R statistics: Adjusted Chi-squared test for clustered binary / categorical data

I'm looking for some assistance in statistical analysis with R, but also some general stats advice.

I am analysing cardiac phenotype data by comparing 2 groups. The 2 groups are unmatched individuals, but within each group, they are clustered in family subgroups (of between 1 and ~6).

I want to report the difference in prevalence of a specific ECG appearance (binary - i.e. either present or absent in each individual) between the 2 groups.

For example:

Quote:

Group 1 consists of 157 individuals comprised of 41 family clusters

Group 2 consists of 463 individuals comprised of 163 family clusters

Prevalence of x in Group 1 = 22.9%

Prevalence of x in Group 2 = 24.6%

Group 1 are cases and Group 2 controls (i.e. not randomized)

What test is most appropriate in this circumstance, and which package in R provides the easiest way to account for the clustering of relatives within families?

Having looked around, I have found:

- Ratio estimate chi-square test
- Generalized estimating equation

But I have no experience of either of these techniques, and can't find any examples of their use in R.

Can anyone help?

(PS: This is not homework)

Re: R statistics: Adjusted Chi-squared test for clustered binary / categorical data

Have done some digging / research:

I believe that the Donner (1989) or Rao & Scott (1992) modifications of chi-squared may be appropriate.

I have found package(aod) which includes functions donner() and raoscott()

I would certainly appreciate a second opinion on which to use, and what options are appropriate. I'm leaning to Donner (as below).

Currently:

Code:

`donner(cbind(y,n-y) ~ group, data=matrix)`

raoscott(cbind(y,n-y) ~ group, data=matrix)