much different results for random effect vs simple lm.
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