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Re: st: maximum likelihood inference for left censored exposure variable


From   Steve Samuels <sjsamuels@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: maximum likelihood inference for left censored exposure variable
Date   Wed, 23 Jun 2010 15:48:26 -0500

I can find no reference to a procedure for handling limits of
detection in Stata (the correct spelling, by the way;  see section 8.2
of the list FAQ).  If you cannot write your own ML procedure, perhaps
one of authors of the two papers has code in Stata, R, or SAS that you
can use.  I suggest that you contact them.

Steve


On Wed, Jun 23, 2010 at 12:05 PM, Floud, Sarah <s.floud07@imperial.ac.uk> wrote:
> Using maximum likelihood inference for left-censored exposure variables:
>
> For an epidemiological study, I have exposure data (noise exposure) which is left-censored, with about 10% of cases set to a limit of detection (LOD). I am currently using logistic regression to estimate the effect of the noise exposure on the health outcomes. But I would like to be able to infer values below the LOD for the exposure variable.
>
> A method proposed by:
> Cole et al 2009 ("Estimating the odds ratio when exposure has a limit of detection" International Journal of Epidemiology 2009 1-7 doi: 10.1093/ije/dyp269) and by:
> Lynn, H.S. 2001 ("Maximum likelihood inference for left-censored HIV RNA data" Statistics in Medicine 2001; 20:33-45)
> is to use maximum likelihood inference for the values set at the LOD instead of using other methods such as setting all at the LOD or LOD/2 etc.
>
> I would like to know if there is a way to do this in STATA without having to program it myself, as I think that would be beyond me. I currently have STATA IC 10.
>
> Thank you for your consideration.
>
> Sarah Floud
>
>
>
>
> Sarah Floud
> Faculty of Medicine
> Imperial College London
> St Mary's Campus, Norfolk Place
> LONDON W2 1PG
>
>
>
>
>
> *
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>



-- 
Steven Samuels
sjsamuels@gmail.com
18 Cantine's Island
Saugerties NY 12477
USA
Voice: 845-246-0774
Fax:    206-202-4783

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