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From |
Steven Samuels <sjhsamuels@earthlink.net> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: partially retrospective survival analysis |

Date |
Wed, 9 Apr 2008 16:31:19 -0400 |

Alan, the situation you describe is an example of left-censoring. Recall that right censoring at a time T provides information that the event time Y >T. The same applies to left-censoring-- Y<T. This is generally handled by parametric models, as the likelihood contribution of a left-censored event is F(T), where F(t) = P(Y<t). In Stata, the programs -stpm- (use the leftvar() option and -intreg- (and maybe some others) will do this. I cannot find a Stata command to implement Bruce Turnbull's 1976 nonparametric estimate, but -stpm- gives flexible models.

-Steve

(Turnbull, B.W. (1976) The empirical distribution function with arbitrarily grouped, censored and truncated data. J. R. Statist. Soc. B, 38, 290-295

On Apr 9, 2008, at 3:23 PM, Feiveson, Alan H. (JSC-SK311) wrote:

Hi - Has anyone seen models for or tried doing a survival analysis when

for some observations the data had been observed after the event already

occurred? For example, trying to predict time to heart attack given some

ECG measurements, but for some patients, the measurements were obtained

after the patient already had been in the hospital after the attack. So

in one sense the time to event is actually negative and censored at

zero. Probably a better approach would be some sort of

discrete/continuous mixture model where with a certain probability, the

event has already occurred and given that it hasn't, a standard survival

model takes effect.

Al Feiveson

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**References**:**st: partially retrospective survival analysis***From:*"Feiveson, Alan H. (JSC-SK311)" <alan.h.feiveson@nasa.gov>

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