ic_par: Parametric Regression Models for Interval Censored Data
In icenReg: Regression Models for Interval Censored Data

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	ic_par(formula, data, model = "ph", dist = "weibull", weights = NULL)

`formula`	Regression formula. Response must be a `Surv` object of type `'interval2'` or `cbind`. See details.
`data`	Dataset
`model`	What type of model to fit. Current choices are "`ph`" (proportional hazards), "`po`" (proportional odds) or "`aft`" (accelerated failure time)
`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

data(miceData)

logist_ph_fit <- ic_par(Surv(l, u, type = 'interval2') ~ grp, 
                       data = miceData, dist = 'loglogistic')

logist_po_fit <- ic_par(cbind(l, u) ~ grp, 
                        data = miceData, dist = 'loglogistic',
                       model = 'po')

summary(logist_ph_fit)
summary(logist_po_fit)

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