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st: RE: Varying survival distributions & interval censoring (2)
> -----Original Message-----
> From: firstname.lastname@example.org
> [mailto:email@example.com] On Behalf Of
> Sent: 23 June 2004 11:07
> To: firstname.lastname@example.org
> Subject: st: Varying survival distributions & interval censoring (2)
> My thanks to Stephen Jenkins for his pointer below, I found
> the notes quite detailed and helpful.
> The "Discrete" entry in the [ST] Manual reads:
> Discrete-time survival analysis concerns analysis of
> time-to-event data whenever survival times are either
> (a) intrinsically discrete (e.g. numbers of machine cycles), or
> (b) grouped into discrete intervals of time ("interval
> censoring"). If intervals are of equal length, then the same
> methods can be applied to both
> (a) and (b): survival times are positive integers.
> In the data set I am handling, every subject has his/her OWN
> interval of time, that is their actual failure happens
> between two home-visit dates that the fieldworker makes. The
> home-visit dates allow us to calculate the exact ages of the
> subjects who fall in one of 3 categories:
> i. Left-censored: the fieldworker initiates the home visits and
> observes the failure has already taken place.
> ii. Interval-censored: failure has take place between two
> home visits,
> and the fieldworker has record of both dates.
> iii. Right-censored: the fieldworker completes the last home
> visit and
> failure has not been observed.
> I am not sure exactly on how to structure the data to reflect
> the 3-states above to use with "streg" or a similar model
> that would allow me to vary the distribution of the failure
> time (between exponential, weibull, lognormal, loglogistic
> and generalised gamma).
If I understand you correctly, you won't be able to use -streg-. You
will have to program yourself, using -ml-, where the sample
log-likelihood expression will include a number of different components
corresponding to each of (i)-(iii) above. [The text by Klein and
Moeschberger, /Survival Analysis/ has a taxonomy of different likelihood
contribution types for the different types of spell that you mention.]
At the stage at which you characterise the contributions, you will have
to also decide which failure time distribution to assume.
Professor Stephen P. Jenkins <email@example.com>
Institute for Social and Economic Research
University of Essex, Colchester CO4 3SQ, U.K.
Tel: +44 1206 873374. Fax: +44 1206 873151.
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