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INTRODUCTION

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Yeah, we don't like numbers either. But they are at the end of the chapter for those of you who want to learn 2 × 2 tables, etc. We do like evidence-based medicine (EBM) though, and it will be on the examination. So here goes….

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Table 28-1 is here for reference. You may want to refer to it as you work your way through the chapter.

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TABLE 28-1USEFUL EQUATIONS
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CASE 28.1

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Research published in World's Best Medical Journal studied screening for lung cancer using a new method. The researchers reported that patients who had lung cancer detected via screening lived longer after diagnosis than people who were diagnosed with lung cancer but not screened.

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Question 28.1.1 Which is true?

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A) This shows that screening is effective at prolonging survival.

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B) This may be an example of lead-time bias.

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C) This may be an example of verification bias.

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D) Well-respected medical journals (and board review books) are always right.

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Answer 28.1.1 The correct answer is "B." This may be an example of lead-time bias. Screening is intended to diagnose disease earlier, hopefully allowing for interventions that prevent or slow the progression of the disease. Without screening, the disease may be discovered only after symptoms develop when it may be too late to intervene. Screening, however, can also give the appearance of longer survival even though no additional life has been gained. This is called lead-time bias. Here's another example. Mr. X has the test, is diagnosed with disease, receives treatment, and dies 5 years later. Mr. Y is in the control group, develops symptoms at year 4 of the study and dies one year after that. They have both lived for 5 years after being randomized to screening or no screening. Mr. X and Mr. Y both die at age 65 of the same disease. Did Mr. X have more survival time or just more "disease time?" This lead-time bias may be avoided by using age-specific mortality rates rather than survival time from diagnosis. "C," verification bias, occurs when you are looking at a new diagnostic modality, and patients with a negative test result (for the new test) are not evaluated with the gold standard test. For example, verification bias could occur in a study where people with a ...

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