Re: st: analysis of continuous gestational age

[Ali Khashan <Ali.Khashan@postgrad.manchester.ac.uk>]

What about the idea that we can use linear regression even if the 
residuals are
not normally distributed if we have a large dataset? Is there any basis for
this?

Many Thanks
Ali

Quoting Svend Juul <SJ@SOCI.AU.DK>:


> Ali wrote (after a suggestion to use -stcox- to study determinants of
> gestational length (duration of pregnancies)):
>
> That is true, specially with the fact that the exposure is during 
> pregnancy so
> it depends on gestational length. However, to avoid this I am looking at
> exposures during the first 23 weeks' gestation were all women have the same
> opportunity to  be exposed. when I want to see the effect of exposure on
> prematurity (binary variable I used binomial regression instead of cox
> regression by restricting the exposure to 23 weeks). In addition I 
> want to see
> the effect of exposure on gestational age (continuous). This calls for linear
> regression-as I understand- but as I mentioned the residuals distribution is
> not normal. In these cases a non-parametric analysis is required. I used qreg
> but the results do not look right.
>
> Since the dataset is very large does it matter if the residuals normal
> distribution assumption was violated in linear regression?
> --------------------------------------------------------------------
>
> There is no doubt that the residuals after a linear regression are far from
> a normal distribution. Also, there is hardly any meaningful transformation of
> gestational age that could make this happen. So, relying on linear regression
> is a problem.
>
> -stci- can be used for simpler descriptions and comparisons, using e.g. the
> median and other percentiles in a sort of non-parametric approach.
>
> Good luck,
> Svend
>
> ________________________________________________________
>
> Svend Juul
> Institut for Folkesundhed, Afdeling for Epidemiologi
> (Institute of Public Health, Department of Epidemiology)
> Vennelyst Boulevard 6
> DK-8000 Aarhus C,  Denmark
> Phone, work:  +45 8942 6090
> Phone, home:  +45 8693 7796
> Fax:          +45 8613 1580
> E-mail:       sj@soci.au.dk
> _________________________________________________________
>
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