Enhancing DHA supplementation adherence: A Bayesian approach with finite mixture models and irregular interim schedules in adaptive trial designs.

Docosahexaenoic acid (DHA) supplementation has proven beneficial in reducing preterm births. However, the challenge lies in addressing nonadherence to prescribed supplementation regimens-a hurdle that significantly impacts clinical trial outcomes. Conventional methods of adherence estimation, such as pill counts and questionnaires, usually fall short when estimating adherence within a specific dosage group. Thus, we propose a Bayesian finite mixture model to estimate adherence among women with low baseline red blood cell phospholipid DHA levels (<6%) receiving higher DHA doses. In our model, adherence is defined as the proportion of participants classified into one of the two distinct components in a normal mixture distribution. Subsequently, based on the estimands from the adherence model, we introduce a novel Bayesian adaptive trial design. Unlike conventional adaptive trials that employ regularly spaced interim schedules, the novelty of our proposed trial design lies in its adaptability to adherence percentages across the treatment arm through irregular interims. The irregular interims in the proposed trial are based on the effect size estimation informed by the finite mixture model. In summary, this study presents innovative methods for leveraging the capabilities of Bayesian finite mixture models in adherence analysis and the design of adaptive clinical trials.

DHA supplementation for early preterm birth prevention: An application of Bayesian finite mixture models to adaptive clinical trial design optimization.

BACKGROUND: Early preterm birth (ePTB) - born before 34 weeks of gestation - poses a significant public health challenge. Two randomized trials indicated an ePTB reduction among pregnant women receiving high-dose docosahexaenoic acid (DHA) supplementation. One of them is Assessment of DHA on Reducing Early Preterm Birth (ADORE). A survey employed in its secondary analysis identified women with low DHA levels, revealing that they derived greater benefits from high-dose DHA supplementation. This survey's inclusion in future trials can provide critical insights for informing clinical practices. OBJECTIVE: To optimize a Phase III trial design, ADORE Precision, aiming at assessing DHA supplement (200 vs. 1000 mg/day) on reducing ePTB among pregnant women with a low baseline DHA. METHODS: We propose a Bayesian Hybrid Response Adaptive Randomization (RAR) Design utilizing a finite mixture model to characterize gestational age at birth. Subsequently, a dichotomized ePTB outcome is used to inform trial design using RAR. Simulation studies were conducted to compare a Fixed Design, an Adaptive Design with early stopping, an ADORE-like Adaptive RAR Design, and two new Hybrid Designs with different hyperpriors. DISCUSSION: Simulation reveals several advantages of the RAR designs, such as higher allocation to the more promising dose and a trial duration reduction. The proposed Hybrid RAR Designs addresses the statistical power drop observed in Adaptive RAR. The new design model shows robustness to hyperprior choices. We recommend Hybrid RAR Design 1 for ADORE Precision, anticipating that it will yield precise determinations, which is crucial for advancing our understanding in this field.

Implementation of statistical features of a Bayesian two-armed responsive adaptive randomization trial with post hoc analysis of time trend drift.

Bayesian adaptive designs with response adaptive randomization (RAR) have the potential to benefit more participants in a clinical trial. While there are many papers that describe RAR designs and results, there is a scarcity of works reporting the details of RAR implementation from a statistical point exclusively. In this paper, we introduce the statistical methodology and implementation of the trial Changing the Default (CTD). CTD is a single-center prospective RAR comparative effectiveness trial to compare opt-in to opt-out tobacco treatment approaches for hospitalized patients. The design assumed an uninformative prior, conservative initial allocation ratio, and a higher threshold for stopping for success to protect results from statistical bias. A particular emerging concern of RAR designs is the possibility that time trends will occur during the implementation of a trial. If there is a time trend and the analytic plan does not prespecify an appropriate model, this could lead to a biased trial. Adjustment for time trend was not pre-specified in CTD, but post hoc time-adjusted analysis showed no presence of influential drift. This trial was an example of a successful two-armed confirmatory trial with a Bayesian adaptive design using response adaptive randomization.

Improved mortality analysis in early-phase dose-ranging clinical trials for emergency medical diseases using Bayesian time-to-event models with active comparators.

Emergency medical diseases (EMDs) are the leading cause of death worldwide. A time-to-death analysis is needed to accurately identify the risks and describe the pattern of an EMD because the mortality rate can peak early and then decline. Dose-ranging Phase II clinical trials are essential for developing new therapies for EMDs. However, most dose-finding trials do not analyze mortality as a time-to-event endpoint. We propose three Bayesian dose-response time-to-event models for a secondary mortality analysis of a clinical trial: a two-group (active treatment vs control) model, a three-parameter sigmoid EMAX model, and a hierarchical EMAX model. The study also incorporates one specific active treatment as an active comparator in constructing three new models. We evaluated the performance of these six models and a very popular independent model using simulated data motivated by a randomized Phase II clinical trial focused on identifying the most effective hyperbaric oxygen dose to achieve favorable functional outcomes in patients with severe traumatic brain injury. The results show that the three-group, EMAX, and EMAX model with an active comparator produce the smallest averaged mean squared errors and smallest mean absolute biases. We provide a new approach for time-to-event analysis in early-phase dose-ranging clinical trials for EMDs. The EMAX model with an active comparator can provide valuable insights into the mortality analysis of new EMDs or other conditions that have changing risks over time. The restricted mean survival time, a function of the model's hazards, is recommended for displaying treatment effects for EMD research.

Using Bayesian hierarchical modeling for performance evaluation of clinical trial accrual for a cancer center.

Introduction: Slow patient accrual in cancer clinical trials is always a concern. In 2021, the University of Kansas Comprehensive Cancer Center (KUCC), an NCI-designated comprehensive cancer center, implemented the Curated Cancer Clinical Outcomes Database (C3OD) to perform trial feasibility analyses using real-time electronic medical record data. In this study, we proposed a Bayesian hierarchical model to evaluate annual cancer clinical trial accrual performance. Methods: The Bayesian hierarchical model uses Poisson models to describe the accrual performance of individual cancer clinical trials and a hierarchical component to describe the variation in performance across studies. Additionally, this model evaluates the impacts of the C3OD and the COVID-19 pandemic using posterior probabilities across evaluation years. The performance metric is the ratio of the observed accrual rate to the target accrual rate. Results: Posterior medians of the annual accrual performance at the KUCC from 2018 to 2023 are 0.233, 0.246, 0.197, 0.150, 0.254, and 0.340. The COVID-19 pandemic partly explains the drop in performance in 2020 and 2021. The posterior probability that annual accrual performance is better with C3OD in 2023 than pre-pandemic (2019) is 0.935. Conclusions: This study comprehensively evaluates the annual performance of clinical trial accrual at the KUCC, revealing a negative impact of COVID-19 and an ongoing positive impact of C3OD implementation. Two sensitivity analyses further validate the robustness of our model. Evaluating annual accrual performance across clinical trials is essential for a cancer center. The performance evaluation tools described in this paper are highly recommended for monitoring clinical trial accrual.

Assessing the incidence and severity of drug adverse events: a Bayesian hierarchical cumulative logit model.

Detection of safety signals based on multiple comparisons of adverse events (AEs) between two treatments in a clinical trial involves evaluations requiring multiplicity adjustment. A Bayesian hierarchical mixture model is a good solution to this problem as it borrows information across AEs within the same System Organ Class (SOC) and modulates extremes due merely to chance. However, the hierarchical model compares only the incidence rates of AEs, regardless of severity. In this article, we propose a three-level Bayesian hierarchical non-proportional odds cumulative logit model. Our model allows for testing the equality of incidence rate and severity for AEs between the control arm and the treatment arm while addressing multiplicities. We conduct simulation study to investigate the operating characteristics of the proposed hierarchical model. The simulation study demonstrates that the proposed method could be implemented as an extension of the Bayesian hierarchical mixture model in detecting AEs with elevated incidence rate and/or elevated severity. To illustrate, we apply our proposed method using the safety data from a phase III, two-arm randomized trial.

Conducting a bayesian multi-armed trial with response adaptive randomization for comparative effectiveness of medications for CSPN.

Background: Response adaptive randomization is popular in adaptive trial designs, but the literature detailing its execution is lacking. These designs are desirable for patients/stakeholders, particularly in comparative effectiveness research, due to the potential benefits including improving participant buy-in by providing more participants with better treatment during the trial. Frequentist approaches have often been used, but adaptive designs naturally fit the Bayesian methodology; it was developed to deal with data as they come in by updating prior information. Methods: PAIN-CONTRoLS was a comparative-effectiveness trial utilizing Bayesian response adaptive randomization to four drugs, nortriptyline, duloxetine, pregabalin, or mexiline, for cryptogenic sensory polyneuropathy (CSPN) patients. The aim was to determine which treatment was most tolerable and effective in reducing pain. Quit and efficacy rates were combined into a utility function to develop a single outcome, which with treatment sample size, drove the adaptive randomization. Prespecified interim analyses allowed the study to stop for early success or update the randomization probabilities to the better-performing treatments. Results: Seven adaptations to the randomization occurred before the trial ended due to reaching the maximum sample size, with more participants receiving nortriptyline and duloxetine. At the end of the follow-up, nortriptyline and duloxetine had lower probabilities of participants that had stopped taking the study medication and higher probabilities were efficacious. Mexiletine had the highest quit rate, but had an efficacy rate higher than pregabalin. Conclusions: Response adaptive randomization has become a popular trial tool, especially for those utilizing Bayesian methods for analyses. By illustrating the execution of a Bayesian adaptive design, using the PAIN-CONTRoLS trial data, this paper continues the work to provide literature for conducting Bayesian response adaptive randomized trials.

DHA, nutrient intake, and maternal characteristics as predictors of pregnancy outcomes in a randomised clinical trial of DHA supplementation.

PURPOSE: To investigate the relationships among docosahexaenoic acid (DHA) intake, nutrient intake, and maternal characteristics on pregnancy outcomes in a phase III randomised clinical trial designed to determine the effect of a DHA dose of 1000 mg/day compared to 200 mg/day on early preterm birth (<34 weeks gestation). METHODS: A secondary aim of the phase III randomised trial was to explore the relationships among pregnancy outcomes (maternal red blood cell phospholipid (RBC-PL) DHA at delivery, preterm birth, gestational age at delivery, labor type, birth anthropometric measures, low birth weight, gestational diabetes, pre-eclampsia, and admission to a neonatal intensive care unit) in participants (n = 1100). We used Bayesian multiple imputation and linear and logistic regression models to conduct an analysis of five general classes of predictor variables collected during the trial: a) DHA intake, b) nutrient intake from food and supplements, c) environmental exposure to tobacco and alcohol, d) maternal demographics, and e) maternal medical history. RESULTS: DHA supplementation lowered the risk of preterm birth and NICU admission, and increased gestation and birth weight as observed in the primary analysis. Higher maternal RBC-PL-DHA at delivery was associated with DHA supplementation and formal education of a bachelor's degree or higher. DHA supplementation and maternal age were associated with a higher risk of gestational diabetes. Total vitamin A intake was associated with longer gestation, while fructose and intake of the long chain omega-6 fatty acid, arachidonic acid, were associated with shorter gestation. Risk of preterm birth was associated with a history of low birth weight, preterm birth, pre-eclampsia, and NICU admission. CONCLUSION: Bayesian models provide a comprehensive approach to relationships among DHA intake, nutrient intake, maternal characteristics, and pregnancy outcomes. We observed previously unreported relationships between gestation duration and fructose, vitamin A, and arachidonic acid that could be the basis for future research. TRIAL REGISTRATION NUMBER AND DATE: ClinicalTrials.gov (NCT02626299); December 10, 2015.

Using a Bayesian model of the joint distribution of pain and time on medication to decide on pain medication for neuropathy.

The PAIN-CONTRoLS trial compared four medications in treating Cryptogenic sensory polyneuropathy. The primary outcome was a utility function that combined two outcomes, patients' pain score reduction and patients' quit rate. However, additional analysis of the individual outcomes could also be leveraged to inform selecting an optimal medication for future patients. We demonstrate how joint modeling of longitudinal and time-to-event data from PAIN-CONTRoLS can be used to predict the effects of medication in a patient-specific manner and helps to make patient-focused decisions. A joint model was used to evaluate the two outcomes while accounting for the association between the longitudinal process and the time-to-event processes. Results suggested no significant association between the patients' pain scores and time to the medication quit in the PAIN-CONTRoLS study, but the joint model still provided robust estimates and a better model fit. Using the model estimates, given patients' baseline characteristics, a drug profile on both the pain reduction and medication time could be obtained for each drug, providing information on how likely they would quit and how much pain reduction they should expect. Our analysis suggested that drugs viable for one patient may not be beneficial for others.