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Why Doctors Are Bad At Stats — And How That Could Affect Your Health

Gerd and his team have explored whether medical professionals understand the statistics measures actually needed to prove that a cancer screening programme saves lives.
This is a classic problem in health statistics. What clinicians need to compare is mortality rates, not 5-year survival rates. The mortality rate tells the number of deaths in a period of time. In contrast, the 5-year survival rate only tells how many people live 5 years after the day they have been diagnosed with cancer.
Some screening programmes can diagnose people earlier — which can increase those ‘5-year survival rates’ — without making them live any longer.

Bayes' theorem

Despite the apparent accuracy of the test, if an individual tests positive, it is more likely that they do not use the drug than that they do. This surprising result arises because the number of non-users is very large compared to the number of users; thus, the number of false positives outweighs the number of true positives. To use concrete numbers, if 1000 individuals are tested, there are expected to be 995 non-users and 5 users. From the 995 non-users, 0.01 × 995 ≃ 10 false positives are expected. From the 5 users, 0.99 × 5 ≈ 5 true positives are expected. Out of 15 positive results, only 5, about 33%, are genuine. This illustrates the importance of base rates, and how the formation of policy can be egregiously misguided if base rates are neglected.[15]
The importance of specificity in this example can be seen by calculating that even if sensitivity is raised to 100% and specificity remains at 99% then the probability of the person being a drug user only rises from 33.2% to 33.4%, but if the sensitivity is held at 99% and the specificity is increased to 99.5% then probability of the person being a drug user rises to about 49.9%.