On Causal Inferences for Personalized Medicine: How Hidden Causal Assumptions Led to Erroneous Causal Claims About the D-Value

Abstract

Personalized medicine asks if a new treatment will help a particular patient, rather than if it improves the average response in a population. Without a causal model to distinguish these questions, interpretational mistakes arise. These mistakes are seen in an article by Demidenko that recommends the “D-value,” which is the probability that a randomly chosen person from the new-treatment group has a higher value for the outcome than a randomly chosen person from the control-treatment group. The abstract states “The D-value has a clear interpretation as the proportion of patients who get worse after the treatment” with similar assertions appearing later. We show these statements are incorrect because they require assumptions about the potential outcomes which are neither testable in randomized experiments nor plausible in general. The D-value will not equal the proportion of patients who get worse after treatment if (as expected) those outcomes are correlated. Independence of potential outcomes is unrealistic and eliminates any personalized treatment effects; with dependence, the D-value can even imply treatment is better than control even though most patients are harmed by the treatment. Thus, D-values are misleading for personalized medicine. To prevent misunderstandings, we advise incorporating causal models into basic statistics education.     

Facilitators of prenatal care access in rural Appalachia

BackgroundThere are many providers and models of prenatal care, some more effective than others. However, quantitative research alone cannot determine the reasons beneficial models of care improve health outcomes. Perspectives of women receiving care from effective clinics can provide valuable insight.MethodsWe surveyed 29 women receiving care at a rural, Appalachian birth center in the United States with low rates of preterm birth. Semi-structured interviews and demographic questionnaires were analyzed using conventional qualitative content analysis of manifest content.FindingsInsurance was the most common facilitator of prenatal access. Beneficial characteristics of the provider and clinic included: personalized care, unrushed visits, varied appointment times, short waits, and choice in the type and location of care.ConclusionThere is a connection between compassionate and personalized care and positive birth outcomes. Women were willing to overcome barriers to access care that met their needs. To facilitate access to prenatal care and decrease health disparities, healthcare planners, and policy makers need to ensure all women can afford to access prenatal care and allow women a choice in their care provider. Clinic administrators should create a welcoming clinic environment with minimal wait time. Unrushed, woman-centered prenatal visits can increase access to and motivation for care and are easily integrated into prenatal care with minimal cost.