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.

Selection of a statistical analysis method for the Glasgow Outcome Scale-Extended endpoint for estimating the probability of favorable outcome in future severe TBI clinical trials.

The Glasgow outcome scale-extended (GOS-E), an ordinal scale measure, is often selected as the endpoint for clinical trials of traumatic brain injury (TBI). Traditionally, GOS-E is analyzed as a fixed dichotomy with favorable outcome defined as GOS-E ≥ 5 and unfavorable outcome as GOS-E < 5. More recent studies have defined favorable vs unfavorable outcome utilizing a sliding dichotomy of the GOS-E that defines a favorable outcome as better than a subject's predicted prognosis at baseline. Both dichotomous approaches result in loss of statistical and clinical information. To improve on power, Yeatts et al proposed a sliding scoring of the GOS-E as the distance from the cutoff for favorable/unfavorable outcomes, and therefore used more information found in the original GOS-E to estimate the probability of favorable outcome. We used data from a published TBI trial to explore the ramifications to trial operating characteristics by analyzing the sliding scoring of the GOS-E as either dichotomous, continuous, or ordinal. We illustrated a connection between the ordinal data and time-to-event (TTE) data to allow use of Bayesian software that utilizes TTE-based modeling. The simulation results showed that the continuous method with continuity correction offers higher power and lower mean squared error for estimating the probability of favorable outcome compared to the dichotomous method, and similar power but higher precision compared to the ordinal method. Therefore, we recommended that future severe TBI clinical trials consider analyzing the sliding scoring of the GOS-E endpoint as continuous with continuity correction.

Micronutrient Gaps and Supplement Use in a Diverse Cohort of Pregnant Women.

BACKGROUND: Micronutrition in pregnancy is critical to impact not only fetal growth and development but also long-term physical and psychiatric health outcomes. OBJECTIVE: Estimate micronutrient intake from food and dietary supplements in a diverse cohort of pregnant women and compare intake to the Dietary Reference Intakes (DRIs). DESIGN: Secondary analysis of women enrolled in a multi-site clinical trial of docosahexaenoic acid (DHA) supplementation who provided their dietary intake using the diet history questionnaire-II (n = 843) or multiple 24 h recalls (n = 178) at baseline and their intake of nutritional supplements at baseline through 30 days postpartum. PARTICIPANTS/SETTING: 1021 participants from the parent trial who had reliable data for dietary intake, supplement intake, or both. MAIN OUTCOME MEASURES: Micronutrient intake from dietary and supplement sources and percentage of intakes meeting the DRIs for pregnancy. STATISTICAL ANALYSES PERFORMED: Percent of participants whose intake was below the estimated average requirement (EAR) or adequate intake (AI) and above the tolerable upper limit (UL). RESULTS: Dietary intakes of choline, folate, iron, vitamin D, zinc, vitamin E, magnesium, and potassium, were below the AI or EAR for 30-91% of the participants; thiamin and vitamin B6 were also below the AI or EAR for non-Hispanic/Latina women. Supplement intake improved the intake for most; however, 80% of the group remained below the AI for choline and 52.5% for potassium while 30% remained below the EAR for magnesium. Folate and iron intakes were above the UL for 80% and 19%, respectively. CONCLUSIONS: Dietary supplements, despite their variability, allowed the majority of this cohort of pregnant women to achieve adequate intakes for most micronutrients. Choline, magnesium, and potassium were exceptions. Of interest, folate intake was above the tolerable UL for the majority and iron for 16.8% of the participants. Clinicians have the opportunity to address the most common nutrient deficits and limits with advice on food sources that provide choline, magnesium, and potassium and to ensure folate is not overabundant. More research is needed to determine if these findings are similar in a cross-sectional population.

Growth and adiposity in newborns study (GAINS): The influence of prenatal DHA supplementation protocol.

BACKGROUND: Obesity and central fat mass (FM) accrual drive disease development and are related to greater morbidity and mortality. Excessive gestational weight gain (GWG) increases fetal fat accretion resulting in greater offspring FM across the lifespan. Studies associate greater maternal docosahexaenoic acid (DHA) levels with lower offspring FM and lower visceral adipose tissue during childhood, however, most U.S. pregnant women do not consume an adequate amount of DHA. We will determine if prenatal DHA supplementation is protective for body composition changes during infancy and toddlerhood in offspring exposed to excessive GWG. METHODS AND DESIGN: Infants born to women who participated in the Assessment of DHA on Reducing Early Preterm Birth randomized controlled trial (ADORE; NCT02626299) will be invited to participate. Women were randomized to either a high 1000 mg or low 200 mg daily prenatal DHA supplement starting in the first trimester of pregnancy. Offspring body composition and adipose tissue distribution will be measured at 2 weeks, 6 months, 12 months, and 24 months using dual energy x-ray absorptiometry. Maternal GWG will be categorized as excessive or not excessive based on clinical guidelines. DISCUSSION: Effective strategies to prevent obesity development are lacking. Exposures during the prenatal period are important in the establishment of the offspring phenotype. However, it is largely unknown which exposures can be successfully targeted to have a meaningful impact. This study will determine if prenatal DHA supplementation modifies the relationship between maternal weight gain and offspring FM and FM distribution at 24 months of age. ETHICS AND DISSEMINATION: The University of Kansas Medical Center Institutional Review Board (IRB) approved the study protocol (STUDY00140895). The results of the trial will be disseminated at conferences and in peer reviewed publications. TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT03310983.

Using Bayesian hierarchical models for controlled post hoc subgroup analysis of clinical trials: application to smoking cessation treatment in American Indians and Alaska Natives.

Clinical trials powered to detect subgroup effects provide the most reliable data on heterogeneity of treatment effect among different subpopulations. However, pre-specified subgroup analysis is not always practical and post hoc analysis results should be examined cautiously. Bayesian hierarchical modelling provides grounds for defining a controlled post hoc analysis plan that is developed after seeing outcome data for the population but before unblinding the outcome by subgroup. Using simulation based on the results from a tobacco cessation clinical trial conducted among the general population, we defined an analysis plan to assess treatment effect among American Indians and Alaska Natives (AI/AN) enrolled in the study. Patients were randomized into two arms using Bayesian adaptive design. For the opt-in arm, clinicians offered a cessation treatment plan after verifying that a patient was ready to quit. For the opt-out arm, clinicians provided all participants with free cessation medications and referred them to a Quitline. The study was powered to test a hypothesis of significantly higher quit rates for the opt-out arm at one-month post randomization. Overall, one-month abstinence rates were 15.9% and 21.5% (opt-in and opt-out arm, respectively). For AI/AN, one-month abstinence rates were 10.2% and 22.0% (opt-in and opt-out arm, respectively). The posterior probability that the abstinence rate in the treatment arm is higher is 0.96, indicating that AI/AN demonstrate response to treatment at almost the same probability as the whole population.

Prenatal Care Utilization Challenges and Facilitators for a Growing Latino Community in the Midwest.

BACKGROUND: Latina women are less likely to start prenatal care in the first trimester and to attend the recommended amount of prenatal visits compared to their non-Latina white counterparts. OBJECTIVES: This study aimed to assess challenges and facilitators to first-trimester prenatal care (FTPNC) and prenatal care utilization (PNCU) in a Midwestern urban area with a growing immigrant Latino community. METHODS: This study used a mixed-method approach based on the Theoretical Domains Framework. Nine semi-structured interviews were conducted with healthcare professionals that worked in birth centers, clinics, or hospitals that provided prenatal care (PNC) services for Latina women. Eight focus groups and quantitative surveys were conducted with Latina women and their supporters in Kansas City metropolitan area. RESULTS: FTPNC was challenged by women's immigrant status, lack of healthcare coverage due to immigrant status, and complexity of Medicaid application. PNCU was challenged by the cost of PNC when diagnosed with gestational diabetes, lack of healthcare coverage, PNC literacy, late access to gynecologists/obstetricians, inadequate interpretation services, transportation, and mental health distress. Meanwhile, FTPNC was facilitated by social support and connectedness. PNCU was facilitated by Spanish-proficient providers and interpreters, effective nonverbal communication and education techniques, and pregnancy prevention access and education. CONCLUSIONS FOR PRACTICE: Results from this study highlight important targets to improve PNC for Latina women. Participants called for various types of support to address identified challenges, ranging from information on social media about PNC services to broader efforts such as building trust from the community toward PNC providers and making PNC services affordable for women with gestational diabetes.

Comparative Effectiveness Research using Bayesian Adaptive Designs for Rare Diseases: Response Adaptive Randomization Reusing Participants.

Slow accrual rate is a major challenge in clinical trials for rare diseases and is identified as the most frequent reason for clinical trials to fail. This challenge is amplified in comparative effectiveness research where multiple treatments are compared to identify the best treatment. Novel efficient clinical trial designs are in urgent need in these areas. Our proposed response adaptive randomization (RAR) reusing participants trial design mimics the real-world clinical practice that allows patients to switch treatments when desired outcome is not achieved. The proposed design increases efficiency by two strategies: 1) Allowing participants to switch treatments so that each participant can have more than one observation and hence it is possible to control for participant specific variability to increase statistical power; and 2) Utilizing RAR to allocate more participants to the promising arms such that ethical and efficient studies will be achieved. Extensive simulations were conducted and showed that, compared with trials where each participant receives one treatment, the proposed participants reusing RAR design can achieve comparable power with a smaller sample size and a shorter trial duration, especially when the accrual rate is low. The efficiency gain decreases as the accrual rate increases.

The Effects of Opt-out vs Opt-in Tobacco Treatment on Engagement, Cessation, and Costs: A Randomized Clinical Trial.

Importance: Tobacco use causes 7 million deaths per year; most national guidelines require people who use tobacco to opt in to care by affirming they are willing to quit. Use of medications and counseling is low even in advanced economy countries. Objective: To evaluate the efficacy of opt-out care vs opt-in care for people who use tobacco. Design, Setting, and Participants: In Changing the Default (CTD), a Bayesian adaptive population-based randomization trial, eligible patients were randomized into study groups, treated according to group assignment, and debriefed and consented for participation at 1-month follow-up. A total of 1000 adult patients were treated at a tertiary care hospital in Kansas City. Patients were randomized from September 2016 to September 2020; final follow-up was in March 2021. Interventions: At bedside, counselors screened for eligibility, conducted baseline assessment, randomized patients to study group, and provided opt-out care or opt-in care. Counselors and medical staff provided opt-out patients with inpatient nicotine replacement therapy, prescriptions for postdischarge medications, a 2-week medication starter kit, treatment planning, and 4 outpatient counseling calls. Patients could opt out of any or all elements of care. Opt-in patients willing to quit were offered each element of treatment described previously. Opt-in patients who were unwilling to quit received motivational counseling. Main Outcomes and Measures: The main outcomes were biochemically verified abstinence and treatment uptake at 1 month after randomization. Results: Of a total of 1000 eligible adult patients who were randomized, most consented and enrolled (270 [78%] of opt-in patients; 469 [73%] of opt-out patients). Adaptive randomization assigned 345 (64%) to the opt-out group and 645 (36%) to the opt-in group. The mean (SD) age at enrollment was 51.70 (14.56) for opt-out patients and 51.21 (14.80) for opt-out patients. Of 270 opt-in patients, 123 (45.56%) were female, and of 469 opt-out patients, 226 (48.19%) were female. Verified quit rates for the opt-out group vs the opt-in group were 22% vs 16% at month 1 and 19% vs 18% at 6 months. The Bayesian posterior probability that opt-out care was better than opt-in care was 0.97 at 1 month and 0.59 at 6 months. Treatment use for the opt-out group vs the opt-in group was 60% vs 34% for postdischarge cessation medication (bayesian posterior probability of 1.0), and 89% vs 37% for completing at least 1 postdischarge counseling call (bayesian posterior probability of 1.0). The incremental cost-effectiveness ratio was $678.60, representing the cost of each additional quit in the opt-out group. Conclusions and Relevance: In this randomized clinical trial, opt-out care doubled treatment engagement and increased quit attempts, while enhancing patients' sense of agency and alliance with practitioners. Stronger and longer treatment could increase cessation. Trial Registration: ClinicalTrials.gov Identifier: NCT02721082.

The value of a two-armed Bayesian response adaptive randomization trial.

We investigate the value of a two-armed Bayesian response adaptive randomization (RAR) design to investigate early preterm birth rates of high versus low dose of docosahexaenoic acid during pregnancy. Unexpectedly, the COVID-19 pandemic forced recruitment to pause at 1100 participants rather than the planned 1355. The difference in power between number of participants at the pause and planned was 87% and 90% respectively. We decided to stop the study. This paper describes how the RAR was used to execute the study. The value of RAR in two-armed studies is quite high and their use in the future is promising.

DHA Supplementation During Pregnancy Enhances Maternal Vagally Mediated Cardiac Autonomic Control in Humans.

BACKGROUND: DHA is an essential omega-3 (ω-3; n-3) fatty acid that has well-established benefits for the fetus. DHA also has the potential to influence the health of the mother, but this area is understudied. OBJECTIVES: The objective of this secondary analysis was to determine if DHA was related to maternal heart rate (HR) and heart rate variability (HRV) metrics in a large cohort of pregnant women. METHODS: In the parent trial (1R01HD086001) eligible participants (≥18 y old, English speaking, carrying a singleton pregnancy, 12-20 wk of gestation) were randomly assigned to consume 200 mg/d or 800 mg/d DHA for the duration of their pregnancy (n = 300). Weight, blood pressure, and magnetocardiograms (MCGs) were collected at 32 wk and 36 wk of gestation (n = 221). Measures of HR and HRV in time-, frequency-, and nonlinear-domains were determined from the isolated maternal MCG. Treatment group and timepoint were examined as predictors in association with HR and HRV metrics using random-intercept mixed-effects ANOVA unadjusted and adjusted models accounting for weight and dietary DHA intake. RESULTS: Women receiving the higher dose of DHA (800 mg/d) during pregnancy had lower HR, lower sympathetic index, higher vagally mediated HRV indices, and greater HRV complexity when compared with the women who received the lower dose (200 mg/d; all P < 0.05). All the dose relations remained significant even after controlling for the effect of time, maternal weight, and dietary DHA intake. CONCLUSIONS: DHA supplementation increases vagal tone in pregnant women. Longitudinal studies examining the potential link between DHA, enhanced vagal tone, and reported reduction in early preterm birth are warranted.

A novel Bayesian adaptive design incorporating both primary and secondary endpoints for randomized IIB chemoprevention study of women at increased risk for breast cancer.

BACKGROUND: Our randomized controlled clinical trial will explore the potential of bazedoxifene plus conjugated estrogen to modulate breast tissue-based risk biomarkers as a surrogate for breast cancer risk reduction. This paper investigates the statistical design features of the trial and the rationale for the final choice of its design. Group sequential designs are a popular design approach to allow a trial to stop early for success or futility, potentially saving time and money over a fixed trial design. While Bayesian adaptive designs enjoy the same properties as group sequential designs, they have the added benefit of using prior information as well as inferential interpretation conditional on the data. Whether a frequentist or Bayesian trial, most adaptive designs have interim analyses that allow for early stopping, typically utilizing only the primary endpoint. A drawback to this approach is that the study may not have enough data for adequate comparisons of a single, key secondary endpoint. This can happen, for example, if the secondary endpoint has a smaller effect than the primary endpoint. METHODS: In this paper, we investigate a trial design called two-endpoint adaptive, which stops early only if a criterion is met for primary and secondary endpoints. The approach focuses the final analysis on the primary endpoint but ensures adequate data for the secondary analysis. Our study has two arms with a primary (change in mammographic fibroglandular volume) and secondary endpoint (change in mammary tissue Ki-67). RESULTS: We present operating characteristics including power, trial duration, and type I error rate and discuss the value and risks of modeling Bayesian group sequential designs with primary and secondary endpoints, comparing against alternative designs. The results indicate that the two-endpoint adaptive design has better operating characteristics than competing designs if one is concerned about having adequate information for a key secondary endpoint. DISCUSSION: Our approach balances trial speed and the need for information on the single, key secondary endpoint.

Higher Diet Quality in Latina Women during Pregnancy May Be Associated with Sociodemographic Factors.

Acculturation contributes to low diet quality and can foster health inequities for Latina women during pregnancy. Conversely, nutrition literacy (NL) increases diet quality and could promote health equity. This study assessed the associations between the diet quality, acculturation, and NL of Latina women (n = 99) participating in the Assessment of Docosahexaenoic Acid On Reducing Early Preterm Birth (ADORE) study. Acculturation and nutrition literacy factored together tended to modify diet quality, but this was not statistically significant. Diet quality was associated with acculturation, age, and nativity. Most (76.8%) demonstrated low nutrition literacy. Women who were bicultural and were born in Latin American countries other than Mexico had lower diet quality scores than women who had lower acculturation and were born in Mexico. Women who were 35 years or older had better diet quality than those who were younger. Future studies are needed to explore diet quality differences for pregnant Latina women with high nutrition literacy and high acculturation, as well as for women from the Caribbean, Central and South American countries living in the US, to promote nutrition and maternal health for Latina women.