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Recent Comments
Interesting discussion but not very helpful for cancer models in mice.
One general aspect for cancer research needs to be considered: Cancer is a heterogeneous disease and we have learned not to translate results from one tumor model in mice to the general cancer patient population.
In cancer research now it is rather standard to test compounds in a panel of models – similar to clinical trails phase II. Single mouse study design is frequently used for this and randomization not longer an important topic. Giving this trial design, it is not longer necessary to compare groups with statistical tests. It is rather important to evaluate treatment effects on tumors (response) critical by using adopted RECIST criteria.
Blinding would definitely helpful, but frequently not possible because treatments are different.
Please do not have mice with different treatments in one cage. Compounds are excreted by mice with urine and feces and can be taken up by other mice. I have seen studies failing because the other compound has been detected in the plasma from one mouse in the same cage.
The unit of analysis is definitely relevant, but in a total different meaning. The statistician has rather to calculate how many different tumor models need to be used to allow comparison of response rates with SoC in clinical use.
I think this paper makes a number of essential points about pre-clinical trial design, backed up by two analytical studies of recent publications. The necessity of using randomization, blinding, and the correct unit of analysis are well explained and supported, including for mouse cancer models. I believe that most MSc and PhD programs require students to take a course in statistics. Perhaps a course specifically on study design should also be required. In the paper, I think more emphasis might have been put on the need to start with a clearly stated Null Hypothesis, and situations when one might not be needed. Secondly, some discussion of when normal distribution of data cannot be assumed and when and where non-parametric statistics might be useful.
Overall a useful and timely contribution.