I uploaded eha-1.2-0 to CRAN a day ago. This version can handle
accelerated failure time and proportional hazards parametric models
for right censored, left truncated, and stratified data. Included
distributions are: Weibull, lognormal, loglogistic, Gompertz, and
extreme value. The new inner structure of eha makes it very simple
(for me) to add families of distributions of a shape-scale type (i.e.
the distribution of the log of the "baseline" survival time is a
location-scale family). Suggestions are welcome. The Gompertz and
Gompertz-Makeham families of distributions are unfortunately not of
this type. While the Gompertz case could be solved by a "trick"
(always include an intercept in the regression model), the
Gompertz-Makeham case is harder. Needs special treatment; hints are
welcome.
With the possibility of keeping the shape parameter(s) fixed, special
cases of the abovementioned distributions are also possible to fit,
e.g., the exponential (Weibull with shape = 1).
Göran
On Wed, Jan 23, 2008 at 5:46 PM, Beth Ireland <[EMAIL PROTECTED]> wrote:
> Dear All,
>
> I would like to fit some parametric survival models using left
> truncated, right censored data in R. However I am having problems
> finding a function to fit parametric survival models which can handle
> left truncated data.
>
> I have tested both the survreg function in package survival:
>
> fit1 <- survreg(Surv(start, stop, status) ~ X + Y + Z, data=data1)
>
> and the psm function in package Design:
>
> fit2 <- psm(Surv(start, stop, status) ~ X + Y + Z, data=data1)
>
>
> But neither function appears to work with left truncated data. The error
> message "Invalid survival type" is received for both functions when left
> truncated data is specified.
>
> The function weibreg in the eha package fits Weibull survival models and
> works with left truncated data. This function is useful, but I need to
> fit parametric models other than Weibull.
>
> Any suggestions of functions that can fit parametric survival models
> other than Weibull on left truncated, right censored data would be
> greatly appreciated.
>
> Kind Regards,
>
> Beth
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