A Bayesian multivariate hierarchical model for developing a treatment benefit index using mixed types of outcomes.

BACKGROUND: Precision medicine has led to the development of targeted treatment strategies tailored to individual patients based on their characteristics and disease manifestations. Although precision medicine often focuses on a single health outcome for individualized treatment decision rules (ITRs), relying only on a single outcome rather than all available outcomes information leads to suboptimal data usage when developing optimal ITRs. METHODS: To address this limitation, we propose a Bayesian multivariate hierarchical model that leverages the wealth of correlated health outcomes collected in clinical trials. The approach jointly models mixed types of correlated outcomes, facilitating the "borrowing of information" across the multivariate outcomes, and results in a more accurate estimation of heterogeneous treatment effects compared to using single regression models for each outcome. We develop a treatment benefit index, which quantifies the relative benefit of the experimental treatment over the control treatment, based on the proposed multivariate outcome model. RESULTS: We demonstrate the strengths of the proposed approach through extensive simulations and an application to an international Coronavirus Disease 2019 (COVID-19) treatment trial. Simulation results indicate that the proposed method reduces the occurrence of erroneous treatment decisions compared to a single regression model for a single health outcome. Additionally, the sensitivity analyses demonstrate the robustness of the model across various study scenarios. Application of the method to the COVID-19 trial exhibits improvements in estimating the individual-level treatment efficacy (indicated by narrower credible intervals for odds ratios) and optimal ITRs. CONCLUSION: The study jointly models mixed types of outcomes in the context of developing ITRs. By considering multiple health outcomes, the proposed approach can advance the development of more effective and reliable personalized treatment.

Bayesian estimation of covariate assisted principal regression for brain functional connectivity.

This paper presents a Bayesian reformulation of covariate-assisted principal regression for covariance matrix outcomes to identify low-dimensional components in the covariance associated with covariates. By introducing a geometric approach to the covariance matrices and leveraging Euclidean geometry, we estimate dimension reduction parameters and model covariance heterogeneity based on covariates. This method enables joint estimation and uncertainty quantification of relevant model parameters associated with heteroscedasticity. We demonstrate our approach through simulation studies and apply it to analyze associations between covariates and brain functional connectivity using data from the Human Connectome Project.

Optimizing the use of ketamine to reduce chronic postsurgical pain in women undergoing mastectomy for oncologic indication: study protocol for the KALPAS multicenter randomized controlled trial.

BACKGROUND: Mastectomies are commonly performed and strongly associated with chronic postsurgical pain (CPSP), more specifically termed postmastectomy pain syndrome (PMPS), with 25-60% of patients reporting pain 3 months after surgery. PMPS interferes with function, recovery, and compliance with adjuvant therapy. Importantly, it is associated with chronic opioid use, as a recent study showed that 1 in 10 patients continue to use opioids at least 3 months after curative surgery. The majority of PMPS patients are women, and, over the past 10 years, women have outpaced men in the rate of growth in opioid dependence. Standard perioperative multimodal analgesia is only modestly effective in prevention of CPSP. Thus, interventions to reduce CPSP and PMPS are urgently needed. Ketamine is well known to improve pain and reduce opioid use in the acute postoperative period. Additionally, ketamine has been shown to control mood in studies of anxiety and depression. By targeting acute pain and improving mood in the perioperative period, ketamine may be able to prevent the development of CPSP. METHODS: Ketamine analgesia for long-lasting pain relief after surgery (KALPAS) is a phase 3, multicenter, randomized, placebo-controlled, double-blind trial to study the effectiveness of ketamine in reducing PMPS. The study compares continuous perioperative ketamine infusion vs single-dose ketamine in the postanesthesia care unit vs placebo for reducing PMPS. Participants are followed for 1 year after surgery. The primary outcome is pain at the surgical site at 3 months after the index surgery as assessed with the Brief Pain Inventory-short form pain severity subscale. DISCUSSION: This project is part of the NIH Helping to End Addiction Long-term (HEAL) Initiative, a nationwide effort to address the opioid public health crisis. This study can substantially impact perioperative pain management and can contribute significantly to combatting the opioid epidemic. TRIAL REGISTRATION: ClinicalTrials.gov NCT05037123. Registered on September 8, 2021.

Improving Individualized Treatment Decisions: A Bayesian Multivariate Hierarchical Model for Developing a Treatment Benefit Index using Mixed Types of Outcomes.

Background: Precision medicine has led to the development of targeted treatment strategies tailored to individual patients based on their characteristics and disease manifestations. Although precision medicine often focuses on a single health outcome for individualized treatment decision rules (ITRs), relying only on a single outcome rather than all available outcomes information leads to suboptimal data usage when developing optimal ITRs. Methods: To address this limitation, we propose a Bayesian multivariate hierarchical model that leverages the wealth of correlated health outcomes collected in clinical trials. The approach jointly models mixed types of correlated outcomes, facilitating the "borrowing of information" across the multivariate outcomes, and results in a more accurate estimation of heterogeneous treatment effects compared to using single regression models for each outcome. We develop a treatment benefit index, which quantifies the relative treatment benefit of the experimental treatment over the control treatment, based on the proposed multivariate outcome model. Results: We demonstrate the strengths of the proposed approach through extensive simulations and an application to an international Coronavirus Disease 2019 (COVID-19) treatment trial. Simulation results indicate that the proposed method reduces the occurrence of erroneous treatment decisions compared to a single regression model for a single health outcome. Additionally, the sensitivity analysis demonstrates the robustness of the model across various study scenarios. Application of the method to the COVID-19 trial exhibits improvements in estimating the individual-level treatment efficacy (indicated by narrower credible intervals for odds ratios) and optimal ITRs. Conclusion: The study jointly models mixed types of outcomes in the context of developing ITRs. By considering multiple health outcomes, the proposed approach can advance the development of more effective and reliable personalized treatment.

Bayesian Index Models for Heterogeneous Treatment Effects on a Binary Outcome.

This paper develops a Bayesian model with a flexible link function connecting a binary treatment response to a linear combination of covariates and a treatment indicator and the interaction between the two. Generalized linear models allowing data-driven link functions are often called "single-index models" and are among popular semi-parametric modeling methods. In this paper, we focus on modeling heterogeneous treatment effects, with the goal of developing a treatment benefit index (TBI) incorporating prior information from historical data. The model makes inference on a composite moderator of treatment effects, summarizing the effect of the predictors within a single variable through a linear projection of the predictors. This treatment benefit index can be useful for stratifying patients according to their predicted treatment benefit levels and can be especially useful for precision health applications. The proposed method is applied to a COVID-19 treatment study.