[R-sig-ME] much different results for random effect vs simple lm.

[David Duffy]

On Mon, 20 Jun 2011, Brent Pedersen wrote:

> Hi, I have a model like this:
>
>    # for many y values/probes
>    formula = y ~ concordant + age.proband + age.sibling + sex.proband
> + sex.sibling
>
> I run this model and get p-values with the formula:
>
>
>    model = lm(formula, data=df2)
>    s = summary(model)
>    p.cordant = s$coefficients["concordantT", "Pr(>|t|)"]
>
>
> But, an proband can have multiple siblings, so I want to account for
> family structure:
> So, I use:
>
>    library(lme4a)
>    # for many y values.
>    model = lmer(y ~ concordant + age.proband + age.other +
> sex.proband + sex.proband + sex.other + (1| family_id.proband),
> data=df)
>
>    degrees.of.freedom = length(unique(df$family_id.proband)) - 1
>
> Everything else between the 2 runs is the same. For the simple case, I
> have unique 80 pairs (since I only use each proband once),
> and for the latter, I have 98 pairs. I'm doing this test for millions
> of probes and looking for regions of where the concordant
> parameter is significant, I find much different regions between the 2
> models--very little overlap.
>
> Is this to be expected? Intuitively, I'd figure that using
> the random effect via lme4a would just give more power. Are my p-value
> calculations correct?

You need to look at just a few probes in detail.  Given you have such a 
small sample size (and how many concordant pairs?), you might expect a bit 
of shifting about.  The other model you should check (in a subset) is your 
first model fitted to all 98 pairs, using your conservative degrees of 
freedom from model 2 (this would be pretty similar to a GEE, AIUI).

Cheers, David Duffy.

-- 
| David Duffy (MBBS PhD)                                         ,-_|\
| email: davidD at qimr.edu.au  ph: INT+61+7+3362-0217 fax: -0101  /     *
| Epidemiology Unit, Queensland Institute of Medical Research   \_,-._/
| 300 Herston Rd, Brisbane, Queensland 4029, Australia  GPG 4D0B994A v