[R-sig-ME] much different results for random effect vs simple lm.
davidD at qimr.edu.au
Tue Jun 21 07:34:40 CEST 2011
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$coefficients["concordantT", "Pr(>|t|)"]
> But, an proband can have multiple siblings, so I want to account for
> family structure:
> So, I use:
> # for many y values.
> model = lmer(y ~ concordant + age.proband + age.other +
> sex.proband + sex.proband + sex.other + (1| family_id.proband),
> degrees.of.freedom = length(unique(df$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
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