Search

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.

Anticoagulants prevent thrombosis and death in patients with atrial fibrillation and venous thromboembolism (VTE) but also increase bleeding risk. The benefit/risk ratio favors anticoagulation in most of these patients. However, some will have a bleeding complication, such as the common trip-and-fall brain injury in elderly patients that results in traumatic intracranial hemorrhage. Clinicians must then make the difficult decision about when to restart the anticoagulant. Restarting too early risks making the bleeding worse. Restarting too late risks thrombotic events such as ischemic stroke and VTE, the indications for anticoagulation in the first place. There are more data on restarting patients with spontaneous intracranial hemorrhage, which is very different than traumatic intracranial hemorrhage. Spontaneous intracranial hemorrhage increases the risk of rebleeding because intrinsic vascular changes are widespread and irreversible. In contrast, traumatic cases are caused by a blow to the head, usually an isolated event portending less future risk. Clinicians generally agree that anticoagulation should be restarted but disagree about when. This uncertainty leads to long restart delays causing a large, potentially preventable burden of strokes and VTE, which has been unaddressed because of the absence of high quality evidence. Restart Traumatic Intracranial Hemorrhage (the “r” distinguished intracranial from intracerebral) (TICrH) is a prospective randomized open label blinded end-point response-adaptive clinical trial that will evaluate the impact of delays to restarting direct oral anticoagulation (1, 2, or 4 weeks) on the composite of thrombotic events and bleeding in patients presenting after traumatic intracranial hemorrhage.

Purpose: Addition of carboplatin (Cb) to anthracycline chemotherapy improves pathologic complete response (pCR), and carboplatin plus taxane regimens also yield encouraging pCR rates in triple-negative breast cancer (TNBC). Aim of the NeoSTOP multisite randomized phase II trial was to assess efficacy of anthracycline-free and anthracycline-containing neoadjuvant carboplatin regimens. Patients and Methods: Patients aged ≥18 years with stage I–III TNBC were randomized (1:1) to receive either paclitaxel (P) weekly × 12 plus carboplatin AUC6 every 21 days × 4 followed by doxorubicin/cyclophosphamide (AC) every 14 days × 4 (CbP → AC, arm A), or carboplatin AUC6 + docetaxel (D) every 21 days × 6 (CbD, arm B). Stromal tumor-infiltrating lymphocytes (sTIL) were assessed. Primary endpoint was pCR in breast and axilla. Other endpoints included residual cancer burden (RCB), toxicity, cost, and event-free (EFS) and overall survival (OS). Results: One hundred patients were randomized; arm A (n = 48) or arm B (n = 52). pCR was 54% [95% confidence interval (CI), 40%–69%] in arm A and 54% (95% CI, 40%–68%) in arm B. RCB 0+I rate was 67% in both arms. Median sTIL density was numerically higher in those with pCR compared with those with residual disease (20% vs. 5%; P = 0.25). At median follow-up of 38 months, EFS and OS were similar in the two arms. Grade 3/4 adverse events were more common in arm A compared with arm B, with the most notable differences in neutropenia (60% vs. 8%; P < 0.001) and febrile neutropenia (19% vs. 0%; P < 0.001). There was one treatment-related death (arm A) due to acute leukemia. Mean treatment cost was lower for arm B compared with arm A (P = 0.02). Conclusions: The two-drug CbD regimen yielded pCR, RCB 0+I, and survival rates similar to the four-drug regimen of CbP → AC, but with a more favorable toxicity profile and lower treatment-associated cost.

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

PURPOSE The University of Kansas Cancer Center (KU Cancer Center) recently developed a data warehouse to Organize and Prioritize Trends to Inform KU Cancer Center (OPTIK). The OPTIK database aggregates and standardizes data collected across the bistate catchment area served by the KU Cancer Center. To improve the usability of the OPTIK database, we developed shinyOPTIK, a user-friendly, interactive web application for visualizing cancer risk factor and mortality rate data across the KU Cancer Center Catchment area. METHODS Data in the OPTIK database were first consolidated at the county level across the KU Cancer Center catchment area. Next, the shinyOPTIK development team met with the KU Cancer Center leadership to discuss the needs and priorities of the shinyOPTIK web application. shinyOPTIK was developed under the R Shiny framework and consists of a user interface (ui.R) and a web server (server.R). At present, s hinyOPTIK can be used to generate county-level geographical heatmaps; bar plots of demographic, screening, and risk factors; and line plots to visualize temporal trends at different Rural-Urban Continuum Codes (RUCCs), rural-urban status, metropolitan, or county levels across the KU Cancer Center catchment area. RESULTS Two examples, adult obesity prevalence and lung cancer mortality, are presented to illustrate how researchers can use shinyOPTIK. Each example is accompanied by post hoc visualizations to help explain key observations in terms of rural-urban disparities. CONCLUSION Although shinyOPTIK was developed to improve understanding of spatial and temporal trends across the population served by the KU Cancer Center, our hope is that the description of the steps involved in the creation of this tool along with open-source code for our application provided herein will serve as a guide for other research centers in the development of similar tools.

Background Rural residence is commonly thought to be a risk factor for poor cancer outcomes. However, a number of studies have reported seemingly conflicting information regarding cancer outcome disparities with respect to rural residence, with some suggesting that the disparity is not present and others providing inconsistent evidence that either urban or rural residence is associated with poorer outcomes. We suggest a simple explanation for these seeming contradictions: namely that rural cancer outcome disparities are related to factors that occur differentially at a local level, such as environmental exposures, lack of access to care or screening, and socioeconomic factors, which differ by type of cancer. Methods We conducted a retrospective cohort study examining ten cancers treated at the University of Kansas Medical Center from 2011 to 2018, with individuals from either rural or urban residences. We defined urban residences as those in a county with a U.S. Department of Agriculture Urban Influence Code (UIC) of 1 or 2, with all other residences defines a rural. Inverse probability of treatment weighting was used to create a pseudo-sample balanced for covariates deemed likely to affect the outcomes modeled with cumulative link and weighted Cox-proportional hazards models. Results We found that rural residence is not a simple risk factor but rather appears to play a complex role in cancer outcome disparities. Specifically, rural residence is associated with higher stage at diagnosis and increased survival hazards for colon cancer but decreased risk for lung cancer compared to urban residence. Conclusion Many cancers are affected by unique social and environmental factors that may vary between rural and urban residents, such as access to care, diet, and lifestyle. Our results show that rurality can increase or decrease risk, depending on cancer site, which suggests the need to consider the factors connected to rurality that influence this complex pattern. Thus, we argue that such disparities must be studied at the local level to identify and design appropriate interventions to improve cancer outcomes.

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.

Electronic health records (EHR) represent a rich resource for conducting observational studies, supporting clinical trials, and more. However, much of the data contains unstructured text, presenting an obstacle to automated extraction. Natural language processing (NLP) can structure and learn from text, but NLP algorithms were not designed for the unique characteristics of EHR. Here, we propose Relevant Word Order Vectorization (RWOV) to aid with structuring. RWOV is based on finding the positional relationship between the most relevant words to predicting the class of a text. This facilitates machine learning algorithms to use the interaction of not just keywords but positional dependencies (e.g. a relevant word occurs 5 relevant words before some term of interest). As a proof-of-concept, we attempted to classify the hormone receptor status of breast cancer patients treated at the University of Kansas Medical Center, comparing RWOV to other methods using the F1 score and AUC. RWOV performed as well as, or better than other methods in all but one case. For F1 score, RWOV had a clear edge on most tasks. AUC tended to be closer, but for HER2, RWOV was significantly better for most comparisons. These results suggest RWOV should be further developed for EHR-related NLP.

INTRODUCTION Maternal-infant equilibrium occurs when cord blood docosahexaenoic acid (DHA) is less than or equal to maternal DHA at delivery. Equilibrium may be an indicator of sufficient DHA for optimal fetal and infant neurodevelopment. The purpose of this study was to test the effect of maternal DHA supplementation on equilibrium status and fetal neurodevelopment. METHODS Women enrolled between 12 and 20 weeks gestation and were randomized to 200 or 800 mg DHA/day until delivery. Maternal red blood cell (RBC) phospholipids were measured at enrollment, 32 weeks, delivery, and in cord blood at delivery. Fetal neurodevelopment was measured at 32 and 36 weeks gestation. Intent-to-treat analyses were conducted to test differences in equilibrium status by group. Fetal outcomes were assessed by equilibrium status and group. RESULTS Three hundred women enrolled and 262 maternal-infant dyads provided blood samples at delivery. No maternal-infant dyads with maternal RBC-DHA ≤ 6.96% at delivery achieved equilibrium. The incidence of equilibrium was significantly higher in the 800 mg group. There was no effect of maternal group or equilibrium status on fetal neurodevelopment. CONCLUSION The significance of maternal-infant DHA equilibrium remains unknown. Ongoing research will test the effect of treatment group, equilibrium, and nutrient status on infant behavior and brain function. IMPACT Pregnant women who received a higher dose of docosahexaenoic acid (DHA) were more likely to achieve maternal-infant DHA equilibrium at delivery. Equilibrium status had no effect on fetal neurodevelopment in this sample. While DHA is crucial for early life neurodevelopment, the significance of achieving maternal-infant equilibrium above the lower threshold is uncertain. There is a lower threshold of maternal DHA status where maternal-infant DHA equilibrium never occurs. The lack of equilibrium associated with low maternal DHA status may indicate insufficient maternal status for optimal placental transfer.

Objective : Patient reported outcomes (PRO) 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. Patient Reported Outcomes Measurement Information System (PROMIS®) is a collection of standardized measures of PROs using Item Response Theory (IRT). However, in clinical trials with PROs as endpoints, observed scores are routinely used for power estimation rather than IRT scores. This paper aims to fill this gap and estimate power in a two-arm clinical trials with PROMIS measures as endpoints with IRT model. Study Design and Setting : We conducted a series of simulations to study the IRT power with validated PROMIS measures controlling factors including sample size, effect size, number of items, and missing data proportion. Results : Our results showed that sample size, effect size, and number of items are important indicators of IRT based power estimation for PROMIS measures. When effect size is small and sample size is limited, IRT model provides higher power than the closed form formula. Conclusion : IRT based simulation should be used for power estimation in two-armed clinical, especially when there is small effect size or small sample size.

One measure of research productivity within the University of Kansas Cancer Center (KU Cancer Center) is peer-reviewed publications. Considerable effort goes into searching, capturing, reviewing, storing, and reporting cancer-relevant publications. Traditionally, the method of gathering relevant information to the publications is done manually. This manuscript describes the efforts to transition KU Cancer Center’s publication gathering process from a heavily manual to a more automated and efficient process. To achieve this transition in the most customized and cost-effective manner, a homegrown, automated system was developed using open source API among other software. When comparing the automated and the manual processes over several years of data, publication search and retrieval time dropped from an average of 59 h to 35 min, which would amount to a cost savings of several thousand dollars per year. The development and adoption of an automated publications search process can offer research centers great potential for less-error prone results with a savings in time and cost.

An increasingly diversified demographic landscape in rural and urban America warrants the attention of The University of Kansas Cancer Center (KU Cancer Center) researchers, clinicians, outreach staff and administrators as the institution assesses ways to reach its expansive, bi-state catchment area. Within the counties of the KU Cancer Center catchment area, patient level and public health data are available and categorized by varying geographic regional boundaries. Multiple data sources and different data collection processes complicate summarizing catchment area data. A curated data warehouse that retrieves and structures the data, with a common denominator, can support meaningful use of the data in a standard and consistent format. The KU Cancer Center built a data warehouse to Organize and Prioritize Trends to Inform KU Cancer Center (OPTIK), which functions to streamline the process of synthesizing data regarding Kansas and Missouri demographics, cancer risk factors and incidence and mortality rates. OPTIK standardizes these diverse data sources to enable analyses of the cancer burden at local, regional and national levels while upholding a strict standard of patient privacy. The OPTIK database enables researchers to use available data and create heat maps and other visualizations to aid in funding proposals, presentations and research activities. Furthermore, using knowledge provided by OPTIK, the KU Cancer Center is able to prioritize action items for research and outreach and more effectively communicate the impact of those efforts.

Heart failure (HF), the leading cause of morbidity and mortality in the US, affects 6.6 million adults with an estimated additional 3 million people by 2030. More than 50% of HF patients have heart failure with preserved left ventricular ejection fraction (HFpEF). These patients have impaired cardiac muscle relaxation and diastolic filling, which investigators have associated with cellular energetic impairment. Patients with HFpEF experience symptoms of: (1) fatigue; (2) shortness of breath; and (3) swelling (edema) of the lower extremities. However, current HF guidelines offer no effective treatment to address these underlying pathophysiologic mechanisms. Thus, we propose a biobehavioral symptom science study using ubiquinol and D-ribose (therapeutic interventions) to target mitochondrial bioenergetics to reduce the complex symptoms experienced by patients with HFpEF. Using a randomized, double-blind, placebo-controlled design, the overall objective is to determine if administering ubiquinol and/or D-ribose to HFpEF patients for 12 weeks would decrease the severity of their complex symptoms and improve their cardiac function. The measures used to assess patients’ perceptions of their health status and level of vigor (energy) will be the Kansas City Cardiomyopathy Questionnaire (KCCQ) and Vigor subscale of the Profile of Mood States. The 6-min walk test will be used to test exercise tolerance. Left ventricular diastolic function will be assessed using innovative advanced echocardiography software called speckle tracking. We will measure B-type natriuretic peptides (secreted from ventricles in HF) and lactate/ATP ratio (measure of cellular energetics). Ubiquinol (active form of Coenzyme Q10) and D-ribose are two potential treatments that can positively affect cellular energetic impairment, the major underlying mechanism of HFpEF. Ubiquinol, the reduced form of CoQ10, is more effective in adults over the age of 50. In patients with HFpEF, mitochondrial deficiency of ubiquinol results in decreased adenosine triphosphate (ATP) synthesis and reduced scavenging of reactive oxygen species. D-ribose is a substrate required for ATP synthesis and when administered has been shown to improve impaired myocardial bioenergetics. Therefore, if the biological underpinning of deficient mitochondrial ATP in HFpEF is not addressed, patients will suffer major symptoms including lack of energy, fatigue, exertional dyspnea, and exercise intolerance. ClinicalTrials.gov Identifier: NCT03133793 ; Data of Registration: April 28, 2017.

In the last few decades, the number of trials using Bayesian methods has grown rapidly. Publications prior to 1990 included only three clinical trials that used Bayesian methods, but that number quickly jumped to 19 in the 1990s and to 99 from 2000 to 2012. While this literature provides many examples of Bayesian Adaptive Designs (BAD), none of the papers that are available walks the reader through the detailed process of conducting a BAD. This paper fills that gap by describing the BAD process used for one comparative effectiveness trial (Patient Assisted Intervention for Neuropathy: Comparison of Treatment in Real Life Situations) that can be generalized for use by others. A BAD was chosen with efficiency in mind. Response-adaptive randomization allows the potential for substantially smaller sample sizes, and can provide faster conclusions about which treatment or treatments are most effective. An Internet-based electronic data capture tool, which features a randomization module, facilitated data capture across study sites and an in-house computation software program was developed to implement the response-adaptive randomization. A process for adapting randomization with minimal interruption to study sites was developed. A new randomization table can be generated quickly and can be seamlessly integrated in the data capture tool with minimal interruption to study sites. This manuscript is the first to detail the technical process used to evaluate a multisite comparative effectiveness trial using adaptive randomization. An important opportunity for the application of Bayesian trials is in comparative effectiveness trials. The specific case study presented in this paper can be used as a model for conducting future clinical trials using a combination of statistical software and a web-based application. ClinicalTrials.gov Identifier: NCT02260388 , registered on 6 October 2014