Incorporating Patient Preferences into Estimation of Optimal Individualized Treatment Rules.

Precision medicine seeks to provide treatment only if, when, to whom, and at the dose it is needed. Thus, precision medicine is a vehicle by which healthcare can be made both more effective and efficient. Individualized treatment rules operationalize precision medicine as a map from current patient information to a recommended treatment. An optimal individualized treatment rule is defined as maximizing the mean of a pre-specified scalar outcome. However, in settings with multiple outcomes, choosing a scalar composite outcome by which to define optimality is difficult. Furthermore, when there is heterogeneity across patient preferences for these outcomes, it may not be possible to construct a single composite outcome that leads to high-quality treatment recommendations for all patients. We simultaneously estimate the optimal individualized treatment rule for all composite outcomes representable as a convex combination of the (suitably transformed) outcomes. For each patient, we use a preference elicitation questionnaire and item response theory to derive the posterior distribution over preferences for these composite outcomes and subsequently derive an estimator of an optimal individualized treatment rule tailored to patient preferences. We prove that as the number of subjects and items on the questionnaire diverge, our estimator is consistent for an oracle optimal individualized treatment rule wherein each patient's preference is known a priori. We illustrate the proposed method using data from a clinical trial on antipsychotic medications for schizophrenia.

Use of Evidence-Based Interventions to Address Disparities in Colorectal Cancer Screening.

Colorectal cancer (CRC) is the second leading cause of cancer death among cancers that affect both men and women. Despite strong evidence of their effectiveness, CRC screening tests are underused. Racial/ethnic minority groups, persons without insurance, those with lower educational attainment, and those with lower household income levels have lower rates of CRC screening. Since 2009, CDC's Colorectal Cancer Control Program (CRCCP) has supported state health departments and tribal organizations in implementing evidence-based interventions (EBIs) to increase use of CRC screening tests among their populations. This report highlights the successful implementation of EBIs to address disparities by two CRCCP grantees: the Alaska Native Tribal Health Consortium (ANTHC) and Washington State's Breast, Cervical, and Colon Health Program (BCCHP). ANTHC partnered with regional tribal health organizations in the Alaska Tribal Health System to implement provider and client reminders and use patient navigators to increase CRC screening rates among Alaska Native populations. BCCHP identified patient care coordinators in each clinic who coordinated staff training on CRC screening and integrated client and provider reminder systems. In both the Alaska and Washington programs, instituting provider reminder systems, client reminder systems, or both was facilitated by use of electronic health record systems. Using multicomponent interventions in a single clinical site or facility can support more organized screening programs and potentially result in greater increases in screening rates than relying on a single strategy. Organized screening systems have an explicit policy for screening, a defined target population, a team responsible for implementation of the screening program, and a quality assurance structure. Although CRC screening rates in the United States have increased steadily over the past decade, this increase has not been seen equally across all populations. Increasing the use of EBIs, such as those described in this report, in health care clinics and systems that serve populations with lower CRC screening rates could substantially increase CRC screening rates.