Description Usage Arguments Details Author(s) Examples

Fits a parametric regression model for interval censored data. Can fita proportional hazards, proportional odds or accelerated failure time model.

1 |

`formula` |
Regression formula. Response must be a |

`data` |
Dataset |

`model` |
What type of model to fit. Current choices are " |

`dist` |
What baseline parametric distribution to use. See details for current choices |

`weights` |
vector of case weights. Not standardized; see details |

Currently supported distributions choices are "exponential", "weibull", "gamma", "lnorm", "loglogistic" and "generalgamma" (i.e. generalized gamma distribution).

Response variable should either be of the form `cbind(l, u)`

or `Surv(l, u, type = 'interval2')`

,
where `l`

and `u`

are the lower and upper ends of the interval known to contain the event of interest.
Uncensored data can be included by setting `l == u`

, right censored data can be included by setting
`u == Inf`

or `u == NA`

and left censored data can be included by setting `l == 0`

.

Does not allow uncensored data points at t = 0 (i.e. `l == u == 0`

), as this will
lead to a degenerate estimator for most parametric families. Unlike the current implementation
of survival's `survreg`

, does allow left side of intervals of positive length to 0 and
right side to be `Inf`

.

In regards to weights, they are not standardized. This means that if weight[i] = 2, this is the equivalent to having two observations with the same values as subject i.

For numeric stability, if abs(right - left) < 10^-6, observation are considered uncensored rather than interval censored with an extremely small interval.

Clifford Anderson-Bergman

1 2 3 4 5 6 7 8 9 10 11 |

```
Loading required package: survival
Loading required package: Rcpp
Loading required package: coda
Model: Cox PH
Baseline: loglogistic
Call: ic_par(formula = Surv(l, u, type = "interval2") ~ grp, data = miceData,
dist = "loglogistic")
Estimate Exp(Est) Std.Error z-value p
log_alpha 6.6310 758.500 0.08673 76.460 0.00000
log_beta 0.9596 2.611 0.38670 2.482 0.01308
grpge 0.8098 2.247 0.32280 2.509 0.01211
final llk = -80.23726
Iterations = 15
Model: Proportional Odds
Baseline: loglogistic
Call: ic_par(formula = cbind(l, u) ~ grp, data = miceData, model = "po",
dist = "loglogistic")
Estimate Exp(Est) Std.Error z-value p
log_alpha 6.603 737.2000 0.07747 85.230 0.000000
log_beta 1.001 2.7200 0.38280 2.614 0.008946
grpge -1.172 0.3097 0.47130 -2.487 0.012880
final llk = -80.30575
Iterations = 10
```

icenReg documentation built on Oct. 16, 2018, 1:04 a.m.

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