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.

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.

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.

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.

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

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

Using 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.

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.

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

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

Bolstering the complex study start-up process at NCI cancer centers using technology.

Background: The study startup process for interventional clinical trials is a complex process that involves the efforts of many different teams. Each team is responsible for their startup checklist in which they verify that the necessary tasks are done before a study can move on to the next team. This regulatory process provides quality assurance and is vital for ensuring patient safety [10]. However, without having this startup process centralized and optimized, study approval can take longer than necessary as time is lost when it passes through many different hands. Objective: This manuscript highlights the process and the systems that were developed at The University of Kansas Comprehensive Cancer Center regarding the study startup process. To facilitate this process the regulatory management, site development, cancer center administration, and the Biostatistics & Informatics Shared Resources (BISR) teams came together to build a platform aimed at streamlining the startup process and providing a transparent view of where a study is in the startup process. Process: Ensuring the guidelines are clearly articulated for the review criteria of each of the three review boards, i.e., Disease Working Group (DWG), Executive Resourcing Committee (ERC), and Protocol Review and Monitoring Committee (PRMC) along with a system that can track every step and its history throughout the review process. Results: Well-defined processes and tracking methodologies have allowed the operations teams to track each study closely and ensure the 90-day and 120-day deadlines are met, this allows the operational team to dynamically prioritize their work daily. It also provides Principal investigators a transparent view of where their study stands within the study startup process and allows them to prepare for the next steps accordingly. Conclusion/future work: The current process and technology deployment has been a significant improvement to expedite the review process and minimize study startup delays. There are still a few opportunities to fine-tune the study startup process; an example of which includes automatically informing the operational managers or the study teams to act upon deadlines regarding study review rather than the current manual communication process which involves them looking it up in the system which can add delays.

Quitting Smoking before and after Pregnancy: Study Methods and Baseline Data from a Prospective Cohort Study.

Smoking during pregnancy and postpartum remains an important public health problem. No known prior study has prospectively examined mutual changes in risk factors and women's smoking trajectory across pregnancy and postpartum. The objective of this study was to report methods used to implement a prospective cohort (Msgs4Moms), present participant baseline characteristics, and compare our sample characteristics to pregnant women from national birth record data. The cohort study was designed to investigate smoking patterns, variables related to tobacco use and abstinence, and tobacco treatment quality across pregnancy through 1-year postpartum. Current smokers or recent quitters were recruited from obstetrics clinics. Analyses included Chi-square and independent sample t-tests using Cohen's d. A total of 62 participants (41 smokers and 21 quitters) were enrolled. Participants were Black (45.2%), White (35.5%), and multiracial (19.3%); 46.8% had post-secondary education; and most were Medicaid-insured (64.5%). Compared with quitters, fewer smokers were employed (65.9 vs 90.5%, Cohen's d = 0.88) and more reported financial strain (61.1% vs 28.6%; Cohen's d = 0.75). Women who continue to smoke during pregnancy cope with multiple social determinants of health. Longitudinal data from this cohort provide intensive data to identify treatment gaps, critical time points, and potential psychosocial variables warranting intervention.

Barriers to Clinical Trial Participation: Comparative Study Between Rural and Urban Participants.

BACKGROUND: The National Clinical Trials Network program conducts phase 2 or phase 3 treatment trials across all National Cancer Institute's designated cancer centers. Participant accrual across these clinical trials is a critical factor in deciding their success. Cancer centers that cater to rural populations, such as The University of Kansas Cancer Center, have an additional responsibility to ensure rural residents have access and are well represented across these studies. OBJECTIVE: There are scant data available regarding the factors that act as barriers to the accrual of rural residents in these clinical trials. This study aims to use electronic screening logs that were used to gather patient data at several participating sites in The Kansas University of Cancer Center's Catchment area. METHODS: Screening log data were used to assess what clinical trial participation barriers are faced by these patients. Additionally, the differences in clinical trial participation barriers were compared between rural and urban participating sites. RESULTS: Analysis revealed that the hospital location rural urban category, defined as whether the hospital was in an urban or rural setting, had a medium effect on enrolment of patients in breast cancer and lung cancer trials (Cohen d=0.7). Additionally, the hospital location category had a medium effect on the proportion of recurrent lung cancer cases at the time of screening (d=0.6). CONCLUSIONS: In consideration of the financially hostile nature of cancer treatment as well as geographical and transportation barriers, clinical trials extended to rural communities are uniquely positioned to alleviate the burden of nonmedical costs in trial participation. However, these options can be far less feasible for patients in rural settings. Since the number of patients with cancer who are eligible for a clinical trial is already limited by the stringent eligibility criteria required of such a complex disease, improving accessibility for rural patients should be a greater focus in health policy.

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.

Accelerating Cancer Patient Recruitment Through a Mobile Application (Clinical Trial Finder).

OBJECTIVE: Participant recruitment is a challenge for any clinical trial but is especially complex in cancer specifically due to the need to initiate treatment urgently. Most participants enrolled in oncology clinical trials are identified as potential participants by the oncologist or other referring provider. Optimal clinical care for patients with cancer includes consideration of participation in a clinical trial. However, the process of finding a clinical trial that is appropriate the patient can be cumbersome and time consuming. MATERIAL AND METHODS: The University of Kansas Cancer Center has developed a mobile application (app) which streamlines the clinical trial search process for physicians, patients, and caregivers by cohesively integrating all clinical trials currently recruiting in the center and making them easy to browse. RESULTS: Key aspects of the app include simple filtering options, the ability to search for trials by name, easily accessible assistance, and in-app referral by phone or email. Initial feedback on the app has been very positive, with several suggestions already being implemented in future development. The app was designed to be used both by physicians to find trials, as well as patients in collaboration with their physicians. CONCLUSION: While long-term results will be crucial to understanding how the app can best serve our patient population, our initial results suggest that health system specific clinical trial apps can address a currently unmet need in the clinical trial recruitment process.

Higher-Dose DHA Supplementation Modulates Immune Responses in Pregnancy and Is Associated with Decreased Preterm Birth.

Pregnancy and parturition involve extensive changes in the maternal immune system. In our randomized, multi-site, double-blind superiority trial using a Bayesian adaptive design, we demonstrated that 1000 mg/day of docosahexaenoic acid (DHA) was superior to 200 mg/day in preventing both early preterm birth (less than 34 weeks' gestation) and preterm birth (less than 37 weeks' gestation). The goal of this secondary study is to compare the effects of 1000 mg/day versus 200 mg/day on maternal inflammation, a possible mechanism by which DHA may prevent preterm birth. Maternal blood samples were collected at enrollment (12-20 weeks' gestation) and at delivery. Red blood cell DHA levels were measured by gas chromatography, and plasma concentrations of sRAGE, IL-6, IL-1ß, TNFα, and INFγ were measured by ELISA. Data were analyzed for associations with the DHA dose, gestational age at birth, and preterm birth (<37 weeks). Higher baseline and lower delivery levels of maternal sRAGE were associated with a greater probability of longer gestation and delivery at term gestation. Higher-dose DHA supplementation increased the probability of a smaller decrease in delivery sRAGE levels. Higher IL-6 concentrations at delivery were associated with the probability of delivering after 37 weeks, and higher-dose DHA supplementation increased the probability of greater increases in IL-6 concentrations between enrollment and delivery. These data provide a proposed mechanistic explanation of how a higher dose of DHA during pregnancy provides immunomodulatory regulation in the initiation of parturition by influencing sRAGE and IL-6 levels, which may explain its ability to reduce the risk of preterm birth.

The need to study rural cancer outcome disparities at the local level: a retrospective cohort study in Kansas and Missouri.

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.

A framework for personalized mammogram screening.

Breast cancer screening guidelines serve as crucial evidence-based recommendations in deciding when to begin regular screenings. However, due to developments in breast cancer research and differences in research interpretation, screening guidelines can vary between organizations and within organizations over time. This leads to significant lapses in adopting updated guidelines, variable decision making between physicians, and unnecessary screening for low to moderate risk patients (Jacobson and Kadiyala, 2017; Corbelli et al., 2014). For analysis, risk factors were assessed for patient screening behaviors and results. The outcome variable for the first analysis was whether the patient had undergone screening. The risk factors considered were age, marital status, education level, rural versus urban residence, and family history of breast cancer. The outcome variable for the second analysis was whether patients who had undergone breast cancer screening presented abnormal results. The risk factors considered were age, Body Mass Index, family history, smoking and alcohol status, hormonal contraceptive use, Hormone Replacement Therapy use, age of first pregnancy, number of pregnancies (parity), age of first menses, rural versus urban residence, and whether or not patients had at least one child. Logistic regression analysis displayed strong associations for both outcome variables. Risk of screening nonattendance was negatively associated with age as a continuous variable, age as a dichotomous variable, being married, any college education, and family history. Risk of one or more abnormal mammogram findings was positively associated with family history, and hormonal contraceptive use. This procedure will be further developed to incorporate additional risk factors and refine the analysis of currently implemented risk factors.

Non-cancer clinical trials start-up metrics at an academic medical center: Implications for advancing research.

The primary goal for any clinical trial after it receives a funding notification is to receive regulatory approval and initiate the trial for recruitment. Every trial must go through documentation and regulatory process before it can start recruiting participants and collecting data; this initial process of review and approval is known as the study start-up process (SSU). We evaluated the average time taken for studies to receive approvals. Using data from clinical trials conducted at the University of Kansas Medical Center, various times to reach the start of the study were calculated based on the dates of individual study. The results of this analysis showed that chart review studies and investigator-initiated trials had a shorter time to activation than other types of studies. Additionally, single-center studies had a shorter activation time than multi-center studies. The analysis also demonstrated that the overall processing time consistently had been reduced over time.

Restarting and timing of oral anticoagulation after traumatic intracranial hemorrhage: a review and summary of ongoing and planned prospective randomized clinical trials.

Anticoagulant-associated traumatic intracranial hemorrhage (tICrH) is a devastating injury with high morbidity and mortality. For survivors, treating clinicians face the dilemma of restarting oral anticoagulation with scarce evidence to guide them. Thromboembolic risk is high from the bleeding event, patients' high baseline risks, that is, the pre-existing indication for anticoagulation, and the risk of immobility after the bleeding episode. This must be balanced with potentially devastating hematoma expansion or new hemorrhagic lesions. Retrospective evidence and expert opinion support restarting oral anticoagulants in most patients with tICrH, but timing is uncertain. Researchers have failed to make clear distinctions between tICrH and spontaneous intracranial hemorrhage (sICrH), which have differing natural histories. While both appear to benefit from restarting, sICrH has a higher rebleeding risk and similar or lower thrombotic risk. Clinical equipoise on restarting is also divergent. In sICrH, equipoise is centered on whether to restart. In tICrH, it is centered on when. Several prospective randomized clinical trials are ongoing or about to start to examine the risk-benefit of restarting. Most of them are restricted to patients with sICrH, with antiplatelet control groups. Most are also restricted to direct oral anticoagulants (DOACs), as they are associated with a lower overall risk of ICrH. There is some overlap with tICrH via subdural hematoma, and one trial is specific to restart timing with DOACs in only traumatic cases. This is a narrative review of the current evidence for restarting anticoagulation and restart timing after tICrH along with a summary of the ongoing and planned clinical trials.

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.

Optimizing Retrieval of Biospecimens Using the Curated Cancer Clinical Outcomes Database (C3OD).

To fully support their role in translational and personalized medicine, biorepositories and biobanks must continue to advance the annotation of their biospecimens with robust clinical and laboratory data. Translational research and personalized medicine require well-documented and up-to-date information, but the infrastructure used to support biorepositories and biobanks can easily be out of sync with the host institution. To assist researchers and provide them with accurate pathological, epidemiological, and bio-molecular data, the Biospecimen Repository Core Facility (BRCF) at the University of Kansas Medical Center (KUMC) merges data from medical records, the tumor registry, and pathology reports using the Curated Cancer Clinical Outcomes Database (C3OD). In this report, we describe the utilization of C3OD to optimally retrieve and dispense biospecimen samples using these 3 data sources and demonstrate how C3OD greatly increases the efficiency of obtaining biospecimen samples for the researchers.