We have measured the range of movement of a hip joint (measured in degrees) before and after surgery. And we have plenty of variables that could explain the differences in the results. Some of them are factors (sex, type of prosthesis, bearing surfaces...) and some are continous (age, weight, height...). We have more than 40 variables that could have an influence on the results.
At first I thought about testing variable after variable to see their influence in the range of movement to create a regression model. But then I though that, by doing that, I would lost the main advantage of the study: we have paired measurements before and after surgery. So I decided to go for a repeated measures GLM... but I am not sure about how to do that. I understand that GLM allows you to introduce a factor and several continous variables... but in our case we do not have a grouping variable. We have more than 20 factors that could have an influence and more than 20 continuous variables that could affect too. If I am not in a mistake, GLM is more to understand if the variables interact, but here we would like to decide which ones do influence the results and which others do not. It is not really about finding interaction effects.
Any ideas?
Thanks in advance.
At first I thought about testing variable after variable to see their influence in the range of movement to create a regression model. But then I though that, by doing that, I would lost the main advantage of the study: we have paired measurements before and after surgery. So I decided to go for a repeated measures GLM... but I am not sure about how to do that. I understand that GLM allows you to introduce a factor and several continous variables... but in our case we do not have a grouping variable. We have more than 20 factors that could have an influence and more than 20 continuous variables that could affect too. If I am not in a mistake, GLM is more to understand if the variables interact, but here we would like to decide which ones do influence the results and which others do not. It is not really about finding interaction effects.
Any ideas?
Thanks in advance.