Hello, I have a big study to analyse and I am unsure of which technique to use.
I have a group of patients suffering from disease 1. This group further divides into 4 sub groups A, B, C and D On the other hand I have another group of patients suffering from disease 2. This group divides into 5 sub groups E, F, G, H and I. The aim of my analysis is to check whether there are proteins which are significantly changed between the two different types of diseases. For the analysis I would pool all patients suffering from disease 1 together to obtain a single group. I would do the same for disease 2. Then I would use either a fixed-effects or random-effects ANOVA to identify significantly changed analytes. When I started to read about the different types of ANOVA's I came across the q-statistics which is used to account for the heterogeneity within a group. So it checks whether the effect size between sub-group A, B, C and D is approx. the same. Does this analysis make sense or how would you analyse this kind of data? Is there an R package which can easily deal with such situations? Cheers, syrvn -- View this message in context: http://r.789695.n4.nabble.com/fixed-effect-or-random-effect-ANOVA-model-tp4099308p4099308.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
