Model selection for survival individualized treatment rules using the jackknife estimator.

BACKGROUND: Precision medicine is an emerging field that involves the selection of treatments based on patients' individual prognostic data. It is formalized through the identification of individualized treatment rules (ITRs) that maximize a clinical outcome. When the type of outcome is time-to-event, the correct handling of censoring is crucial for estimating reliable optimal ITRs. METHODS: We propose a jackknife estimator of the value function to allow for right-censored data for a binary treatment. The jackknife estimator or leave-one-out-cross-validation approach can be used to estimate the value function and select optimal ITRs using existing machine learning methods. We address the issue of censoring in survival data by introducing an inverse probability of censoring weighted (IPCW) adjustment in the expression of the jackknife estimator of the value function. In this paper, we estimate the optimal ITR by using random survival forest (RSF) and Cox proportional hazards model (COX). We use a Z-test to compare the optimal ITRs learned by RSF and COX with the zero-order model (or one-size-fits-all). Through simulation studies, we investigate the asymptotic properties and the performance of our proposed estimator under different censoring rates. We illustrate our proposed method on a phase III clinical trial of non-small cell lung cancer data. RESULTS: Our simulations show that COX outperforms RSF for small sample sizes. As sample sizes increase, the performance of RSF improves, in particular when the expected log failure time is not linear in the covariates. The estimator is fairly normally distributed across different combinations of simulation scenarios and censoring rates. When applied to a non-small-cell lung cancer data set, our method determines the zero-order model (ZOM) as the best performing model. This finding highlights the possibility that tailoring may not be needed for this cancer data set. CONCLUSION: The jackknife approach for estimating the value function in the presence of right-censored data shows satisfactory performance when there is small to moderate censoring. Winsorizing the upper and lower percentiles of the estimated survival weights for computing the IPCWs stabilizes the estimator.

Cultural Transformation in Healthcare: How Well Does the Veterans Health Administration Vision for Whole Person Care Fit the Needs of Patients at an Academic Rehabilitation Center?

Background: The Veterans Health Administration is undergoing a cultural transformation toward person-driven care referred to as the Whole Health System of Care. Objective: This pilot study evaluated whether the Whole Health model resonates with patients of a large public university rehabilitation clinic. Methods: Thirty participants completed the Veterans Health Administration's Personal Health Inventory (PHI), and six attended the course "Taking Charge of My Life and Health." Researchers analyzed PHI responses and post-course focus group transcripts. A short post-PHI survey and post-course evaluation were collected. Results: Participants agreed the PHI is a simple, useful tool. The course, while well attended, did not meet participants' expectations. Participants wanted access to integrative therapies and opportunities to contribute to healthcare transformation. Conclusion: Rehabilitation patients resonated with the Whole Health vision. They expressed enthusiasm for the cultural transformation represented by the model along with frustration that standard healthcare experiences fall short of this vision.