2DkcTXceO

398 Good health: Statistical challengesFIGURE 35.3Scatterplot of BCRAT and IBIS case risk percentiles for NY BCFR data.not be generalizable. Thus we need methods for accommodating bias in estimatedperformance measures due to cohort selection. Also, because large cohortstudies are costly, we need ways to evaluate model discrimination usingcase-control data that is not nested within a cohort. Finally, we need methodsfor developing, applying and evaluating multi-state models for multipleadverse events. The following is a brief description of these problem areas.35.5.1 Cohort selection biasThe covariate distributions of individuals in the general population are notwell represented by those of the highly selected participants in large, long-termcohort studies. For example, we found that a published ovarian cancer riskmodel developed using postmenopausal women in the Nurses’ Health Study(Rosner et al., 2005) was well-calibrated to postmenopausal subjects in theCalifornia Teachers Study (CTS) (Bernstein et al., 2002) but poorly calibratedto those in the Women’s Health Initiative (WHI) (Luo et al., 2011). We foundthat although covariate-specific hazard-ratios are similar in the two cohorts,their covariate distributions are very different: e.g., parity is much higherin WHI than CTS. Moreover the distributions of covariates like education andparity among cohort subjects tend to be more homogeneous than those of thegeneral population. Work is needed to compare the distributions of covariatesamong subjects in cohort studies with those of the general US population, as