If I have an option to choose between two diagnostic tests with the following qualities:
A: conf.level of 95% and error of +/- 3.5%
B: conf.level of 99% and error of +/-5%
Both have more/less the same sensitivity and specificity.
Which of the upper two should I use?
If a high sensitivity is crucial, take the test with the higher sensitivity. If specificity is crucial, take the test with the higher specificity.
confidence interval of 99% represents close to 3 standard deviations, while 95% is 2. An 99% CI with 5% error is about 3.3% at 95% CI, which is within rounding error (all figures provided are severely rounded) of the first test.
I am assuming that the figures quoted are for the test as used (including the fixed number of sub-samples or replicates used per sample).
If CIs are based on different numbers of replicates then it may be useful to work backwards to find the precision for comparing systems - one system may have less precise measurements but compensate by taking more measurements to achieve the same level of confidence in the result.
If as well as confidence intervals the sensitivity and specificity are similar then the choice is likely to come down to practical considerations and require a benefit/risk analysis. Is there a different in capital cost, running cost, throughput, scalability etc.
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