Thanks Svend.
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?
Many Thanks
Ali
Quoting Svend Juul < [email protected]>:
Ali wrote:
I am using a very large dataset (N=1.3m) to analyse the effect
of a categorical variable on gestational age (continuous variable).
I used linear regression (regress) but as you would probably expect
the residuals are not normally distributed. I used qreg but the
results do not look right.
Could anyone tell me what is the best analysis for continuous
gestational please.
--------------------------------------------------------------
Your outcome variable is time to an event; this calls for survival
analysis, e.g. -stcox-. There are requirements for Cox regression
too, so be careful.
Hope this helps
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: [email protected]
_________________________________________________________
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