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

This function fits a discrete-time survival model with and without random effects.

1 |

`formula` |
a formula object, with the response on the left of a ~ operator, and the terms on the right. The response must be a survival object as returned by |

`data` |
a data.frame in which to interpret the variables named in the formula. This augmented data frame can be returned by function |

`na.action` |
a function which indicates what should happen when the data contain NAs. |

`initial` |
a vector of initial values for the paramters to be optimized over. If NULL, the default initial values will be used. |

`lower` |
a vector of lower bound values for the paramters. If NULL, the default lower bound will be used. |

`upper` |
a vector of upper bound values for the parameters. If NULL, the default upper bound will be used. |

`frailty` |
logic value: if TRUE, the discrete-time survival model with random effects will be run. Otherwise it is assumed that there is no random effect. The default is TRUE. |

Kang et al. (2015) proposed to use a discrete-time survival model with gamma-distributed random effects and a complementary log-log link function to model data from repeated low-dose challenge studies, assuming an animal's risks of infection across challenges are independent of each other conditional on random effects. Please refer to Kang et al.(2015) for more details.

`rld`

returns an object of class “rld”. The functions `summary`

is used to obtain and print a summary of the results.

`coefficients` |
a vector of parameter estimates. |

`hessian` |
the hessian matrix returned from the function |

`X` |
the design matrix created based on the input formula. |

`VEexpr` |
the formula expression on the right of ~ operator. |

`loglikvalue` |
the log-likelihood value. |

`call` |
the matched call. |

`frailty` |
the chosen model. |

`augdata` |
the augmented data set. |

Bin Yao, Ying Huang and Chaeryon Kang

Kang, C., Huang, Y., and Miller, C. (2015). A discrete-time survival model with random effects for designing and analyzing repeated low-dose challenge experiments. *Biostatistics*, 16(2): 295-310.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ```
data(SampleData)
newdata <- transdata(data = SampleData, ndlevel = 3, nexposure = c(10, 10, 2))
#interaction between the hightest dose level and treatment assignment
#under the discrete-time survival model with random effects
fitout1 <- rld(formula = survival::Surv(time, delta)~factor(dose)+trt+I(I(dose==3)*trt),
data = newdata, frailty = TRUE)
#summary(fitout1)
## Not run:
#main effects model without random effectss
ini <- rep(0.5, 4)
lwr <- rep(-Inf, 4)
upr <- rep(Inf, 4)
fitout2 <- rld(formula = survival::Surv(time, delta)~factor(dose)+trt,
initial = ini, lower = lwr, upper = upr, data = newdata,
frailty = FALSE)
#summary(fitout2)
## End(Not run)
``` |

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