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Klazien Matter-Walstra writes: >The foundation Paracelsus today organises many cources >on EBM and general practice medicine. > >One of the subjects which are discussed in the cources >is how pre-test and positive or negative post-test >probabilities (positive predictive value, negative >predicitve value) are related. Although these >relations itself are easy understood we experience >that physicians have problems with how to appraise >pre-test probabilities. Because they realise that >appraising pre-test probabilities is mostly intuative >and influenced by experience, they argue that it is >still better to do a test than refrain from it because >they can't know the pre-test probability for certain. > >My question is if there is literature on (estimations >of) pre-test probabilities for certain deseases. I'm very interested in what others say about this. My understanding is that you look at the prevalence of the disease and adjust it upwards or downwards based on special characteristics of the patients you see and perhaps on information the patient tells you. I'm not a doctor, but I suspect that a good doctor has to have some ideas about prevalence in order to make even non-quantitative assessments of their patients. They also have to know whether the chances of a disease change when a patient is a two pack a day smoker or had a heart attack five years ago. I hope you stress that the doctors don't have to specify pre-test probabilities to three significant figures. I understand people's reluctance to attach numbers to these things, but surely they can provide an upper and lower bound. This can be as wide as they like. If the pre-test probability is anywhere from 3% to 30% and the likelihood ratio for a positive result is 2.0, then the post test probability is between 6% and 46%. If the likelihood ratio is 10.0, then the post test probability is between 24% and 81%. If the likelihood ratio is 50.0 then the post test probability is between 61% and 96%. In each one of these cases, knowing the range of post-test probabilities is still better than not doing the calculation at all. If you decide to treat when the post-test probability is greater than 50%, then the three decisions would be "don't treat",."order additional tests", and "treat". If you order a test, and you don't have a good idea about the probability of disease after ordering the test, you haven't made good use of the test. If your doctors are still reluctant to use pre-test probabilities, perhaps it would help to present them with a test that has three or four possible results. If they don't know immediately what to do on the basis of an intermediate result, you can show them how assessing pre-test probabilities can answer that question for them. Another situation that is ambiguous is a positive test result during the "off-season". Can we still rely on a positive test when we know that the condition is very rare in the summer? How rare does the condition have to get in order to make the test a waste of time? Another important situation is how to assess these tests when you are a specialist who only sees cases that are referred to you by others. The patients that a specialist sees are far more likely to have a serious condition. How does this change how you view test results? My background is in Statistics rather than in Medicine, so my comments may be naive. I'd appreciate any clarifications from others on this list. Steve Simon, [log in to unmask], Standard Disclaimer. STATS - Steve's Attempt to Teach Statistics: http://www.cmh.edu/stats %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%