Dear R Helpers,
I am trying to carry out survival analysis in the presence of long term
survivors (immunes/cureds). This involves using a split population model
(some call it a mixture model) where the assumption of eventual failure is
relaxed. I am following closely the formulation by Maller and Zhou (1996).
Parametric modelling in this situation involves the introduction of a new
parameter, p: the proportion of susceptibles. For the Weibull the
cumulative distribution function would look like:
F(t) = p*[1 e^(-lambda*t)^scale]
where [...] is the conventional CDF for the Weibull distribution.
I have spent a fair deal of time trying to create a new survreg
distribution but have made next to no progress. I understand that there
are four 'primary' distributions and any other distribution can be derived
from transformations of these. I am unclear how I might 'declare' this new
parameter in the newly defined distribution. I am keen to establish how to
do this for more than just the Weibull.
Searching R-Help hasn't led me to any obvious solution. The
survreg.distribution help page has an example for user-defined
distributions but it only looks like a name change.
Can anyone point me in the right direction? It had occurred to me that I
could define the likelihood function myself and use mle() but I am
uncertain whether this is appropriate for censored data.
Thanks for any advice (and great software......and support),
MT
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Michael Townsley
Email: [EMAIL PROTECTED]
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