Can a mixed model be used for missing data?

Can a mixed model be used for missing data?

The mixed model will retain the 70 people who have data for only one time point. It will use the 48 people with pretest-only data along with the 90 people with full data to estimate the pretest mean.

How does listwise deletion affect a mixed model?

In some ways listwise deletion appealed most, but it would mean the loss of too much data. One of the nice things about mixed models is that we can use all of the data we have. If a score is missing, it is just missing. It has no effect on other scores from that same patient.

How are missing data related to sample size?

The missing data was pretty random–some participants missed time 1, others, time 4, etc. Only 6 people out of 150 had full data. Listwise deletion created a nightmare, leaving only 6 people in the data set. Each person contributed data to 4 means, so each mean had a pretty reasonable sample size.

Which is a good feature of a mixed model?

In some ways listwise deletion appealed most, but it would mean the loss of too much data. One of the nice things about mixed models is that we can use all of the data we have.

How is the observed coefficient tested in a mixed model?

The observed coefficient is tested against the generated empirical distribution. Since the distributions of coefficients are only approximately asymptotical, two or more of the above are generally done to confirm results of tests that are inconclusive.

Which is better, a linear mixed model or a mixed model?

This causes problems with both power and bias, but bias is the bigger issue. Another alternative is to use a Linear Mixed Model, which will use the full data set. This is an advantage, but it’s not as big of an advantage in this design as in other studies.

How is a test of zero valid in a mixed model?

Since the variance must be greater than or equal to zero, a test of zero is on the border of the parameter space. Tests of parameters are valid only on the interior of their space and not on the border. The correlation structure within the data complicates using bootstrap procedures to test these statistics which do not have known distributions.

Are there any problems with generalized linear mixed model?

My first problem is that I have missing values (because some of the patients died, were lost to follow-up…etc). And my second one is that this biomarker has a non-normally distribution. Is it correct to use a generalized linear mixed model to analyse the data?

How does repeated measures ANOVA deal with missing data?

Another 48 completed only the pretest and 22 completed only the post-test. Repeated Measures ANOVA will deal with the missing data through listwise deletion. That means keeping only the 90 people with complete data.