[R] Experimental Design

[Spencer Graves]

	  It looks to me like you have two blocking variables with 1 control 
group and 4 treatment groups, with the control replicated between the 
"master blocking variable" = "experiment 1 vs. 2".  (The minor blocking 
variable occurs at 6 levels unless "Blk1" in Experiment 1 somehow 
relates to "Blk1" in Experiment 2.)  People who deal with this routinely 
could probably provide R code plus citations to the literature where 
this kind of analysis is discussed.  I would write an appropriate model 
and do the analysis.

	  And yes, I would want to confirm any encouraging results in a future 
experiment.

hth.  spencer graves

Isaac Neuhaus wrote:
> I don't know if this is the best place to post this question but I will 
> try anyway. I have two experiements for which I use one-way 
> matched-randomized ANOVA for the analysis and I would like to compare 
> different treatments in the two experiments. The only common group in 
> the two experiments are the controls. Is there any  ANOVA design that 
> allows me  to  make this comparison taking into consideration the 
> confounding effect? Any help would be greatly appreciated.
> 
> Isaac
> 
> A representation of the experiments follows:
> 
> Experiment 1
>           Control1     Treat1      Treat2
> Blk1          s1          s2          s3
> Blk2          s4          s5          s6
> Blk3          s7          s8          s9
> 
> 
> Experiment 2
>           Control2     Treat3      Treat4
> Blk1          s1a          s2a          s3a
> Blk2          s4a          s5a          s6a
> Blk3          s7a          s8a          s9a
> 
> Control1 and Control2 I are the same control cell line. I would like to 
> compare Treat1 to Treat3 and Treat 4 and also I would like to compare 
> Treat2 to Treat3 and Treat4. The fact that those experiments are done in 
> two different blocks will confound the interpretation. Can I use the 
> common control group to build a model? Should I include one of the 
> treatments in future experiments to test my model?
> 
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