w1Fint

Note that in R there is the numeric routine stats::qgamma which could also be used
as in <code>stats::qgamma(p=.5, shape = 10, rate = 10)</code> for n=10.

Fit parametric models for time-to-event data that show an initial
'incubation period', i.e., a variable delay phase where no events occur.
The delayed Weibull distribution serves as the foundational data model.
For parameter estimation, different flavours of maximum likelihood
estimation ('MLE') and the method of maximum product of spacings estimation
('MPSE') are implemented. Bootstrap confidence intervals for parameters and
significance tests in a two group setting are provided.

Matthias Kuhn

incubate

Parametric Time-to-Event Analysis with Variable Incubation
Phases

w1Fint function

<dl><dt>nObs</dt>
<dd>numeric. number of observations (vectorized)</dd></dl>

Arguments

Internal MLEw-weight W1 function according to sampling distribution
Weight W1 for given sample sizes (of one group).
For small nObs we use direct results from Monte-Carlo simulation.
For higher nObs we use an approximation (based on Wilson-Hilferty transformation). — w1Fint

<dl>

<dt>nObs</dt>
<dd>numeric. number of observations (vectorized)</dd>

</dl>

Internal MLEw-weight W1 function according to sampling distribution
Weight W1 for given sample sizes (of one group).
For small <code>nObs</code> we use direct results from Monte-Carlo simulation.
For higher <code>nObs</code> we use an approximation (based on Wilson-Hilferty transformation).

w1Fint: Internal MLEw-weight W1 function according to sampling distribution Weight W1 for given sample sizes (of one group). For small `nObs` we use direct results from Monte-Carlo simulation. For higher `nObs` we use an approximation (based on Wilson-Hilferty transformation).

Description

Usage

Value

Arguments