Mixed (type III) model ANOVA in R and GraphPad Prism Consider the following setup: two mouse strains ("KO" and "WT") have been compared in three independent experiments ("E1", "E2" and "E3"). In each experiment, there were two groups corresponding to the two mouse strains compared, and each group consisted of four different mice. There were all in all 8 mice per experiment, and three experiments (total of 4 x 2 x 3 = 24 mice).
The question is, of course, whether the strains differ.
In R, I would do this as follows, given the variables strain and experiment:
a <- data.frame(
  response= c(100, 110, 120, 130, 200, 210, 220, 230, 105, 115, 125, 135, 205, 
              215, 225, 235, 105, 115, 125, 135, 215, 225, 235, 245),
  experiment= rep( c( "E1", "E2", "E3" ), each= 8 ),
  strain= rep( rep( c( "WT", "KO" ), each= 4 ), 3 ) )

myaov <- aov( response ~ strain + Error( experiment ), data= a ) 
summary( myaov )

However, I have been asked this question by persons who work on a regular basis with GraphPad Prism. It does feature "repeated measures ANOVA" which is (in the help file) said to be equivalent to random block design, but I find the explanations somewhat confusing:

"Repeated measures" vs. "randomized block" experiments
The term repeated measures is appropriate when you made repeated measurements from each subject.
Some experiments involve matching but not repeated measurements. The term randomized-block describes these kinds of experiments. For example, imagine that the three rows were three different cell lines. All the Y1 data came from one experiment, and all the Y2 data came from another experiment performed a month later. The value at row 1, column A, Y1 (23) and the value at row 1, column B, Y1 (28) came from the same experiment (same cell passage, same reagents). The matching is by row.
Randomized block data are analyzed identically to repeated-measures data. Prism always uses the term repeated measures, so you should choose repeated measures analyses when your experiment follows a randomized block design.

My question: how can I analyse these data using GraphPad Prism? Or, alternatively: does the quoted description fit my case?
 A: Repeated measures anova is an old technique that assumes the correlation between any two time points are the same (compound symmetric covariance structure).  It also requires a correction to be applied to get correct P-values that account for non-independence of repeated observations within subject.  Other approaches work better such as the full likelihood methods of mixed effect models and generalized least squares.  R provides many approaches to modeling repeated/longitudinal data and to using realistic correlation structures such as exponential declines over longer time spans.  Full likelihood methods are also more robust to missing values that are not missing completely at random.  My course notes at http://biostat.mc.vanderbilt.edu/rms have a case study worked out using generalized least squares.
A: From your description, I don't think you've really done an experiment where a repeated measures analysis (or the alternatives Frank mentioned) are appropriate. 
I'd be inclined to analyze each of your three experiments separately, with an unpaired t test or nonparametric Mann-Whitney test. Then you can report the results of all three experiments (confidence intervals and perhaps p values) and look for consistency. I suspect that reporting three consistent experiments will be more informative than trying to combine all the analysis into one pooled result. 
If you wanted to be fancier, you could put all the data into a two-way ANOVA, where genotype is one factor, and experiment is the other. Showing that the interaction P value is high would demonstrate that the experiments are consistent (the difference between genotypes is consistent for all three experiments).  But, from your description, there would be no repeated measures (because no mouse was measured more than once). 
Write me at support@graphpad.com if you need help implementing either approach in GraphPad Prism. 
