Patient Preferences for Multi-Cancer Early Detection (MCED) Screening Tests

Abstract

Background: Emerging blood-based multi-cancer early detection (MCED) tests can detect a variety of cancer types across stages with a range of sensitivity, specificity, and ability to predict the origin of the cancer signal. However, little is known about the general US population's preferences for MCED tests.

Objective: To quantify preferences for MCED tests among US adults aged 50-80 years using a discrete choice experiment (DCE).

Methods: To quantify preferences for attributes of blood-based MCED tests, an online DCE was conducted with five attributes (true positives, false negatives, false positives, likelihood of the cancer type unknown, number of cancer types detected), among the US population aged 50-80 years recruited via online panels and social media. Data were analyzed using latent class multinomial logit models and relative attribute importance was obtained.

Results: Participants (N = 1700) were 54% female, mean age 63.3 years. Latent class modeling identified three classes with distinct preferences for MCED tests. The rank order of attribute importance based on relative attribute importance varied by latent class, but across all latent classes, participants preferred higher accuracy (fewer false negatives and false positives, more true positives) and screenings that detected more cancer types and had a lower likelihood of cancer type unknown. Overall, 72% of participants preferred to receive an MCED test in addition to currently recommended cancer screenings.

Conclusions: While there is significant heterogeneity in cancer screening preferences, the majority of participants preferred MCED screening and the accuracy of these tests is important. While the majority of participants preferred adding an MCED test to complement current cancer screenings, the latent class analyses identified a small (16%) and specific subset of individuals who value attributes differently, with particular concern regarding false-negative and false-positive test results, who are significantly less likely to opt-in.