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Sburel
Level III

Response Screening vs Dunnett for large datasets

Hello,

 

I'm dealing with very large datasets in which we have conducted many different studies (study_id) where we compare various 'test_article' vs a  control (ie PBS). This is done in multiple replicates (subject_id). For each of subject_id, in each of those studies, we are making measurement for up 50000 different type of measurement (Veggies).  The result of each measurement is reported as a log2 (of the measurement of a given subject normalized by the mean of the control group from the same study, but the nature of the measurement is not that important, I think).

 

On a small dataset, I would run a One-way anova (one per study) for each veggy to compare the effect of treatment (test_article) and follow that up with a Dunnett's. On a large data, it seems to become intractable for 2  reason (multiple measurement - so FDR becomes a requisite) and unable (ie don't know how) to only report the p-value and bypassing the graphical phase( which is not practical for that many tests).

 

The 'response screening' seems to be a slightly better alternative but is missing the option to define a reference control group as I am not interested in knowing if test-article A is better than B but only if any of the test_article are different from the control.

Finally, I would like to be able code the process for reproducibility.

 

I've attached a toy example of the dataset.

Any suggestions would be very much appreciated.

 

Sebastien

2 REPLIES 2
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Thierry_S
Level VI

Re: Response Screening vs Dunnett for large datasets

Hi Sebastien,
Your example seems to call for a repeated measure mixed linear model. There is a really good Add-In (https://community.jmp.com/t5/JMP-Add-Ins/Full-Factorial-Repeated-Measures-ANOVA-Add-In/ta-p/23904) that should let you design the right combination of factors taking into account that you are evaluating the same subject across different conditions.
Of note: the Within Subject Factors are those that span across Subjects such as Sampling Time. In contrast, the Between Subject Factors are those that are only found for a specific Subject such as Treatment or Experiment.

Best regards,

TS
Thierry R. Sornasse
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Sburel
Level III

Re: Response Screening vs Dunnett for large datasets

Hi Thierry,

Thanks a lot for the tip. It could be helpful. I'll give it a try

Best

S
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