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.

Rurality Impacts Mammography Screening Adherence Among Mid-life Women in the Kansas Region.

OBJECTIVE: The gold standard for breast cancer screening and prevention is regular mammography; thus, understanding what impacts adherence to this standard is essential in limiting cancer-associated costs. We assessed the impact of various understudied sociodemographic factors of interest on adherence to the receipt of regular mammograms. METHODS: A total Nc = 14,553 mammography-related claims from Nw = 6,336 female Kansas aged between 45 and 54 were utilized from insurance claim databases furnished by multiple providers. Adherence to regular mammography was quantified continuously via a compliance ratio, used to capture the number of eligible years in which at least one mammogram was received, as well as categorically. The relationship between race, ethnicity, rurality, insurance (public/private), screening facility type, and distance to nearest screening facility with both continuous and categorically defined compliance were individually assessed via Kruskal-Wallis one-way ANOVAs, chi-squared tests, multiple linear regression models, and multiple logistic regression, as appropriate. Findings from these individual models were used to inform the construction of a basic, multifaceted prediction model. RESULTS: Model results demonstrated that all factors race and ethnicity had at least some bearing on compliance with screening guidelines among mid-life female Kansans. The strongest signal was observed in the rurality variable, which demonstrated a significant relationship with compliance regardless of how it was defined. CONCLUSION: Understudied factors that are associated with regular mammography adherence, such as rurality and distance to nearest facility, may serve as important considerations when developing intervention strategies for ensuring that female patients stick to prescribed screening regimens.

An R Shiny Application (SDOH) for Predictive Modeling Using Regional Social Determinants of Health Survey Responses.

Social determinants of health (SDoH) surveys are data sets that provide useful health-related information about individuals and communities. This study aims to develop a user-friendly web application that allows clinicians to get a predictive insight into the social needs of their patients before their in-patient visits using SDoH survey data to provide an improved and personalized service. The study used a longitudinal survey that consisted of 108,563 patient responses to 12 questions. Questions were designed to have a binary outcome as the response and the patient's most recent responses for each of these questions were modeled independently by incorporating explanatory variables. Multiple classification and regression techniques were used, including logistic regression, Bayesian generalized linear model, extreme gradient boosting, gradient boosting, neural networks, and random forests. Based on the area under the curve values, gradient boosting models provided the highest precision values. Finally, the models were incorporated into an R Shiny application, enabling users to predict and compare the impact of SDoH on patients' lives. The tool is freely hosted online by the University of Kansas Medical Center's Department of Biostatistics and Data Science. The supporting materials for the application are publicly accessible on GitHub.

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.

The Effects of COVID-19 Pandemic Policy on Social Needs Across the State of Kansas and Western Missouri: Paired Survey Response Testing.

BACKGROUND: Studying patients' social needs is critical to the understanding of health conditions and disparities, and to inform strategies for improving health outcomes. Studies have shown that people of color, low-income families, and those with lower educational attainment experience greater hardships related to social needs. The COVID-19 pandemic represents an event that severely impacted people's social needs. This pandemic was declared by the World Health Organization on March 11, 2020, and contributed to food and housing insecurity, while highlighting weaknesses in the health care system surrounding access to care. To combat these issues, legislators implemented unique policies and procedures to help alleviate worsening social needs throughout the pandemic, which had not previously been exerted to this degree. We believe that improvements related to COVID-19 legislature and policy have positively impacted people's social needs in Kansas and Missouri, United States. In particular, Wyandotte County is of interest as it suffers greatly from issues related to social needs that many of these COVID-19-related policies aimed to improve. OBJECTIVE: The research objective of this study was to evaluate the change in social needs before and after the COVID-19 pandemic declaration based on responses to a survey from The University of Kansas Health System (TUKHS). We further aimed to compare the social needs of respondents from Wyandotte County from those of respondents in other counties in the Kansas City metropolitan area. METHODS: Social needs survey data from 2016 to 2022 were collected from a 12-question patient-administered survey distributed by TUKHS during a patient visit. This provided a longitudinal data set with 248,582 observations, which was narrowed down into a paired-response data set for 50,441 individuals who had provided at least one response before and after March 11, 2020. These data were then bucketed by county into Cass (Missouri), Clay (Missouri), Jackson (Missouri), Johnson (Kansas), Leavenworth (Kansas), Platte (Missouri), Wyandotte (Kansas), and Other counties, creating groupings with at least 1000 responses in each category. A pre-post composite score was calculated for each individual by adding their coded responses (yes=1, no=0) across the 12 questions. The Stuart-Maxwell marginal homogeneity test was used to compare the pre and post composite scores across all counties. Additionally, McNemar tests were performed to compare responses before and after March 11, 2020, for each of the 12 questions across all counties. Finally, McNemar tests were performed for questions 1, 7, 8, 9, and 10 for each of the bucketed counties. Significance was assessed at P<.05 for all tests. RESULTS: The Stuart-Maxwell test for marginal homogeneity was significant (P<.001), indicating that respondents were overall less likely to identify an unmet social need after the COVID-19 pandemic. McNemar tests for individual questions indicated that after the COVID-19 pandemic, respondents across all counties were less likely to identify unmet social needs related to food availability (odds ratio [OR]=0.4073, P<.001), home utilities (OR=0.4538, P<.001), housing (OR=0.7143, P<.001), safety among cohabitants (OR=0.6148, P<.001), safety in their residential location (OR=0.6172, P<.001), child care (OR=0.7410, P<0.01), health care access (OR=0.3895, P<.001), medication adherence (OR=0.5449, P<.001), health care adherence (OR=0.6378, P<.001), and health care literacy (0.8729, P=.02), and were also less likely to request help with these unmet needs (OR=0.7368, P<.001) compared with prepandemic responses. Responses from individual counties were consistent with the overall results for the most part. Notably, no individual county demonstrated a significant reduction in social needs relating to a lack of companionship. CONCLUSIONS: Post-COVID-19 responses showed improvement across almost all social needs-related questions, indicating that the federal policy response possibly had a positive impact on social needs across the populations of Kansas and western Missouri. Some counties were impacted more than others and positive outcomes were not limited to urban counties. The availability of resources, safety net services, access to health care, and educational opportunities could play a role in this change. Future research should focus on improving survey response rates from rural counties to increase their sample size, and to evaluate other explanatory variables such as food pantry access, educational status, employment opportunities, and access to community resources. Government policies should be an area of focused research as they may affect the social needs and health of the individuals considered in this analysis.

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.