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From |
"Feiveson, Alan H. (JSC-SK311)" <alan.h.feiveson@nasa.gov> |

To |
"statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: R: Problem with Left Truncation |

Date |
Fri, 23 Oct 2009 08:13:34 -0500 |

I think the only safe conclusion that can be made by analyzing this data alone is that the survival analysis is valid conditional on the event (e.g. a death) occurring. In other words the distribution that is estimated is the time to the event given that the event has occurred. In most cases this would not be the same as the unconditional distribution of time to the event. Al Feiveson -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Antoine Terracol Sent: Friday, October 23, 2009 5:06 AM To: statalist@hsphsun2.harvard.edu Subject: Re: st: R: Problem with Left Truncation Hi, Carlo Lazzaro wrote: > Dear Elaine, > Please find beneath the following point-to-point comments about your query: > > <We are doing survival analysis, but unlike other dataset, our dataset > only includes observations that have failed.> > > I would not be concerned about all failure=1; how long patient takes to > failure (failure time (tn)- risk onset (t0)) it's the relevant issue. I haven't done the math, but my intuition is the following. If Elaine's dataset can be considered as a random sample from the population, then she can use -stset- and proceed as usual. I can think of one case where it would not be true: Consider the case where individuals cannot be at risk before a certain calendar date t0 (say, because she is studying spells in a social program that did not exist before). If Elaine's observation window is relatively (compared to the average real duration in the state under study) short, and begins relatively (again, compared to the real durations) close to t0, then Elaine's sample will only contain spells short enough to have failed during the observation window. In this cas, the coefficients will be biased (although I'm not quite sure if there is a way to handle that with Stata). On the other hand, if the spells could have started at any given date before the observation window (or long enough before the start of the observation window, relatively to the real durations), then I think her sample will be random and can be analysed as usual. HTH, Antoine -- Ce message a ete verifie par MailScanner pour des virus ou des polluriels et rien de suspect n'a ete trouve. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: R: Problem with Left Truncation***From:*"Carlo Lazzaro" <carlo.lazzaro@tin.it>

**Re: st: R: Problem with Left Truncation***From:*Antoine Terracol <Antoine.Terracol@univ-paris1.fr>

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