www.cmm.bristol.ac.uk/forum

Several aspects to this

1) you can use coverage intervals in customised predictions to give between higher level unit overall differences on the probability scale but this will not give specific hospitals

2 You can use residuals at the higher level to get estimates of the specific hospital ...

I am a bit confused by your questions. and I would not regard 3 way cross - level interactions as simple!

The variances are stored iand there are many ways of getting at them.

All MlwiN our resources are set out here

http://www.bristol.ac.uk/cmm/software/mlwin/mlwin-resources.html

and I suggest ...

1 Use a Wald test of whether a parameter or a set of parameters are zero – a univariate Wald and a multivariate Wald (for multiple simultaneous testing) are available under Interval and Tests; in the main you need to be careful with random effects estimates but the fixed ones are generally fine;

2 ...

One method of estimation in MLWIN is a likelihood based one and there are 2 versions. Iterative Generalised Least Squares estimates the random part under the assumption that fixed part estimates are known. Restrictive IGLS estimates the random part variances as if the fixed part means have been ...

In a discrete outcome model there is no level 1 variance to be estimated as it really depends on the predicted underlying response that is the fixed part.

In fitting a separate model model to each country you are doing the equivalent of a fixed effect analysis (see Bell and Jones below) in which ...

If you want to see the Customised predictions facility in action see they later chapters of

Jones, K and Subramanian, V S (2014) Developing multilevel models for analysing contextuality, heterogeneity and change using MLwiN, Volume 1 , University of Bristol.

https://www.researchgate.net ...

I will try and answer but as a lot of this is substantive rather than method /software my answers may be limited.

First - customised predictions - these make predictions for the response for chosen specific values of the predictor. This works best when you have built a model using the Add term ...

You can test a set of fixed effects simultaneously by a simultaneous Wald test that produces an overall chi-square test. It is illustrated in the Chapter on significant testing in MLwin. See Chapter 6 of

https://www.researchgate.net/publication/260771330_Developing_multilevel_models_for_analysing ...

MLwin does not fit a GEE model; it fits a random effects model, that is it estimates an explicit between-group variance You can fit a two level model with people at level 1 nested in cities at level 2 and have a random intercepts model for between city variation. You can then put in a dummy that ...

There are a number of ways that you can evaluate ‘significance’ in MLwin.

1 if the model does not have a discrete response as is estimated by maximum likelihood or restricted maximum likelihood you can do a Likelihood Ratio Test – essentially calculate the difference in the likelihood for the two ...