Abstract
Unintended consequences of secondary prevention include potential introduction of bias into epidemiologic studies estimating genotype-disease associations. To better understand such bias, we simulated a family-based study of colorectal cancer (CRC), which can be prevented by resecting screen-detected polyps. We simulated genes related to CRC development through risk of polyps (G1), risk of CRC but not polyps (G2), and progression from polyp to CRC (G3). Then, we examined 4 analytical strategies for studying diseases subject to secondary prevention, comparing the following: 1) CRC cases with all controls, without adjusting for polyp history; 2) CRC cases with controls, adjusting for polyp history; 3) CRC cases with only polyp-free controls; and 4) cases with either CRC or polyps with controls having neither. Strategy 1 yielded estimates of association between CRC and each G that were not substantially biased. Strategies 2-4 yielded biased estimates varying in direction according to analysis strategy and gene type. Type I errors were correct, but strategy 1 provided greater power for estimating associations with G2 and G3. We also applied each strategy to case-control data from the Colon Cancer Family Registry (1997-2007). Generally, the best analytical option balancing bias and power is to compare all CRC cases with all controls, ignoring polyps.