    NA    NA    NA

Variance of frailties:
      Estimate    LL    UL
theta    0.739 0.596 0.916

Baseline hazard function components:
                    h1-PM     LL     UL  h2-PM     LL     UL  h3-PM     LL
Weibull: log-kappa -1.170 -1.262 -1.079 -2.869 -3.049 -2.690 -2.792 -2.959
Weibull: log-alpha -0.536 -0.597 -0.475 -0.323 -0.410 -0.235 -0.510 -0.578
                       UL
Weibull: log-kappa -2.625
Weibull: log-alpha -0.442