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Re: st: Mixed continuous and interval censored time-to-event analysis

From   Steve Samuels <>
Subject   Re: st: Mixed continuous and interval censored time-to-event analysis
Date   Thu, 4 Oct 2012 16:57:50 -0400

The following commands can take mixture of interval censoring and uncensored
data( the proper term for what you are calling "continuous").
-intcens- (SSD)
-stpm- (SSC)

They work, because each assumes a parametric model. For parametric models, the likelihood contributions of different types of observations
(uncensored, left-censored, right-censored, interval-censored, late
entry) are well-defined. The likelihood analysis of parametric models is
covered in every text, and you can find some good ones in the Stata
Manual references to -streg-.

The lag between the actual failure event and hospital detection means
that the hospital events are interval-censored. To ignore the lag, you
must have strong evidence that it is "short". A better approach, still
more "exact" in comparison to questionnaire-based detections, is to treat
the hospital-based admissions as interval censored, but with interval lower
endpoints based on theory or on empirical knowledge.

Another issue: if failure is associated with hospitalization,
then the hospital-detected events are a biased sample of all events.


On Oct 3, 2012, at 10:26 AM, MacLennan, Graeme wrote:

Dear Statalist, I have data on time to an event, the event is "failure" in a randomised controlled trial.  Information on failure is collected through two channels.  Firstly, annual questionnaires where failure is defined as being below a certain cut-off on a self-reported outcome measure, although reported annually this failure will have occurred at some point between the last non-failure questionnaire the failure questionnaire, I consider this to be interval censored time-to-event data.  Secondly failure data is captured through routine data sources on hospital readmissions, and as such is a more exact representation of failure time (putting aside any concerns one might have about lag between time of failure and admission to hospital), and I consider this to be continuous time-to-event data.

A clear strategy is to aggregate the continuous data up to interval censored data and use appropriate methods, but this seems like a waste of information to me.  However, after some initial digging about I can't find any pointers, so my question is do the list members know of any literature on this, particularly with Stata in mind?

Regards, Graeme.

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