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.

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.

PROpwr: a Shiny R application to analyze patient-reported outcomes data and estimate power.

Patient Reported Outcomes (PROs) are widely used in quality of life (QOL) studies, health outcomes research, and clinical trials. The importance of PRO has been advocated by health authorities. We propose this R shiny web application, PROpwr, that estimates power for two-arm clinical trials with PRO measures as endpoints using Item Response Theory (GRM: Graded Response Model) and simulations. PROpwr also supports the analysis of PRO data for convenience of estimating the effect size. There are seven function tabs in PROpwr: Frequentist Analysis, Bayesian Analysis, GRM power, T-test Power Given Sample Size, T-test Sample Size Given Power, Download, and References. PROpwr is user-friendly with point-and-click functions. PROpwr can assist researchers to analyze and calculate power and sample size for PRO endpoints in clinical trials without prior programming knowledge.

Evaluating the Strength and Quality of Evidence in American Heart Association/American Stroke Association's Guidelines for Aneurysmal Subarachnoid Hemorrhage and Spontaneous Intracerebral Hemorrhage.

BACKGROUND: Clinical practice recommendations guide healthcare decisions. This study aims to evaluate the strength and quality of evidence supporting the American Heart Association (AHA)/American Stroke Association (ASA) guidelines for aneurysmal subarachnoid hemorrhage (aSAH) and spontaneous intracerebral hemorrhage (ICH). METHODS: We reviewed the current AHA/ASA guidelines for aSAH and spontaneous ICH and compared with previous guidelines. Guidelines were classified based on the Class of recommendation (COR) and Level of evidence (LOE). COR signifies recommendation strength (COR 1: Strong; COR 2a: Moderate; COR 2b: Weak; COR 3: No Benefit/Harm), while LOE denotes evidence quality (LOE A: High-Quality; LOE B-NR: Moderate-Quality, Not Randomized; LOE B-R: Moderate-Quality, Randomized; LOE C-EO: Expert Opinion; LOE C-LD: Limited Data). RESULTS: For aSAH, we identified 84 recommendations across 15 guideline categories. Of these, 31% were classified as COR I, 30% as COR 2a, 17% as COR 2b, and 18% as COR 3. In terms of LOE, 7% were based on LOE A, 10% on LOE B-R, 65% on LOE B-NR, 14% on LOE C-LD, and 5% on LOE C-EO. Compared to previous guidelines, there was a 46% decrease in LOE A, a 45% increase in LOE B, and an 11% decrease in LOE C. For spontaneous ICH, 124 guidelines were identified across 31 guideline categories. Of these, 28% were COR I, 32% COR 2b, and 9% COR 3. For LOE, 4% were based on LOE A, 35% on LOE B-NR, and 42% on LOE C-LD. Compared to previous guidelines, there was a 78% decrease in LOE A, an 82% increase in LOE B, and a 14% increase in LOE C. This analysis highlights that less than a third of AHA/ASA guidelines are classified as the highest class of recommendation, with less than 10% based on the highest LOE. CONCLUSION: Less than a third of AHA/ASA guidelines on aSAH and spontaneous ICH are classified as the highest class of recommendation with less than 10% based on highest LOE. There appears to be a decrease in proportion of guidelines based on highest LOE in most recent guidelines.

A Regional Study to Evaluate the Impact of Coal-fired Power Plants on Lung Cancer Incident Rates.

Background: Lung cancer is the leading cause of cancer related deaths. In Kansas, where coal-fired power plants account for 34% of power, we investigated whether hosting counties had higher age-adjusted lung cancer incidence rates. We also examined demographics, poverty levels, percentage of smokers, and environmental conditions using spatial analysis. Methods: Data from the Kansas Health Matters, and the Behavioral Risk Factor Surveillance System (2010-2014) for 105 counties in Kansas were analyzed. Multiple Linear Regression (MLR) assessed associations between potential risk factors and age-adjusted lung cancer incidence rates while Geographically Weighted Regression (GWR) examined regional risk factors. Results: Moran's I test confirmed spatial autocorrelation in age-adjusted lung cancer incidence rates (p<0.0003). MLR identified percentage of smokers, population size, and proportion of elderly population as significant predictors of age-adjusted lung cancer incidence rates (p<0.05). GWR showed positive associations between percentage of smokers and age-adjusted lung cancer incidence rates in over 50% of counties. Conclusion: Contrary to our hypothesis, proximity to a coal-fired power plant was not a significant predictor of age-adjusted lung cancer incidence rates. Instead, percentage of smokers emerged as a consistent global and regional risk factor. Regional lung cancer outcomes in Kansas are influenced by wind patterns and elderly population.

The Effect of Prenatal Docosahexaenoic Acid Supplementation on Offspring Fat Mass and Distribution at 24 Months Old.

Background: Excessive gestational weight gain (GWG) is related to increased offspring fat accrual, and increased fat mass (FM) is related to obesity development. Prenatal DHA supplementation has been linked to lower levels of offspring FM; however, conflicting data exist. Objectives: This study aimed to determine if there is a protective effect of prenatal DHA supplementation on offspring fat accrual and adipose tissue deposition at 24 mo in offspring born to females who gain excessive weight compared with nonexcessive weight during pregnancy. We also explored if the effect of DHA dose on FM differed by offspring sex. Methods: Infants born to females who participated in the Assessment of DHA on Reducing Early Preterm Birth randomized controlled trial (ADORE) were recruited. In ADORE, females were randomly assigned to either a high or low prenatal DHA supplement. Offspring body composition and adipose tissue distribution were measured using dual-energy x-ray absorptiometry (DXA). GWG was categorized as excessive or not excessive based on clinical guidelines. Results: For total FM, there was a significant main effect for the DHA dose (P = 0.03); however, the dose by GWG status was nonsignificant (P = 0.44). Therefore, a higher prenatal DHA dose was related to greater offspring FM (622.9 g greater) and unrelated to GWG status. When investigating a DHA dose by sex effect, a significant main effect for DHA dose (P = 0.01) was detected for central FM. However, no interaction was detected (P = 0.98), meaning that both boys and girls had greater central FM if their mother was assigned to the higher DHA dose. Conclusions: Greater prenatal DHA supplementation was associated with greater offspring FM and adipose tissue distribution at 24 mo. It will be important to understand if these effects persist into childhood.This trial was registered at clinicaltrials.gov as NCT03310983.

A high fiber diet intervention during pregnancy: The SPROUT (Single goal in PRegnancy to optimize OUTcomes) protocol paper.

BACKGROUND: Interventions to prevent excessive gestational weight gain (GWG) have had a limited impact on maternal and infant outcomes. Dietary fiber is a nutrient with benefits that counters many of the metabolic and inflammatory changes that occur during pregnancy. We will determine if a high dietary fiber (HFib) intervention provides benefit to maternal and infant outcomes. METHODS AND DESIGN: Pregnant women will be enrolled in an 18-week intervention and randomized in groups of 6-10 women/group into the intervention or control group. Weekly lessons will include information on high-dietary fiber foods and behavior change strategies. Women in the intervention group will be given daily snacks high in dietary fiber (10-12 g/day) to facilitate increasing dietary fiber intake. The primary aim will assess between-group differences for the change in maternal weight, dietary fiber intake, dietary quality, and body composition during pregnancy and up to two months post-partum. The secondary aim will assess between-group differences for the change in maternal weight, dietary fiber intake, and dietary quality from two months to one year post-partum and infant body composition from birth to one-year-old. DISCUSSION: Effective and simple intervention strategies to improve maternal and offspring outcomes are lacking. Changes during the perinatal period are related to the risk of disease development in the mother and offspring. However, it is unknown which changes can be successfully targeted to have a meaningful impact. We will test the effect of an intervention designed to counter many of the metabolic and inflammatory changes that occur during pregnancy. ETHICS AND DISSEMINATION: The University of Kansas Medical Center Institutional Review Board (IRB) approved the study protocol (STUDY00145397). The results of the trial will be disseminated at conferences and in peer reviewed publications. TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT04868110.

Evaluating the impact of delayed study startup on accrual in cancer studies.

Background: Drug development in cancer medicine depends on high-quality clinical trials, but these require large investments of time to design, operationalize, and complete; for oncology drugs, this can take 8-10 years. Long timelines are expensive and delay innovative therapies from reaching patients. Delays often arise from study startup, a process that can take 6 months or more. We assessed how study-specific factors affected the study startup duration and the resulting overall success of the study. Method: Data from The University of Kansas Cancer Center (KUCC) were used to analyze studies initiated from 2018 to 2022. Accrual percentage was computed based on the number of enrolled participants and the desired enrollment goal. Accrual success was determined by comparing the percentage of enrollments to predetermined threshold values (50%, 70%, or 90%). Results: Studies that achieve or surpass the 70% activation threshold typically exhibit a median activation time of 140.5 days. In contrast, studies that fall short of the accrual goal tend to have a median activation time of 187 days, demonstrating the shorter median activation times associated with successful studies. Wilcoxon rank-sum test conducted for the study phase (W=13607, p-value=0.001) indicates that late-phase projects took longer to activate compared to early-stage projects. We also conducted the study with 50% and 90% accrual thresholds; our findings remained consistent. Conclusions: Longer activation times are linked to reduced project success, and early-phase studies tend to have higher success than late-phase studies. Therefore, by reducing impediments to the approval process, we can facilitate quicker approvals, increasing the success of studies regardless of phase.

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.

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.