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Can somebody explain the logic behind "SpIn SnOut" ?

Let Sp = specificity, Sn = sensitivity, TP/TN = true positives/negatives, FP/FN = false positives/negatives, and PPV/NPV = positive/negative predictive value. SpIn = "Specific test when Positive test rules IN the disease", and SnOut = "Sensitive test when Negative test rules OUT the disease").

Let's take SpIn for a spin in the following examples.

Firstly, the phrase regarding specificity, "when Positive test rules IN the disease" implies that a positive test has a high probability to be true. Putting these words into formula leads to a high (TP/(TP+FP)) = high PPV. The phrase "when positive test rules IN" describes high PPV rather than high Sp.

Secondly, the formula for specificity is Sp = (TN/(TN+FP)) – it describes the probability of a healthy individual to be identified as healthy. So, while, the formula for specificity describes how good a test is at categorising healthy people (aka people who ideally should get a negative test result), the SpIn mnemonic refers to positive test results. This juxtaposition is confusing (formula referring to healthy people vs mnemonic referring to positive test results).

Thirdly, the formula for specificity Sp = (TN/(TN+FP)) doesn't directly address the probability of a positive test to be true (remember, the mnemonic states "specific is when positive test rules in disease), albeit it implies the existence of a low number of false positives (Sp = 1 if FP = 0), which itself implies a high PPV (PPV = 1 if FP = 0). However, for any value of FP ≠ 0, PPV can theoretically equal anything, so it doesn't make sense to say "specific test rules in the disease".

So, why does the SpIn SnOut mnemonic exist, and is it actually true?

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    $\begingroup$ Are you asking to explain sensitivity and specificity? It is not clear for me what is your question. $\endgroup$
    – Tim ♦
    May 30, 2021 at 20:01
  • $\begingroup$ I'm asking why specificity is a feature of tests which can rule in (confirm) disease (in medicine). $\endgroup$
    – Tib
    May 31, 2021 at 5:22
  • $\begingroup$ Not sure what you mean. It is just one of the metrics to assess how good is a diagnostic method or an predictive algorithm etc. $\endgroup$
    – Tim ♦
    May 31, 2021 at 8:10
  • $\begingroup$ An alternative way to ask my question is - why is specificity the most important metric for a diagnostic test, and why is sensitivity the most important metric for a screening test ? It's a very basic question about the very basics of specificity and sensitivity. $\endgroup$
    – Tib
    Jun 1, 2021 at 1:48

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The SPIN rule assumes that your false positives are super low and the the prevalence is not skewed and the proportions with all the numbers are similar. In this instance when you have a high specificity (and assuming essentially a zero false positive rate) AND you have a positive result, then people assume that this positive result has ruled in a disease.

In my opinion, the assumptions are too great and the rule is not the best system to use. I would suggest knowing the a priori risk and then using the test as a conditional factor to give you a better posterior risk.

BTW.. you can use the same logic for SNOUT.

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