Q assembling sample set from existing smaller sets

[Cosine]

Hi:

How do we properly assemble a dataset for testing the performance of a new method of screening by a set of small datasets?

To have enough power, we need to have a dataset that is large enough. This might not be possible in practice. Some papers resolve this issue by combining a set of similar but small datasets.

The critical problem here is: what are the rules to make sure the dataset combined is appropriate? Are there books that illustrate this type of problem?

[David Duffy]

One approach
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2790297/

This is not just statistical, but domain specific (excluding one dataset
is based on common-sensical tests of study quality, but also a
knowledge of the underlying science of the test). Check out
http://prisma-statement.org/

[Rich Ulrich]

Thanks for the reference.

"Welcome to the Preferred Reporting Items for Systematic Reviews
and Meta-Analyses (PRISMA) website!"

I haven't spent much time reading, but that looks like an
excellent resource.

--
Rich Ulrich

[David Duffy]

Rich Ulrich <rich....@comcast.net> wrote:
>>http://prisma-statement.org/

> "Welcome to the Preferred Reporting Items for Systematic Reviews
> and Meta-Analyses (PRISMA) website!"

The Cochrane Consortium is the other such go-to resource. I just noticed they
have a textbook on how to do "Cochrane Reviews of Diagnostic Test Accuracy",

https://methods.cochrane.org/sdt/handbook-dta-reviews

and software notes,

https://methods.cochrane.org/sdt/software-meta-analysis-dta-studies