If you enter 0 in var. x_2, you are saying that you observed some age at onset of menstruation - mainly, 0 years old. The analysis would then proceed on presumption that this is real data. It ain't, and I don't think you observed any menstruation in neonates. :)
Go with Max' method. If the subject is male, onset of menstruation was never observed, or not observed yet. If you include them at all, the age of onset should be older than the subject is today. :)
Cheers,
Jay
On Jan 20, 2011, at 12:24:52 AM, Max Blumberg wrote:
> Lars,
>
> If male menstruation onset is treated like "missing data" and replacement
> estimates are made, it will lead to inflated statistical power. My sense is
> that in this instance where data doesn't apply, male and female cases need
> to be analysed separately.
>
> Max
>
>
> -----Original Message-----
> From: A UK-based worldwide e-mail broadcast system mailing list
> [mailto:[log in to unmask]] On Behalf Of Lars Chi
> Sent: 20 January 2011 01:46
> To: [log in to unmask]
> Subject: Query: Dealing with "conditional" input variables in Regression‏
>
> Hi,
>
> I'd appreciate your help on the following. As a simple example, suppose I
> have two independent variables x_1 = {Female, Male} and x_2 = {Age at which
> menstruation began}. Obviously, X_2 will only be populated if X_1=Female.
> Suppose I want to estimate the impact of x_1 and x_2 on some dependent
> variable y. How should I code these variables in the regression analysis?
>
> My methodology is the following: replace "missing" observations in x_2
> (i.e., those corresponding to males) with zeros, and set x_1=1 if x_2 is
> zero and x_1=0 otherwise. Then fit y = beta_1*x_1 + beta_2*x_2. Does this
> sound right?
> Thanks in advance for your help,
>
> Lars.
>
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Jay Warner
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