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.

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.

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.

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.

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.

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.

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.

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.

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.

Early and late preterm birth rates in participants adherent to randomly assigned high dose docosahexaenoic acid (DHA) supplementation in pregnancy

BACKGROUND

Intention-to-treat analyses do not address adherence. Per protocol analyses treat nonadherence as a protocol deviation and assess if the intervention is effective if followed.

OBJECTIVE

To determine the rate of early preterm birth (EPTB, <34 weeks gestation) and preterm birth (PTB, <37 weeks gestation) in participants who adhered to a randomly assigned docosahexaenoic acid (DHA) dose of 1000 mg/day.

STUDY DESIGN

Eleven hundred women with a singleton pregnancy were enrolled before 20-weeks' gestation, provided a capsule with 200 mg/day DHA and randomly assigned to two additional capsules containing a placebo or 800 mg of DHA. In the Bayesian Adaptive Design, new randomization schedules were determined at prespecified intervals. In each randomization, the group with the most EPTB was assigned fewer participants than the other group. Adherence was defined a priori as a postpartum red blood cell phospholipid DHA (RBC-PL-DHA) ≥5.5%.and post hoc as ≥8.0% RBC-PL-DHA, the latter after examination of postpartum RBC-PL-DHA. Bayesian mixture models were fitted for gestational age and dichotomized for EPTB and PTB as a function of baseline RBC-PL-DHA and dose-adherence. Bayesian hierarchical models were also fitted for EPTB by dose adherence and quartiles of baseline RBC-PL-DHA.

RESULTS

Adherence to the high dose using both RBC-PL-DHA cut points resulted in less EPTB compared to 200 mg [Bayesian posterior probability (pp) = 0.93 and 0.92, respectively]. For participants in the two lowest quartiles of baseline DHA status, adherence to the higher dose resulted in lower EPTB (≥5.5% RBC-PL-DHA, quartiles 1 and 2, pp = 0.95 and 0.96; ≥8% RBC-PL-DHA, quartiles 1 and 2, pp = 0.94 and 0.95). Using the Bayesian model, EPTB was reduced by 65%, from 3.45% to 1.2%, using both cut points. Adherence also reduced PTB before 35, 36 and 37 weeks using both cut points (pp ≥ 0.95). In general, performance of the nonadherent subgroup mirrored that of participants assigned to 200 mg.

CONCLUSION

Adherence to high dose DHA reduced EPTB and PTB. The largest effect of adherence on reducing EPTB was observed in women with low baseline DHA levels.

CLINICALTRIALS

gov (NCT02626299).

Docosahexaenoic acid (DHA) intake estimated from a 7-question survey identifies pregnancies most likely to benefit from high-dose DHA supplementation

BACKGROUND

Two randomized trials found women with low blood docosahexaenoic acid (DHA; an omega 3 fatty acid) had fewer early preterm births (<34 weeks gestation) if they were assigned to high dose DHA supplementation, however, there is currently no capacity for clinicians who care for pregnancies to obtain a blood assessment of DHA. Determining a way to identify women with low DHA intake whose risk could be lowered by high dose DHA supplementation is desired.

OBJECTIVE

To determine if assessing DHA intake can identify pregnancies that benefit from high dose DHA supplementation.

STUDY DESIGN

This secondary analysis used birth data from 1310 pregnant women who completed a 7-question food frequency questionnaire (DHA-FFQ) at 16.8 ± 2.5 weeks gestation that is validated to assess DHA status. They were then randomly assigned to a standard (200 mg/day) or high dose (800 or 1000 mg/day) DHA supplement for the remainder of pregnancy. Bayesian logistic regressions were fitted for early preterm birth and preterm birth as a function of DHA intake and assigned DHA dose.

RESULTS

Participants who consumed less than 150 mg/day DHA prior to 20 weeks' gestation (n = 810/1310, 58.1%) had a lower Bayesian posterior probability (pp) of early preterm birth if they were assigned to high dose DHA supplementation (1.4% vs 3.9%, pp = 0.99). The effect on preterm birth (<37 weeks) was also significant (11.3% vs 14.8%, pp = 0.97).

CONCLUSION

The DHA-FFQ can identify pregnancies that will benefit most from high dose DHA supplementation and reduce the risk of preterm birth. The DHA-FFQ is low burden to providers and patients and could be easily implemented in obstetrical practice.

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.

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.

Nutrition Literacy Among Latina/x People During Pregnancy Is Associated With Socioeconomic Position

BACKGROUND

During pregnancy, Latina/x people experience nutrition and nutrition-related health inequities. Nutrition literacy is a potential factor impacted by these inequities. However, the nutrition literacy level of Latina/x people during pregnancy is not well investigated.

OBJECTIVES

The study aimed to assess the nutrition literacy level of Latina/x people during pregnancy and explore the association of nutrition literacy with socioeconomic position.

DESIGN

This was a cross-sectional study of data collected from 2016 to 2018 within the double-blinded, randomized clinical trial Assessment of Docosahexaenoic Acid on Reducing Early Preterm Birth.

PARTICIPANTS/SETTING

A total of 112 Latina/x people during pregnancy from the Kansas City metro area were included in this study.

MAIN OUTCOME MEASURES

Nutrition literacy level assessed between 12 and 20 gestational weeks using the Nutrition Literacy Assessment Instrument, both in English and Spanish.

STATISTICAL ANALYSES PERFORMED

Descriptive measures were used to describe the nutrition literacy level during pregnancy. Multiple logistic regression models were used to examine the association between low nutrition literacy and socioeconomic position, adjusting for age and race.

RESULTS

In this study, most participants demonstrated low nutrition literacy during pregnancy. Those with low nutrition literacy were 2 times more likely to have low annual household income (odds ratio [OR] = 2.74, 95% confidence interval [CI]: 0.99-7.59), 3 times more likely to prefer Spanish as their primary language of communication (OR = 3.03, 95% CI: 0.95-9.67), and 7 times more likely to be uninsured (OR = 7.47; 95% CI: 1.57-35.64).

CONCLUSIONS

Nutrition literacy scores during pregnancy were associated with variables of socioeconomic position. Future research should focus on nutrition literacy associations with health outcomes during pregnancy and interventions to improve the nutrition literacy level of primarily Spanish-speaking people who have low household incomes and are uninsured.

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.

Optimizing a Bayesian hierarchical adaptive platform trial design for stroke patients

BACKGROUND

Platform trials are well-known for their ability to investigate multiple arms on heterogeneous patient populations and their flexibility to add/drop treatment arms due to efficacy/lack of efficacy. Because of their complexity, it is important to develop highly optimized, transparent, and rigorous designs that are cost-efficient, offer high statistical power, maximize patient benefit, and are robust to changes over time.

METHODS

To address these needs, we present a Bayesian platform trial design based on a beta-binomial model for binary outcomes that uses three key strategies: (1) hierarchical modeling of subgroups within treatment arms that allows for borrowing of information across subgroups, (2) utilization of response-adaptive randomization (RAR) schemes that seek a tradeoff between statistical power and patient benefit, and (3) adjustment for potential drift over time. Motivated by a proposed clinical trial that aims to find the appropriate treatment for different subgroup populations of ischemic stroke patients, extensive simulation studies were performed to validate the approach, compare different allocation rules, and study the model operating characteristics.

RESULTS AND CONCLUSIONS

Our proposed approach achieved high statistical power and good patient benefit and was also robust against population drift over time. Our design provided a good balance between the strengths of both the traditional RAR scheme and fixed 1:1 allocation and may be a promising choice for dichotomous outcomes trials investigating multiple subgroups.

Utility of a 7- question online screener for DHA intake

The secondary analyses of two large, recently completed randomized clinical trials of DHA supplementation in pregnancy found that women with a low baseline DHA status benefited from randomization to a higher dose (800 vs 0 and 1000 vs 200 mg/day DHA). To obtain DHA status, it is necessary to obtain a blood sample and conduct an analysis using gas chromatography (GC) or GC-mass spectrometry (GCMS), both barriers to clinics where pregnant women receive advice on nutrition. Participants consuming less than 150 mg/day of DHA at baseline in our recent trial had a lower risk of early preterm birth and preterm birth when assigned to 1000 vs 200 m/day DHA. DHA intake was determined using a 7-question food frequency questionnaire administered by a trained nutritionist. Because the need for trained personnel to administer the questionnaire would be a barrier to implementing this finding in clinical management of pregnancy, the goal of this study was to determine if an online version of the questionnaire could be validly completed without assistance.

Validation of an abbreviated food frequency questionnaire for estimating DHA intake of pregnant women in the United States

Docosahexaenoic acid (DHA) intake was estimated in pregnant women between 12- and 20-weeks' gestation using the National Cancer Institute's (NCI) Diet History Questionnaire-II (DHQ-II) and a 7-question screener designed to capture DHA intake (DHA Food Frequency Questionnaire, DHA-FFQ). Results from both methods were compared to red blood cell phospholipid DHA (RBC-DHA) weight percent of total fatty acids. DHA intake from the DHA-FFQ was more highly correlated with RBC-DHA (r_s=0.528) than the DHQ-II (r_s=0.352). Moreover, the DHA-FFQ allowed us to obtain reliable intake data from 1355 of 1400 participants. The DHQ-II provided reliable intake for only 847 of 1400, because many participants only partially completed it and it was not validated for Hispanic participants. Maternal age, parity, and socioeconomic status (SES) were also significant predictors of RBC-DHA. When included with estimated intake from the DHA-FFQ, the model accounted for 36% of the variation in RBC-DHA.

Unifying the analysis of continuous and categorical measures of weight loss and incorporating group effect: a secondary re-analysis of a large cluster randomized clinical trial using Bayesian approach

Background

Although frequentist paradigm has been the predominant approach to clinical studies for decades, some limitations associated with the frequentist null hypothesis significance testing have been recognized. Bayesian approaches can provide additional insights into data interpretation and inference by deriving posterior distributions of model parameters reflecting the clinical interest. In this article, we sought to demonstrate how Bayesian approaches can improve the data interpretation by reanalyzing the Rural Engagement in Primary Care for Optimizing Weight Reduction (REPOWER).

Methods

REPOWER is a cluster randomized clinical trial comparing three care delivery models: in-clinic individual visits, in-clinic group visits, and phone-based group visits. The primary endpoint was weight loss at 24 months and the secondary endpoints included the proportions of achieving 5 and 10% weight loss at 24 months. We reanalyzed the data using a three-level Bayesian hierarchical model. The posterior distributions of weight loss at 24 months for each arm were obtained using Hamiltonian Monte Carlo. We then estimated the probability of having a higher weight loss and the probability of having greater proportion achieving 5 and 10% weight loss between groups. Additionally, a four-level hierarchical model was used to assess the partially nested intervention group effect which was not investigated in the original REPOWER analyses.

Results

The Bayesian analyses estimated 99.5% probability that in-clinic group visits, compared with in-clinic individual visits, resulted in a higher percent weight loss (posterior mean difference: 1.8%[95% CrI: 0.5,3.2%]), a greater probability of achieving 5% threshold (posterior mean difference: 9.2% [95% CrI: 2.4, 16.0%]) and 10% threshold (posterior mean difference: 6.6% [95% CrI: 1.7, 11.5%]). The phone-based group visits had similar result. We also concluded that including intervention group did not impact model fit significantly.

Conclusions

We unified the analyses of continuous (the primary endpoint) and categorical measures (the secondary endpoints) of weight loss with one single Bayesian hierarchical model. This approach gained statistical power for the dichotomized endpoints by leveraging the information in the continuous data. Furthermore, the Bayesian analysis enabled additional insights into data interpretation and inference by providing posterior distributions for parameters of interest and posterior probabilities of different hypotheses that were not available with the frequentist approach.

Trial registration

ClinicalTrials.gov Identifier NCT02456636; date of registry: May 28, 2015.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-021-01499-0.

Use of pre-enrollment randomization and delayed consent to maximize participation in a clinical trial of opt-in versus opt-out tobacco treatment

Background: Enrollment in smoking cessation trials remain sub-optimal. The aim of this analysis was to determine the effectiveness of a modified Zelen's design in engaging hospitalized patients who smoke in a pragmatic OPT-IN versus OPT-OUT tobacco treatment trial. Methods: At bedside, clinical staff screened smokers for eligibility, randomized eligible into study arms, and delivered the appropriate treatment approach. Study staff called randomized patients at one-month post-discharge, debriefed patients on the study design, and collected consent to participate. We used frequencies and percentages for categorical variables and means and standard deviations for quantitative variables to describe the characteristics of those who consented and were enrolled versus those who did not enroll. We also compared the characteristics of participants who consented and those who were reached and explicitly refused consent at one-month follow-up. We used the Cohen's d measure of effect size to evaluate differences. Results: Of the 1,000 randomized, 741 (74.1%) consented to continue in the study at one-month follow-up. One hundred and twenty-seven (12.7%) refused consent and 132 (13.2%) were unreachable. Cohen's d effect size differences between those who consented/enrolled (n = 741) and those who were not enrolled (n = 259) were negligible (<0.2) for age, gender, race/ethnicity, and most forms of insurance. The effect size was small for Medicaid (0.36), and other public insurance (0.48). After excluding those unreached at 1 month (12.7%), there were medium Cohen's d effect size differences between those who consented to participate (n = 741) and those who explicitly refused (n = 127) with respect to age (0.55) and self-pay or no insurance (0.51). There were small to negligible effect size differences with respect to sex, race/ethnicity, and other forms of health insurance. Conclusions: The modified Zelen's design resulted in successful enrollment of most participants who were initially randomized into the trial, including those not motivated to quit.

Bayesian EMAX model with a mixture of normal distributions for dose-response in clinical trials

When a dose-response relationship is monotonic, the EMAX model has been shown to provide a good empirical fit for designing and analyzing dose-response data across a wide range of pharmaceutical studies. However, the EMAX model has never been applied to a finite mixture distribution. Motivated by a proposal investigating DHA dose effect on preterm birth (PTB, <37 weeks gestation) rate, we developed a Bayesian EMAX mixture model incorporating the three normal components finite mixture model into the EMAX framework. The proposed Bayesian EMAX mixture model analyzes gestational age as a continuous variable, which allows for statistically efficient estimates of PTB rate using various cut point with the same parsimonious model. For example, we can estimate the rate of early PTB (ePTB, <34 weeks gestation), PTB (<37 weeks gestation), and late-term birth (>41 weeks gestation) using the same model. We compared our proposed EMAX mixture model with an EMAX logistic model and an independent doses logistic model for a dichotomized endpoint using extensive simulations. Across the scenarios under consideration, the EMAX mixture model achieved higher power than the EMAX logistic model and the independent doses logistic model in detecting the effect of DHA supplementation on the PTB rate. The EMAX mixture model also resulted in smaller mean squared errors (MSE) in PTB rate estimates.

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.

Higher dose docosahexaenoic acid supplementation during pregnancy and early preterm birth: A randomised, double-blind, adaptive-design superiority trial

BACKGROUND

Several meta analyses have concluded n-3 fatty acids, including docosahexaenoic acid (DHA), reduce early preterm birth (EPB, < 34 weeks), however, the amount of DHA required is unclear. We hypothesized that 1000 mg DHA per day would be superior to 200 mg, the amount in most prenatal supplements.

METHODS

This randomised, multicentre, double-blind, adaptive-design, superiority trial was conducted in three USA medical centres. Women with singleton pregnancies and 12 to 20 weeks gestation were eligible. randomization was generated in SAS® by site in blocks of 4. The planned adaptive design periodically generated allocation ratios favoring the better performing dose. Managing study personnel were blind to treatment until 30 days after the last birth. The primary outcome was EPB by dose and by enrolment DHA status (low/high). Bayesian posterior probabilities (pp) were determined for planned efficacy and safety outcomes using intention-to-treat. The study is registered with ClinicalTrials.gov (NCT02626299) and closed to enrolment.

FINDINGS

Eleven hundred participants (1000 mg, n = 576; 200 mg, n = 524) were enrolled between June 8, 2016 and March 13, 2020 with the last birth September 5, 2020. 1032 (n = 540 and n = 492) were included in the primary analyses. The higher dose had a lower EPB rate [1.7% (9/540) vs 2.4% (12/492), pp=0.81] especially if participants had low DHA status at enrolment [2.0% (5/249) vs 4.1%, (9/219), pp=0.93]. Participants with high enrolment DHA status did not realize a dose effect [1000 mg: 1.4% (4/289); 200 mg: 1.1% (3/271), pp = 0.57]. The higher dose was associated with fewer serious adverse events (maternal: chorioamnionitis, premature rupture of membranes and pyelonephritis; neonatal: feeding, genitourinary and neurologic problems, all pp>0.90).

INTERPRETATION

Clinicians could consider prescribing 1000 mg DHA daily during pregnancy to reduce EPB in women with low DHA status if they are able to screen for DHA.

FUNDING

The National Institutes of Health Child Health and Human Development (NICHD) funded the study. Life's DHA™-S oil, DSM Nutritional Products LLC, Switzerland provided all capsules.

Designing and analyzing clinical trials for personalized medicine via Bayesian models

Patients with different characteristics (e.g., biomarkers, risk factors) may have different responses to the same medicine. Personalized medicine clinical studies that are designed to identify patient subgroup treatment efficacies can benefit patients and save medical resources. However, subgroup treatment effect identification complicates the study design in consideration of desired operating characteristics. We investigate three Bayesian adaptive models for subgroup treatment effect identification: pairwise independent, hierarchical, and cluster hierarchical achieved via Dirichlet Process (DP). The impact of interim analysis and longitudinal data modeling on the personalized medicine study design is also explored. Interim analysis is considered since they can accelerate personalized medicine studies in cases where early stopping rules for success or futility are met. We apply integrated two-component prediction method (ITP) for longitudinal data simulation, and simple linear regression for longitudinal data imputation to optimize the study design. The designs' performance in terms of power for the subgroup treatment effects and overall treatment effect, sample size, and study duration are investigated via simulation. We found the hierarchical model is an optimal approach to identifying subgroup treatment effects, and the cluster hierarchical model is an excellent alternative approach in cases where sufficient information is not available for specifying the priors. The interim analysis introduction to the study design lead to the trade-off between power and expected sample size via the adjustment of the early stopping criteria. The introduction of the longitudinal modeling slightly improves the power. These findings can be applied to future personalized medicine studies with discrete or time-to-event endpoints.

Sliding Scoring of the Glasgow Outcome Scale-Extended as Primary Outcome in Traumatic Brain Injury Trials

The Glasgow Outcome Scale-Extended (GOS-E), an ordinal scale measuring global outcome, is used commonly as the primary outcome measure in clinical trials of traumatic brain injury. Analysis is often based on a dichotomization and thus has inherent statistical limitations, including loss of information related to the collapse of adjacent categories. A fixed dichotomization defines favorable outcome consistently for all subjects, whereas a sliding dichotomy tailors the definition of favorable outcome according to baseline prognosis/severity. Literature indicates that the sliding dichotomy is more statistically efficient than the fixed dichotomy; however, the sliding dichotomy still collapses categories and therefore discards information. We propose an alternative, a sliding scoring system for the GOS-E, intended to address the limitations of the sliding dichotomy. The score is assigned based on the number of levels between the achieved score and the favorable cut-point. The proposed scoring system reflects the magnitude of change, where change is defined according to each subject's baseline prognosis. Because the score is approximately continuous, statistical methods can rely on the normal distribution, both for analysis and study design. Two examples show the corresponding potential for improved power. A sliding score approach allows for quantification of the magnitude of change while still accounting for prognosis. Scientific advantages include increased power and an intuitive interpretation.

An Evaluation of the Cross-Check Principle Using Visual Reinforcement Audiometry, Otoacoustic Emissions, and Tympanometry

Background: Early intervention to reduce the effects of congenital hearing loss requires accurate description of hearing loss. In pediatric audiology, a cross-check principle is used to compare behavioral and physiological tests.

Purpose: The purpose of this study was to investigate the correspondence of visual reinforcement audiometry (VRA) minimal response levels (MRLs), otoacoustic emissions (OAEs), tympanometry, and VRA test reliability to determine the odds of obtaining the expected cross-check results. We hypothesized that (1) when MRLs were within normal limits (WNL), OAEs would be present; (2) in the event of normal MRLs and absent OAEs, tympanograms would be abnormal; and (3) in the event of elevated MRLs and present OAEs, the tester's confidence in the MRLs would be judged to be only fair, rather than good.

Research Design: This was a retrospective study.

Study Sample: A previous study provided data from 993 infants who had diagnostic audiologic evaluations at 8–12 mo of age.

Data Collection and Analysis: The data were analyzed to compare VRA MRLs with OAE signal-to-noise ratios at 1, 2, and 4 kHz. Odds ratios and 95% confidence intervals were calculated to test the three hypotheses related to the correspondence among MRLs, OAEs, tympanometry, and the reliability of MRLs.

Results: The probability that OAEs would be present when MRLs were WNL varied from 12 to 26 to 1, depending on the test frequency. When OAEs were absent in the presence of normal MRLs, the odds of abnormal tympanometry varied from 5 to 10 to 1, depending on the test frequency. When MRLs were elevated (>20 dB HL), the odds suggested that examiners judged the MRLs at 1 and 2 kHz to lack reliability.

Conclusion: The results suggest that the cross-check principle is effective when employing VRA, OAE, and tympanometry to rule out or determine the degree, type, and configuration of hearing loss in infants.

Comparison of hierarchical EMAX and NDLM models in dose-response for early phase clinical trials

Background

Phase II clinical trials primarily aim to find the optimal dose and investigate the relationship between dose and efficacy relative to standard of care (control). Therefore, before moving forward to a phase III confirmatory trial, the most effective dose is needed to be identified.

Methods

The primary endpoint of a phase II trial is typically a binary endpoint of success or failure. The EMAX model, ubiquitous in pharmacology research, was fit for many compounds and described the data well, except for a single compound, which had nonmonotone dose–response (Thomas et al., Stat Biopharmaceutical Res. 6:302-317 2014). To mitigate the risk of nonmonotone dose response one of the alternative options is a Bayesian hierarchical EMAX model (Gajewski et al., Stat Med. 38:3123-3138 2019). The hierarchical EMAX adapts to its environment.

Results

When the dose-response curve is monotonic it enjoys the efficiency of EMAX. When the dose-response curve is non-monotonic the additional random effect hyperprior makes the hierarchical EMAX model more adjustable and flexible. However, the normal dynamic linear model (NDLM) is a useful model to explore dose-response relationships in that the efficacy at the current dose depends on the efficacy of the previous dose(s). Previous research has compared the EMAX to the hierarchical EMAX (Gajewski et al., Stat Med. 38:3123-3138 2019) and the EMAX to the NDLM (Liu et al., BMC Med Res Method 17:149 2017), however, the hierarchical EMAX has not been directly compared to the NDLM.

Conclusions

The focus of this paper is to compare these models and discuss the relative merit for each of their uses for an ongoing early phase dose selection study.

Improving the efficiency of clinical trials by standardizing processes for Investigator Initiated Trials

Early phase clinical trials are the first step in testing new medications and therapeutics developed by clinical and biomedical investigators. These trials aim to find a safe dose of a newly targeted drug (phase I) or find out more about the side effects and early signals of treatment efficacy (phase II). In a research institute, many biomedical investigators in oncology are encouraged to initiate such trials early in their careers as part of developing their research portfolio. These investigator-initiated trials (IITs) are funded internally by the University of Kansas Cancer Center or partially funded by pharmaceutical companies. As financial, administrative, and practical considerations play an essential role in the successful completion of IITs, it is imperative to efficiently allocate resources to plan, design, and execute these studies within the allotted time. This manuscript describes monitoring tools and processes to improve the efficiency, cost-effectivness, and reliability of IITs. The contributions of this team to processes such as: participant recruitment, feasibility analysis, clinical trial design, accrual monitoring, data management, interim analysis support, and final analysis and reporting are described in detail. This manuscript elucidates how, through the aid of technology and dedicated personnel support, the efficiency of IIT-related processes can be improved. Early results of these initiatives look promising, and the Biostatistics and Informatics team intends to continue fostering innovative methodologies to enhance cancer research by improving the efficiency of IITs.

Abstract PD3-06: Biomarker modulation by bazedoxifene and conjugated estrogen (Duavee®) in women at high risk for development of breast cancer

Agents which both reduce risk for development of breast cancer and relieve vasomotor symptoms are likely to have good uptake and adherence. We conducted a pilot study with 6 months of the tissue selective estrogen complex bazedoxifene (20 mg) and conjugated estrogen (0.45 mg) (Duavee®) to assess feasibility and effects on biomarkers. Risk biomarkers for postmenopausal breast cancer included fully automated mammographic volumetric density (Volpara®), benign breast tissue Ki-67, and serum levels of progesterone, IGF-1 and IGFBP3, bioavailable estradiol and testosterone. Exploratory biomarkers included selected estrogen and progesterone responsive gene expression in benign breast tissue. 28 peri- and post-menopausal women at increased risk for breast cancer were enrolled; 13 in Cohort A with baseline Ki-67 &lt; 1% and 15 in Cohort B with baseline Ki-67 of 1-4%. All completed the study with &gt; 85% drug adherence. An improvement in median hot flash score from 15 at baseline to 0 at 6 months, and menopause specific quality of life total, vasomotor and sexual domain scores were also observed (p&lt; 0.001). Significant changes in risk biomarkers, uncorrected for multiple comparisons, were a decrease in mammographic fibroglandular volume (p=0.043); decreases in serum progesterone, bioavailable testosterone, and IGF-1 (p&lt;0.01); and for women from Cohort B, a reduction in Ki-67 (p=0.017) despite an increase in serum bioavailable estradiol. Unsupervised cluster analysis of RT-qPCR results indicated two clusters with differences in change in early estrogen response genes including ERS1, TFF1, GREB1a, PGR and AREG. The 10 women in one cluster tended to have increased expression of two or more of early estrogen response genes, but not increased expression of CCND1 (cyclin D1) or genes downstream of activated progesterone receptor such as STAT5a, PdK4, and STK. A trend towards decrease in several genes with predominant stromal expression implicated in breast cancer development including FASN, LEP, CXCL12, SDF1a and B, and CYP19A1 was observed. The 17 women in cluster 2 by contrast exhibited predominately decreased expression of early estrogen response genes. Given the favorable effects on vasomotor symptoms and risk biomarkers, a placebo-controlled Phase IIB trial is warranted.

This study was supported in part by grants from the Breast Cancer Research Foundation (BCRF-16-049, BCRF-17-049, BCRF-18-049); and an NIH Clinical and Translational Science Award grant (UL1 TR000001, formerly UL1RR033179) awarded to the University of Kansas Medical Center, and an internal clinical pilot grant program of the KUMC Research Institute.

Citation Format: Carol J Fabian, Lauren Nye, Teresa A Phillips, Onalisa Winblad, Carola M Zalles, Christy R Hagan, Merit L Goodman, Byron J Gajewski, Devin C Koestler, Prabhakar Chalise, Bruce F Kimler. Biomarker modulation by bazedoxifene and conjugated estrogen (Duavee®) in women at high risk for development of breast cancer [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr PD3-06.

Effect of Bazedoxifene and Conjugated Estrogen (Duavee) on Breast Cancer Risk Biomarkers in High-Risk Women: A Pilot Study

Interventions that relieve vasomotor symptoms while reducing risk for breast cancer would likely improve uptake of chemoprevention for perimenopausal and postmenopausal women. We conducted a pilot study with 6 months of the tissue selective estrogen complex bazedoxifene (20 mg) and conjugated estrogen (0.45 mg; Duavee) to assess feasibility and effects on risk biomarkers for postmenopausal breast cancer. Risk biomarkers included fully automated mammographic volumetric density (Volpara), benign breast tissue Ki-67 (MIB-1 immunochemistry), and serum levels of progesterone, IGF-1, and IGFBP3, bioavailable estradiol and testosterone. Twenty-eight perimenopausal and postmenopausal women at increased risk for breast cancer were enrolled: 13 in cohort A with baseline Ki-67 < 1% and 15 in cohort B with baseline Ki-67 of 1% to 4%. All completed the study with > 85% drug adherence. Significant changes in biomarkers, uncorrected for multiple comparisons, were a decrease in mammographic fibroglandular volume (P = 0.043); decreases in serum progesterone, bioavailable testosterone, and IGF-1 (P < 0.01), an increase in serum bioavailable estradiol (P < 0.001), and for women from cohort B a reduction in Ki-67 (P = 0.017). An improvement in median hot flash score from 15 at baseline to 0 at 6 months, and menopause-specific quality-of-life total, vasomotor, and sexual domain scores were also observed (P < 0.001). Given the favorable effects on risk biomarkers and patient reported outcomes, a placebo-controlled phase IIB trial is warranted.

Accrual Prediction Program: A web-based clinical trials tool for monitoring and predicting accrual for early-phase cancer studies

BACKGROUND

Monitoring subject recruitment is key to the success of a clinical trial. Accordingly, researchers have developed accrual-monitoring tools to support the design and conduct of trials. At an institutional level, delays in identifying studies with high risk of accrual failure can lead to inefficient and costly trials with little chances of meeting study objectives. Comprehensive accrual monitoring is necessary to the success of the research enterprise.

METHODS

This article describes the design and implementation of the University of Kansas Cancer Center Accrual Prediction Program, a web-based platform was developed to support comprehensive accrual monitoring and prediction for all active clinical trials. The Accrual Prediction Program provides information on accrual, including the predicted completion date, predicted number of accrued subjects during the pre-specified accrual period, and the probability of achieving accrual targets. It relies on a Bayesian accrual prediction model to combine protocol information with real-time trial enrollment data and disseminates results via web application.

RESULTS

First released in 2016, the Accrual Prediction Program summarizes enrollment information for active studies categorized by various trial attributes. The web application supports real-time evidence-based decision making for strategic resource allocation and study management of over 120 ongoing clinical trials at the University of Kansas Cancer Center.

CONCLUSION

The Accrual Prediction Program makes accessing comprehensive accrual information manageable at an institutional level. Cancer centers or even entire institutions can reproduce the Accrual Prediction Program to achieve real-time comprehensive monitoring and prediction of subject accrual to aid investigators and administrators in the design, conduct, and management of clinical trials.

Using Adaptive Designs to Avoid Selecting the Wrong Arms in Multiarm Comparative Effectiveness Trials

Limited resources are a challenge when planning comparative effectiveness studies of multiple promising treatments, often prompting study planners to reduce the sample size to meet the financial constraints. The practical solution is often to increase the efficiency of this sample size by selecting a pair of treatments among the pool of promising treatments before the clinical trial begins. The problem with this approach is that the investigator may inadvertently leave out the most beneficial treatment. This paper demonstrates a possible solution to this problem by using Bayesian adaptive designs. We use a planned comparative effectiveness clinical trial of treatments for sialorrhea in amyotrophic lateral sclerosis as an example of the approach. Rather than having to guess at the two best treatments to compare based on limited data, we suggest putting more arms in the trial and letting response adaptive randomization (RAR) determine better arms. To ground this study relative to previous literature we first compare RAR, adaptive equal randomization (ER), arm(s) dropping, and a fixed design. Given the goals of this trial we demonstrate that we may avoid 'type III errors' - inadvertently leaving out the best treatment - with little loss in power compared to a two-arm design, even when choosing the correct two arms for the two-armed design. There are appreciable gains in power when the two arms are prescreened at random.

Bayesian hierarchical EMAX model for dose-response in early phase efficacy clinical trials

A primary goal of a phase II dose-ranging trial is to identify a correct dose before moving forward to a phase III confirmatory trial. A correct dose is one that is actually better than control. A popular model in phase II is an independent model that puts no structure on the dose-response relationship. Unfortunately, the independent model does not efficiently use information from related doses. One very successful alternate model improves power using a pre-specified dose-response structure. Past research indicates that EMAX models are broadly successful and therefore attractive for designing dose-response trials. However, there may be instances of slight risk of nonmonotone trends that need to be addressed when planning a clinical trial design. We propose to add hierarchical parameters to the EMAX model. The added layer allows information about the treatment effect in one dose to be "borrowed" when estimating the treatment effect in another dose. This is referred to as the hierarchical EMAX model. Our paper compares three different models (independent, EMAX, and hierarchical EMAX) and two different design strategies. The first design considered is Bayesian with a fixed trial design, and it has a fixed schedule for randomization. The second design is Bayesian but adaptive, and it uses response adaptive randomization. In this article, a randomized trial of patients with severe traumatic brain injury is provided as a motivating example.

The Kansas University DHA Outcomes Study (KUDOS) clinical trial: long-term behavioral follow-up of the effects of prenatal DHA supplementation

BACKGROUND

Docosahexaenoic acid (DHA) is a long-chain polyunsaturated fatty acid that has been linked to improved vision and cognition in postnatal feeding studies and has been consistently associated with reduction of early preterm birth in prenatal supplementation trials. This is a report of the first long-term follow-up of infants from mothers receiving prenatal DHA supplementation in a US cohort.

OBJECTIVE

Our objective was to evaluate the efficacy of the prenatal supplementation on both global and granular longitudinal assessments of cognitive and behavioral development.

METHODS

In a randomized double-blind clinical trial, mothers received either 600 mg/d of DHA or a placebo beginning at 14.5 weeks of gestation and capsules were provided until delivery. Children from those pregnancies were followed by cognitive and behavioral assessments administered from 10 mo through 6 y of age. From 301 mothers in the initial study, ∼200 infants completed the longitudinal schedule.

RESULTS

Although this intervention had been shown to reduce high-risk pregnancies and improve visual attention in infants during the first year, only a few positive long-term effects of prenatal DHA supplementation emerged from analyses of this follow-up. Increases in maternal blood DHA during pregnancy were related to verbal and full scale intelligence quotient (IQ) scores at 5 and 6 y, but these effects disappeared after controlling for SES. Maternal blood DHA concentrations at delivery were unrelated to outcomes, although maternal DHA at enrollment was related to productive vocabulary at 18 mo.

CONCLUSIONS

Although prenatal DHA supplementation substantially reduced early preterm birth and improved visual attention in infancy in this sample, no consistent long-term benefits were observed into childhood. Increases in maternal blood DHA concentration in pregnancy were related to higher IQs but this effect was confounded with SES and disappeared when SES was statistically controlled. This trial was registered at http://www.clinicaltrials.gov as NCT00266825 and NCT02487771.

Using an Anchor to Improve Linear Predictions with Application to Predicting Disease Progression

Linear models are some of the most straightforward and commonly used modelling approaches. Consider modelling approximately monotonic response data arising from a time-related process. If one has knowledge as to when the process began or ended, then one may be able to leverage additional assumed data to reduce prediction error. This assumed data, referred to as the "anchor," is treated as an additional data-point generated at either the beginning or end of the process. The response value of the anchor is equal to an intelligently selected value of the response (such as the upper bound, lower bound, or 99^th percentile of the response, as appropriate). The anchor reduces the variance of prediction at the cost of a possible increase in prediction bias, resulting in a potentially reduced overall mean-square prediction error. This can be extremely effective when few individual data-points are available, allowing one to make linear predictions using as little as a single observed data-point. We develop the mathematics showing the conditions under which an anchor can improve predictions, and also demonstrate using this approach to reduce prediction error when modelling the disease progression of patients with amyotrophic lateral sclerosis.

Using automated electronic medical record data extraction to model ALS survival and progression

BACKGROUND

To assess the feasibility of using automated capture of Electronic Medical Record (EMR) data to build predictive models for amyotrophic lateral sclerosis (ALS) outcomes.

METHODS

We used an Informatics for Integrating Biology and the Bedside search discovery tool to identify and extract data from 354 ALS patients from the University of Kansas Medical Center EMR. The completeness and integrity of the data extraction were verified by manual chart review. A linear mixed model was used to model disease progression. Cox proportional hazards models were used to investigate the effects of BMI, gender, and age on survival.

RESULTS

Data extracted from the EMR was sufficient to create simple models of disease progression and survival. Several key variables of interest were unavailable without including a manual chart review. The average ALS Functional Rating Scale - Revised (ALSFRS-R) baseline score at first clinical visit was 34.08, and average decline was - 0.64 per month. Median survival was 27 months after first visit. Higher baseline ALSFRS-R score and BMI were associated with improved survival, higher baseline age was associated with decreased survival.

CONCLUSIONS

This study serves to show that EMR-captured data can be extracted and used to track outcomes in an ALS clinic setting, potentially important for post-marketing research of new drugs, or as historical controls for future studies. However, as automated EMR-based data extraction becomes more widely used there will be a need to standardize ALS data elements and clinical forms for data capture so data can be pooled across academic centers.

Dose-response relationship between docosahexaenoic acid (DHA) intake and lower rates of early preterm birth, low birth weight and very low birth weight

As previously reported, intention-to-treat findings from our phase III randomized clinical trial found that a supplement of 600 mg docosahexaenoic acid (DHA)/day during the last half of pregnancy reduced the incidence of early preterm birth (ePTB, <34 weeks gestation) and very low birth weight (VLBW < 1500 g) offspring. Given the potentially immense clinical significance of these findings, the goal of this secondary analysis was to (1) identify maternal characteristics related with capsule intake (i.e. DHA dose exposure) and (2) determine if DHA dose was associated with low (<2500 g) and very low birth weight after controlling for any relevant maternal characteristics. Three hundred forty-five pregnant mothers were recruited from hospitals in the Kansas City metropolitan area between 2006 and 2011. Most participants (n = 299) were from the phase III trial mentioned above, but we also included 46 participants from a second smaller, randomized trial that utilized an identical intervention design and was conducted concurrent to the larger trial. Both trials assigned participants to either 3 daily capsules of vegetable oil without DHA (n = 169) or 3 daily capsules of 200 mg DHA each (n = 176). Total capsules consumed was recorded by pharmacy supervised capsule count or participant self-report when needed. Maternal age, education, race and gestational age at delivery as well as infant birth weight were available for both trials. A Bayesian linear model indicated capsule intake increased with maternal age (p = 0.0100) and years of education (p = 0.0002). A Bayesian bivariate mixture-model associated capsule intake with simultaneous lower probability of ePTB, low birth weight (LBW, <2500 g) and VLBW (p = 0.0327). This, in conjunction with the positive findings in the clinical trial, support the need for future research to examine intervention methods to improve capsule compliance strategies in younger and less educated mothers.

Comments on "Tutorial on statistical considerations on subgroup analysis in confirmatory clinical trials"

This paper is the letter to the editor regarding several comments on 'Tutorial on statistical considerations on subgroup analysis in confirmatory clinical trials.'

Using an onset-anchored Bayesian hierarchical model to improve predictions for amyotrophic lateral sclerosis disease progression

BACKGROUND

Amyotrophic Lateral Sclerosis (ALS), also known as Lou Gehrig's disease, is a rare disease with extreme between-subject variability, especially with respect to rate of disease progression. This makes modelling a subject's disease progression, which is measured by the ALS Functional Rating Scale (ALSFRS), very difficult. Consider the problem of predicting a subject's ALSFRS score at 9 or 12 months after a given time-point.

METHODS

We obtained ALS subject data from the Pooled Resource Open-Access ALS Clinical Trials Database, a collection of data from various ALS clinical trials. Due to the typical linearity of the ALSFRS, we consider several Bayesian hierarchical linear models. These include a mixture model (to account for the two potential classes of "fast" and "slow" ALS progressors) as well as an onset-anchored model, in which an additional artificial data-point, using time of disease onset, is utilized to improve predictive performance.

RESULTS

The onset-anchored model had a drastically reduced posterior predictive mean-square-error distributions, when compared to the Bayesian hierarchical linear model or the mixture model under a cross-validation approach. No covariates, other than time of disease onset, consistently improved predictive performance in either the Bayesian hierarchical linear model or the onset-anchored model.

CONCLUSIONS

Augmenting patient data with an additional artificial data-point, or onset anchor, can drastically improve predictive modelling in ALS by reducing the variability of estimated parameters at the cost of a slight increase in bias. This onset-anchored model is extremely useful if predictions are desired directly after a single baseline measure (such as at the first day of a clinical trial), a feat that would be very difficult without the onset-anchor. This approach could be useful in modelling other diseases that have bounded progression scales (e.g. Parkinson's disease, Huntington's disease, or inclusion-body myositis). It is our hope that this model can be used by clinicians and statisticians to improve the efficacy of clinical trials and aid in finding treatments for ALS.

Two brief valid measures of therapeutic alliance in counseling for tobacco dependence

Behavioral counseling is effective for smoking cessation and the psychotherapy literature indicates therapeutic alliance is key to counseling effectiveness. However, no tobacco-counseling specific measures of alliance exist that are suitable in most tobacco counseling contexts. This hinders assessment of counseling components in research and clinical practice. Based on the Working Alliance Inventory, and external expert review, we developed two alliance instruments: the 12-item and 3-item Working Alliance Inventory for Tobacco (WAIT-12 and WAIT-3). Two samples of 226 daily smokers via Amazon Mechanical Turk completed measures including demographics, tobacco characteristics, working alliance scales, and quit attempts. Both WAIT-12 and WAIT-3 had good to excellent internal consistency (0.92 and 0.88 for the WAIT-3 and 0.96 for the WAIT-12). The WAIT-12 1-factor model indicated poor fit (CFI=0.83, TLI=0.79, RMSEA=0.19, SRMR=0.09). The WAIT-12 3-factor model (CFI=0.94, TLI=0.93, RMSEA=0.11, SRMR=0.04) was indicative of acceptable fit. Both the WAIT-12 and the WAIT-3 were significantly associated with participants' self-reported cigarettes per day, quit attempts, and cessation. Initial validation of the WAIT-12 and WAIT-3 indicates they are psychometrically sound measures of tobacco dependence counseling alliance. The WAIT-3 provides brevity; it can be administered in under 1min. The WAIT-12 allows for assessment of specific components of therapeutic alliance. Overall, these instruments should allow for better measurement of alliance in clinical services and research.

Changing the default for tobacco-cessation treatment in an inpatient setting: study protocol of a randomized controlled trial

BACKGROUND

Most health care providers do not treat tobacco dependence routinely. This may in part be due to the treatment "default." Current treatment guidelines recommend that providers (1) ask patients if they are willing to quit and (2) provide cessation-focused medications and counseling only to smokers who state that they are willing to quit. The default is that patients have to "opt in" to receive cessation assistance: providers ask smokers if they are willing to quit, and only offer medications and cessation support to those who say "yes." This drastically limits the reach of cessation services because, at any given encounter, only one in three smokers say that they are ready to quit. The objective of this study is to determine the impact of providing all smokers with tobacco-cessation treatment unless they refuse it (OPT OUT) versus current practice-screening for readiness and only offering treatment to smokers who say they are ready to quit (OPT IN).

METHODS

This individually randomized clinical trial is conducted in a tertiary-care hospital. We will conduct the trial among up to 1000 randomly selected hospitalized smokers to determine the population impact of changing the treatment default, identify mediators of outcome, and determine the cost-effectiveness of this new, highly proactive approach. This is a population-based study that targets an endpoint of vital interest; applies minimal eligibility criteria to broaden generalizability; and utilizes hospital staff for interventions to ensure long-term sustainability. The study employs delayed consent and an innovative Bayesian adaptive design to evaluate a major shift in our approach to care. If effective, this change would expand the reach of tobacco-cessation treatment from 30% to 100% of smokers.

DISCUSSION

Regardless of outcome, the trial will provide a model of how to alter and evaluate the impact of health care defaults. If OPT OUT proves to be more effective, it will expand the population eligible for cessation treatment by over 300%. It will also simplify the tobacco-cessation treatment algorithm, and relieve busy health care providers of the burden of evaluating readiness to quit.

TRIAL REGISTRATION

Clinical Trials Registration, ID: NCT02721082 . Registered on 22 March 2016.

Personalized Medicine Enrichment Design for DHA Supplementation Clinical Trial

Personalized medicine aims to match patient subpopulation to the most beneficial treatment. The purpose of this study is to design a prospective clinical trial in which we hope to achieve the highest level of confirmation in identifying and making treatment recommendations for subgroups, when the risk levels in the control arm can be ordered. This study was motivated by our goal to identify subgroups in a DHA (docosahexaenoic acid) supplementation trial to reduce preterm birth (gestational age<37 weeks) rate. We performed a meta-analysis to obtain informative prior distributions and simulated operating characteristics to ensure that overall Type I error rate was close to 0.05 in designs with three different models: independent, hierarchical, and dynamic linear models. We performed simulations and sensitivity analysis to examine the subgroup power of models and compared results to a chi-square test. We performed simulations under two hypotheses: a large overall treatment effect and a small overall treatment effect. Within each hypothesis, we designed three different subgroup effects scenarios where resulting subgroup rates are linear, flat, or nonlinear. When the resulting subgroup rates are linear or flat, dynamic linear model appeared to be the most powerful method to identify the subgroups with a treatment effect. It also outperformed other methods when resulting subgroup rates are nonlinear and the overall treatment effect is big. When the resulting subgroup rates are nonlinear and the overall treatment effect is small, hierarchical model and chi-square test did better. Compared to independent and hierarchical models, dynamic linear model tends to be relatively robust and powerful when the control arm has ordinal risk subgroups.

Assessment of DHA on reducing early preterm birth: the ADORE randomized controlled trial protocol

BACKGROUND

Preterm birth contributes to 0.5 million deliveries in the United States (one of eight pregnancies) and poses a huge burden on public health with costs in the billions. Of particular concern is that the rate of earliest preterm birth (<34 weeks) (ePTB), which has decreased little since 1990 and has the greatest impact on the overall infant mortality, resulting in the greatest cost to society. Docosahexaenoic acid (DHA) supplementation provides a potential high yield, low risk strategy to reduce early preterm delivery in the US by up to 75%. We propose a Phase III Clinical Trial (randomized to low or high dose DHA, double-blinded) to examine the efficacy and safety of high dose DHA supplementation to reduce ePTB. We also plan for a secondary pregnancy efficacy analysis to determine if there is a subset of pregnancies most likely to benefit from DHA supplementation.

METHODS

Between 900 and 1200 pregnant women who are ≥ 18 years old and between 12 and 20 weeks gestation will be recruited from three trial experienced academic medical institutions. Participants will be randomly assigned to two daily capsules of algal oil (totaling 800 mg DHA) or soybean and corn oil (0 mg DHA). Both groups will receive a commercially available prenatal supplement containing 200 mg DHA. Therefore, the experimental group will receive 1000 mg DHA/d and the control group 200 mg DHA/d. We will then employ a novel Bayesian response adaptive randomization design that assigns more subjects to the "winning" group and potentially allows for substantially smaller sample size while providing a stronger conclusion regarding the most effective group. The study has an overall Type I error rate of 5% and a power of 90%. Participants are followed throughout pregnancy and delivery for safety and delivery outcomes.

DISCUSSION

We hypothesize that DHA will decrease the frequency of ePTB <34 weeks. Reducing ePTB is clinically important as these earliest preterm deliveries carry the highest risk of neonatal morbidity, as well as contribute significant stress for families and post a large societal burden.

TRIAL REGISTRATION

This trial was registered with ClinicalTrials.gov (identifier: NCT02626299 ) on December 8, 2015. Additional summary details may be found in Table 1.

Commitment and capacity for providing evidence-based tobacco treatment in US drug treatment facilities

BACKGROUND

Although people with mental illness, including substance use disorders, consume 44% of cigarettes in the United States, few facilities provide tobacco treatment. This study assesses staff- and facility-level drivers of tobacco treatment in substance use treatment.

METHODS

Surveys were administered to 405 clinic directors selected from a comprehensive inventory of 3800 US outpatient facilities. The main outcome was the validated 7-item Index of Tobacco Treatment Quality. Other measures included the validated Tobacco Treatment Commitment Scale and indicators of facility resources for providing tobacco treatment.

RESULTS

Stepwise model selection was used to determine the relationship between capacity/resources and treatment quality. The final model retained 7 items and had good fit (adjusted R² = 0.43). Four capacities significantly predicted treatment quality. Structural equation modeling (SEM) was used to test the impact of staff commitment on treatment quality; the model had good fit and the relationship was significant (comparative fit index [CFI] = 0.951, root mean square error of approximation [RMSEA] = 0.054). Adding the 7 capacity/resources maintained similar model fit (CFI = 0.922, RMSEA = 0.053). Staff commitment was slightly strengthened in this model, with a rise in parameter estimate from 0.449 to 0.560. All resource/capacity items were also significant predictors of treatment quality; the strongest was receiving training in how to provide tobacco treatment (0.360), followed by dedicated staff time (0.279) and having a policy that requires staff to offer treatment (0.272).

CONCLUSIONS

Staff commitment to providing tobacco treatment was the strongest predictor of tobacco treatment quality, followed by resources for providing treatment. Interventions to change staff attitudes and improve resources for tobacco treatment have the strongest potential for improving quality of care.

Prenatal DHA supplementation and infant attention

BACKGROUND

Results of randomized trials on the effects of prenatal docosahexaenoic acid (DHA) on infant cognition are mixed, but most trials have used global standardized outcomes, which may not be sensitive to effects of DHA on specific cognitive domains.

METHODS

Women were randomized to 600 mg/d DHA or a placebo for the last two trimesters of pregnancy. Infants of these mothers were then followed on tests of visual habituation at 4, 6, and 9 mo of age.

RESULTS

DHA supplementation did not affect look duration or habituation parameters but infants of supplemented mothers maintained high levels of sustained attention (SA) across the first year; SA declined for the placebo group. The supplemented group also showed significantly reduced attrition on habituation tasks, especially at 6 and 9 mo.

CONCLUSION

The findings support with the suggestion that prenatal DHA may positively affect infants' attention and regulation of state.