Personalized Medicine and the Role of Patient Preferences, Risk Perception and Information Exchange
- Author(s): Chanfreau, Catherine Claire
- Advisor(s): Ponce, Ninez A
- et al.
There are great expectations in the potential of personalized medicine to improve health outcomes by tailoring treatments to the needs of individual patients. Still, uncertainty remains on the values of these new technologies in the clinical setting. In particular, little is known about how physicians and patients use such information for decision-making, and about the influence of patient preferences, attitudes and information exchange in this process.
This dissertation expands on existing frameworks modeling health behaviors and treatment decisions to conceptualize pathways intervening on treatment decisions. The model studied here is the decision of using chemotherapy in early-stage breast cancer treatment based on a genomic test. This test predicts the risk of tumor recurrence, and helps identify patients at low risk who may avoid potentially unnecessary chemotherapy.
The three studies used data from a retrospective patient survey examining patient preferences, risk perception, and information exchange at the time of the treatment decision. Survey reports were linked to claims data and laboratory results for a diverse sample of privately insured women who had all received the genomic test. The methodology includes multivariate logistic regression models, a test for mediation using the Karlson-Holm-Breen method for nested logistic models, and a test for moderation using the inclusion of an interaction term. Limitations of the studies include a retrospective design, lack of information on the physician perspective, and omitted clinical factors that may confound the results.
Results support that 1) risk perception mediates the effect of patient preferences on the treatment decision and is a suppressor of this effect, 2) information exchange moderates the effect of risk perception on the treatment decision, and 3) those relationships explain in part variations in treatment decisions observed in this sample. Specifically, we found variations in decision by race/ethnicity consistent with significant variations in risk perception and information exchange by race/ethnicity.
The dissertation provides new knowledge on patient factors influencing the treatment decision. Importantly, those factors are mutable and suitable targets for interventions. Those factors may also be relevant to address disparities in breast cancer care, as they are strongly associated with race and ethnicity.