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Calculating the sample size required for developing a clinical prediction model

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Re: Calculating the sample size required for developing a clinical prediction model

#21
post #19

Earlier quoted context omitted.

> Not sure if any of the test-driven development people have thought about this, but the same principle also applies there: if you debug and fix the code until it passes the tests, you can overfit the tests. It's no longer a good measure for code quality, once it has been explicitly optimized. You'd need new, previously unseen test cases. It would be an obvious mistake in machine learning to simply add failed test ex…

Yes but then the tests would formally describe the requirements - i.e.they would be a de-facto implementation. Congratulations, you just coded an (indirect) solution to your problem (a solution that you postulate is correct/bug-free)

Are you saying this like it's a bad thing?

Tests describing requirements acts as documentation, validation, and regression prevention with future refactors.

Re: Calculating the sample size required for developing a clinical prediction model

#22
post #21
post #19

Earlier quoted context omitted.

Yes but then the tests would formally describe the requirements - i.e.they would be a de-facto implementation. Congratulations, you just coded an (indirect) solution to your problem (a solution that you postulate is correct/bug-free)

Are you saying this like it's a bad thing? Tests describing requirements acts as documentation, validation, and regression prevention with future refactors.

My beef is with the assumption that tests can fully encode the requirements - to the point where any bug would trigger a test failure. Having such a test suite is no simpler than having a perfect implementation of the requirements - i.e. it's probably only feasible, at all, in the simplest/ "didactic" cases.

Having a perfect implementation is not bad, of course. It's just not realistic.

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