What can you do when a ‘gold standard’ isn’t actually that good at diagnosing a condition? It can be terribly problematic in interpreting sensitivity and specificity – for example comparing polymerase chain reaction diagnosis of microbiological infection with culture results. The ‘false positive’ may actually reflect real, and otherwise missed, diagnosis, and the ‘false negatives’ a failure of the old standard to identify someone who isn’t really unwell.<\/p>\n
One thing to hold onto is that, at their core, most ‘diagnoses’ are a short-hand for a similar group of pathologies leading to a similar set of outcomes. What is ‘bronchiolitis’? What is ‘leukaemia’? It may be that with some conditions, a new diagnostic test needs to be evaluated as a prognostic marker or risk stratification aid before emerging as a new diagnostic criterion (it’s worth reviewing the story of the Philadelphia chromosome and leukaemia in this regard). With many tests giving continuous outcome values, it becomes potentially more meaningful to think of them as a graded indicator rather than a positive or negative result.<\/p>\n
With more thinking along these lines, it can become tricky to really split hairs between prognosis and diagnostic tests, and may be worth considering them all as predictive factors. How you wish to interpret them, as risk ratios, or sensitivity\/specificity, then depends on how you need to use them.<\/p>\n","protected":false},"excerpt":{"rendered":"
What can you do when a ‘gold standard’ isn’t actually that good at diagnosing a condition? It can be terribly problematic in interpreting sensitivity and specificity – for example comparing polymerase chain reaction diagnosis of microbiological infection with culture results. The ‘false positive’ may actually reflect real, and otherwise missed, diagnosis, and the ‘false negatives’ […]<\/p>\n