
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: m.townsley@ucl.ac.uk
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