Skip to main content
Cross Validated

Return to Question

added 19 characters in body
Source Link
Francis
Francis
  • 609
  • 1
  • 7
  • 15

I have a data set with repeated measurements on subjects. The total sample size is $n=118$ and the number of clusters (i.e. subjects) is $m=49$. The smallest cluster is of size 2 and the largest cluster is of size 4. In fact, about 60% of the clusters are of size 2, i.e. only two observations per subject. The outcome variable is continuous and there are 5 covariates of interest.

My plan is to fit a linear mixed model (LMM) with a random subject effect, i.e. a random intercept term. If the normality assumption of the residuals should not be satisfied and transformations do not solve the issue, I would then use GEE with identity link (i.e. a marginal model) to model the data since it does not require the normality assumption.

However, before embarking on this adventure, I had some concerns about the asymptotic propoerties of the LMMs and GEEs. I know that for GEE the asymptotic behaviours depends on the number of clusters $m$ (e.g. Li and McKeague, Statistica Sinica, 2013).

Are there any guidelines/recommendations of the number of clusters $m$, the number of observations $n$ and the minimum/maximum cluster size for the two methods?

I have a data set with repeated measurements on subjects. The total sample size is $n=118$ and the number of clusters (i.e. subjects) is $m=49$. The smallest cluster is of size 2 and the largest cluster is of size 4. In fact, about 60% of the clusters are of size 2, i.e. only two observations per subject. The outcome variable is continuous and there are 5 covariates of interest.

My plan is to fit a linear mixed model (LMM) with a random subject effect, i.e. a random intercept term. If the normality assumption of the residuals should not be satisfied and transformations do not solve the issue, I would then use GEE (i.e. a marginal model) to model the data since it does not require the normality assumption.

However, before embarking on this adventure, I had some concerns about the asymptotic propoerties of the LMMs and GEEs. I know that for GEE the asymptotic behaviours depends on the number of clusters $m$ (e.g. Li and McKeague, Statistica Sinica, 2013).

Are there any guidelines/recommendations of the number of clusters $m$, the number of observations $n$ and the minimum/maximum cluster size for the two methods?

I have a data set with repeated measurements on subjects. The total sample size is $n=118$ and the number of clusters (i.e. subjects) is $m=49$. The smallest cluster is of size 2 and the largest cluster is of size 4. In fact, about 60% of the clusters are of size 2, i.e. only two observations per subject. The outcome variable is continuous and there are 5 covariates of interest.

My plan is to fit a linear mixed model (LMM) with a random subject effect, i.e. a random intercept term. If the normality assumption of the residuals should not be satisfied and transformations do not solve the issue, I would then use GEE with identity link (i.e. a marginal model) to model the data since it does not require the normality assumption.

However, before embarking on this adventure, I had some concerns about the asymptotic propoerties of the LMMs and GEEs. I know that for GEE the asymptotic behaviours depends on the number of clusters $m$ (e.g. Li and McKeague, Statistica Sinica, 2013).

Are there any guidelines/recommendations of the number of clusters $m$, the number of observations $n$ and the minimum/maximum cluster size for the two methods?

Source Link
Francis
Francis
  • 609
  • 1
  • 7
  • 15

How many clusters for linear mixed models and GEE?

I have a data set with repeated measurements on subjects. The total sample size is $n=118$ and the number of clusters (i.e. subjects) is $m=49$. The smallest cluster is of size 2 and the largest cluster is of size 4. In fact, about 60% of the clusters are of size 2, i.e. only two observations per subject. The outcome variable is continuous and there are 5 covariates of interest.

My plan is to fit a linear mixed model (LMM) with a random subject effect, i.e. a random intercept term. If the normality assumption of the residuals should not be satisfied and transformations do not solve the issue, I would then use GEE (i.e. a marginal model) to model the data since it does not require the normality assumption.

However, before embarking on this adventure, I had some concerns about the asymptotic propoerties of the LMMs and GEEs. I know that for GEE the asymptotic behaviours depends on the number of clusters $m$ (e.g. Li and McKeague, Statistica Sinica, 2013).

Are there any guidelines/recommendations of the number of clusters $m$, the number of observations $n$ and the minimum/maximum cluster size for the two methods?