Attitudes, knowledge, and risk perceptions of patients who received elective genomic testing as a clinical service.

PURPOSE: Elective genomic testing (EGT) is increasingly available clinically. Limited real-world evidence exists about attitudes and knowledge of EGT recipients. METHODS: After web-based education, patients who enrolled in an EGT program at a rural nonprofit health care system completed a survey that assessed attitudes, knowledge, and risk perceptions. RESULTS: From August 2020 to April 2022, 5920 patients completed the survey and received testing. Patients most frequently cited interest in learning their personal disease risks as their primary motivation. Patients most often expected results to guide medication management (74.0%), prevent future disease (70.4%), and provide information about risks to offspring (65.4%). Patients were "very concerned" most frequently about the privacy of genetic information (19.8%) and how well testing predicted disease risks (18.0%). On average, patients answered 6.7 of 11 knowledge items correctly (61.3%). They more often rated their risks for colon and breast cancers as lower rather than higher than the average person but more often rated their risk for a heart attack as higher rather than lower than the average person (all P < .001). CONCLUSION: Patients pursued EGT because of the utility expectations but often misunderstood the test's capabilities.

Assessing the goodness of fit of personal risk models.

We describe a flexible family of tests for evaluating the goodness of fit (calibration) of a pre-specified personal risk model to the outcomes observed in a longitudinal cohort. Such evaluation involves using the risk model to assign each subject an absolute risk of developing the outcome within a given time from cohort entry and comparing subjects' assigned risks with their observed outcomes. This comparison involves several issues. For example, subjects followed only for part of the risk period have unknown outcomes. Moreover, existing tests do not reveal the reasons for poor model fit when it occurs, which can reflect misspecification of the model's hazards for the competing risks of outcome development and death. To address these issues, we extend the model-specified hazards for outcome and death, and use score statistics to test the null hypothesis that the extensions are unnecessary. Simulated cohort data applied to risk models whose outcome and mortality hazards agreed and disagreed with those generating the data show that the tests are sensitive to poor model fit, provide insight into the reasons for poor fit, and accommodate a wide range of model misspecification. We illustrate the methods by examining the calibration of two breast cancer risk models as applied to a cohort of participants in the Breast Cancer Family Registry. The methods can be implemented using the Risk Model Assessment Program, an R package freely available at http://stanford.edu/~ggong/rmap/.