The precise calculation of sample sizes is a crucial aspect in the design of clinical trials particularly for pharmaceutical statisticians. While various R statistical software packages have been developed by researchers to estimate required sample sizes under different assumptions, there has been a notable absence of a standalone R statistical software package that allows researchers to comprehensively estimate sample sizes under generalized scenarios. This paper introduces the R statistical software package "GenTwoArmsTrialSize" available on the Comprehensive R Archive Network (CRAN), designed for estimating the required sample size in two-arm clinical trials. The package incorporates four endpoint types, two trial treatment designs, four types of hypothesis tests, as well as considerations for noncompliance and loss of follow-up, providing researchers with the capability to estimate sample sizes across 24 scenarios. To facilitate understanding of the estimation process and illuminate the impact of noncompliance and loss of follow-up on the size and variability of estimations, the paper includes four hypothetical examples and one applied example. The discussion encompasses the package's limitations and outlines directions for future extensions and improvements.

Background/Objectives:Obesity is among the most common global public health issues in the 21st century and contributes significantly to cardiovascular morbidity and mortality burden. The success of well-targeted policies and intervention strategies aimed at addressing obesity depends heavily on understanding the effect of geographical location on obesity and other predictors. The study aim was to quantify county-level geographical differences in obesity across Florida counties while simultaneously identifying predictors of obesity prevalence. Methods:This study used the 2019 data from the Florida state-based telephone surveillance systems, known as the Behavioral Risk Factor Surveillance System (BRFSS) which provides county-level data on measures of the prevalence of personal health behaviors that are risk factors for morbidity and mortality. The survey collected data on a total sample of 54,260 adults residing in 67 counties of Florida. This study applied Bayesian geospatial models and interactive web-based mapping approaches to analyze and map county-level geographical differences in the risk of obesity. The estimated coefficients were presented as log mean with their associated 95% credible intervals (Cr.Is). Results:The study identified sedentary lifestyle (log mean = 0.023, 95% Cr.I: 0.006, 0.039) as the only risk factor independently associated with increased burden of obesity. The results showed substantial county-level geographical differences in the predicted obesity prevalence with an overall obesity prevalence of 68.6% with a range of 59.0%-75.7%. Residing in Holmes was associated with the highest burden of obesity. Furthermore, the prevalence was relatively high in Levy, Columbia, Lafayette, Hendry, Bradford, Calhoun, Dixie, Okeechobee, and Gadsden counties. Conclusion:The substantial county-level geographical difference in obesity prevalence found is of great importance for sound public health policy and intervention strategies at the local level. The geospatial modeling supported by the web-based spatial mapping tool employed in this study can help guide the design of geographical prioritization of targeted public health policies and intervention strategies to combat adult obesity and its associated mortality.

Background In clinical trials and epidemiological research, mixed-effects models are commonly used to examine population-level and subject-specific trajectories of biomarkers over time. Despite their increasing popularity and application, the specification of these models necessitates a great deal of care when analysing longitudinal data with non-linear patterns and asymmetry. Parametric (linear) mixed-effect models may not capture these complexities flexibly and adequately. Additionally, assuming a Gaussian distribution for random effects and/or model errors may be overly restrictive, as it lacks robustness against deviations from symmetry. Methods This paper presents a semiparametric mixed-effects model with flexible distributions for complex longitudinal data in the Bayesian paradigm. The non-linear time effect on the longitudinal response was modelled using a spline approach. The multivariate skew-t distribution, which is a more flexible distribution, is utilized to relax the normality assumptions associated with both random-effects and model errors. Results To assess the effectiveness of the proposed methods in various model settings, simulation studies were conducted. We then applied these models on chronic kidney disease (CKD) data and assessed the relationship between covariates and estimated glomerular filtration rate (eGFR). First, we compared the proposed semiparametric partially linear mixed-effect (SPPLM) model with the fully parametric one (FPLM), and the results indicated that the SPPLM model outperformed the FPLM model. We then further compared four different SPPLM models, each assuming different distributions for the random effects and model errors. The model with a skew-t distribution exhibited a superior fit to the CKD data compared to the Gaussian model. The findings from the application revealed that hypertension, diabetes, and follow-up time had a substantial association with kidney function, specifically leading to a decrease in GFR estimates. Conclusions The application and simulation studies have demonstrated that our work has made a significant contribution towards a more robust and adaptable methodology for modeling intricate longitudinal data. We achieved this by proposing a semiparametric Bayesian modeling approach with a spline smoothing function and a skew-t distribution.

Abstract Black men hold the highest prostate cancer burden among all ethnicities; thus, there is an urgent need for research to inform the development and implementation of a culturally tailored intervention to reduce disparities and improve outcomes. In response, the Inclusive Cancer Care Research Equity (iCCaRE) Consortium was created to advance health equity and reduce disparities in prostate cancer. Addressing the unique needs of Black men require a genuine bidirectional relationship between scientist and the community. In the Black community, faith-based organizations are respected institutions advocating for health equity and justice. Given their vital role as community health gatekeepers, the iCCaRE Consortium partners with faith-based organizations. Rooted in community engaged research (CER), we conducted an environmental scan through stakeholder meetings to discuss the prevailing challenges and opportunities to meet the needs of the Black community along the prostate cancer care continuum. iCCaRE investigators including Partnership Engagement Services (PES) core partnered with faith-based organizations to ensure and advance community responsive cancer prevention. Together, with our faith-based leaders (N=18), we identified gaps including issues associated with the care continuum from screening to diagnosis and from treatment to survivorship; disparities according to health coverage and social determinants of health, and best practices to assist patients including care coordination, patient navigation, digital innovations, and programs offered by patient advocacy organizations. In response to the needs of the community and the strengths of faith-based organization, we prioritized our initial focus on increasing community prostate cancer literacy and activation using co-design educational infographics for both traditional and technology-based communication platforms. The infographics provided evidence-based facts about prostate cancer burden in the Black community; and using a call to action approach, we emphasized the importance of taking action for life saving screening, prevention and healthy lifestyle. Taking action in prostate cancer symptom recognition, survivorship care, and quality care. Also, co-designed infographics were created for clinical studies and biospecimen awareness and participation and the importance of advocacy and equitable care. Another component incorporated face to face trainings and presentations to enhance their knowledge of cancer screening tools, epidemiology of prostate cancer, and pathways to care. This presentation highlighted the first step towards community based intervention development, implementation and dissemination towards equity and justice for prostate cancer prevention and patient/survivor improvements for Black men. Citation Format: James C. Morrison, Kimlin T. Ashing, Gaole Song, Timethia J. Bonner, Che Ngufor, Getachew Dagne, Arnold Merriweather, John McCall, Ewan Cobran, Cassandra N. Moore, Fornati Bedell, Rotimi Oladapo, Floyd B. Willis, Runcie Chidebe Chidebe, Noreen A. Stephenson, JoAnne S. Oliver, Vinessa Gordon, Folakemi Odedina. iCCaRE engagement of faith-based organizations to co-create and co-disseminate infographics addressing disparities in prostate cancer literacy and clinical and biospecimen studies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 808.

Background:The assessment of heavy metals' effects on human health is frequently limited to investigating one metal or a group of related metals. The effect of heavy metals mixture on heart attack is unknown. Methods:This study applied the Bayesian kernel machine regression model (BKMR) to the 2011-2016 National Health and Nutrition Examination Survey (NHANES) data to investigate the association between heavy metal mixture exposure with heart attack. 2972 participants over the age of 20 were included in the study. Results:Results indicate that heart attack patients have higher levels of cadmium and lead in the blood and cadmium, cobalt, and tin in the urine, while having lower levels of mercury, manganese, and selenium in the blood and manganese, barium, tungsten, and strontium in the urine. The estimated risk of heart attack showed a negative association of 0.0030 units when all the metals were at their 25th percentile compared to their 50th percentile and a positive association of 0.0285 units when all the metals were at their 75th percentile compared to their 50th percentile. The results suggest that heavy metal exposure, especially cadmium and lead, may increase the risk of heart attacks. Conclusions:This study suggests a possible association between heavy metal mixture exposure and heart attack and, additionally, demonstrates how the BKMR model can be used to investigate new combinations of exposures in future studies.

ObjectiveIn adolescent substance use research, more work has been done in identifying risk factors at individual level while little work was done at community level. The objective of this study is to examine the possible risk factors associated with substance use by high-school students at county level.MethodsData on the percentages of county-level substance use by adolescents and county-level risk factors were obtained from the Florida Department of Health, Division of Public Health Statistics & Performance Management (DPHSM). Spatial beta models were used to examine the associations between risk factors and rates of substance use.ResultsGeographic variation in rates of substance use by adolescents was observed in Florida, ranging from 6.5% to 28.6% for drug use, and from 14.8% to 36.3% for alcohol. It was found that percentages of black population and individual below poverty level were inversely associated with rates of drug use by adolescents. There were no significant associations between percentages of drug use by adolescents and median income, adult drug use, school suspension, and student emotional/behavioral disability per county.ConclusionInterventions for reducing rates of adolescent substance use may focus on involving parents to monitor financial resources for their children.

Abstract Intro: Compared to white men, Black men bear the highest burden of prostate cancer, experiencing a higher incidence and mortality rate, and poorer survivorship outcomes. Although prioritized, survivorship and quality of life (QOL) are under-studied in the fight against prostate cancer disparities. To address this issue, clinicians, researchers, and community advocates have formed the Inclusive Cancer Care Research Equity (iCCaRE) Consortium for Black Men. iCCaRE’s goal is to advance the science and practice of survivorship focused on the needs of Black men and their loved ones affected by prostate cancer. Methods: We developed a survivorship care plan (SCP) tailored to the whole person's needs. IRB-approved qualitative methods to preliminarily gain evaluation and guidance on the SCP. The SCP presented content in the following areas: diagnosis and treatment, follow-up care/surveillance, family history, care team, major co-morbidities, symptoms and quality of life, survivorship concerns, and health advisories. Results: 23 Black prostate cancer survivors (PCS) participated and provided feedback on the SCP Mean age was 64 years. PCS diagnosed between 2018-2023 between stages 1-4. The Majority of PCS were provided information for follow-up and side effects. But none had received a comprehensive SCP All agreed that the SCP would contribute to enhancing cancer knowledge, patient activation, and shared decision, and their QOL, if the SCP was obtained. A survivor noted “having inspirational quotes throughout the SCP is important”. An unforeseen consequence to the interviews was the emotional release for the men, justifying the need for the SCP and an expansion of its scope. Domains that the men wanted more contents were social support, sexuality, masculinity, and maintaining a healthy lifestyle. All PCS understood the value of having trusted family and friends to bear the burden of the disease despite some not having a solid support system. PCS emphasized the role of family support stating, “my wife and children helped me stay positive”. Another critical lifestyle component that survivors mentioned was their sexuality and how it directly reflects their masculinity. Although PCS adapted to other forms of intimacy like couples’ activities and stronger communication with their partners, majority of men struggle with adapting to their sexual performance. A quote by a survivor encompasses the mindset expressed by many of the men fear “Like a track star in his prime who can’t run anymore”. Erectile Dysfunction can lead to fear and anxiety among survivors, ultimately affecting QOL. Lastly, PCS wanted more options to stay fit and healthy. Further research and resources are needed to address survivor concerns in this area. Discussion: Overall, PCS have acknowledged that the SCP can assist them in these areas of concern, but more research is required. Life after prostate cancer treatment is an ongoing process; while some survivors cope better than others, it is evident a need for more information and guidance is not being provided to all those affected. Citation Format: James Morrison, Kimlin Ashing, Gaole Song, John McCall, Timethia J. Bonner, Che Ngufor, Getachew A. Dagne, Arnold Merriweather, Ewan Cobran, Cassandra N. Moore, Fornati Bedell, Rotimi Oladapo, Floyd B. Willis, JoAnne S. Oliver, Ernie Kaninjing, Roxana Dronca, Folakemi T. Odedina. iCCaRE qualitative interviews for Black prostate cancer survivors and quality of life [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C042.

Prevention science has increasingly turned to integrative data analysis (IDA) to combine individual participant-level data from multiple studies of the same topic, allowing us to evaluate overall effect size, test and model heterogeneity, and examine mediation. Studies included in IDA often use different measures for the same construct, leading to sparse datasets. We introduce a graph theory method for summarizing patterns of sparseness and use simulations to explore the impact of different patterns on measurement bias within three different measurement models: a single common factor, a hierarchical model, and a bifactor model. We simulated 1000 datasets with varying levels of sparseness and used Bayesian methods to estimate model parameters and evaluate bias. Results clarified that bias due to sparseness will depend on the strength of the general factor, the measurement model employed, and the level of indirect linkage among measures. We provide an example using a synthesis dataset that combined data on youth depression from 4146 youth who participated in 16 randomized field trials of prevention programs. Given that different synthesis datasets will embody different patterns of sparseness, we conclude by recommending that investigators use simulation methods to explore the potential for bias given the sparseness patterns they encounter.

We examined colorectal cancer (CRC) risk perceptions among Black men in relation to socio-demographic characteristics, disease prevention factors, and personal/family history of CRC. A self-administered cross-sectional survey was conducted in five major cities in Florida between April 2008 and October 2009. Descriptive statistics and multivariable logistic regression were performed. Among 331 eligible men, we found a higher proportion of CRC risk perceptions were exhibited among those aged ≥ 60 years (70.5

Modelling longitudinal biomarkers and time-to-event processes jointly is becoming essential in medical research and other follow-up studies in order to evaluate their association, obtain unbiased results, and make valid statistical inferences. This study was motivated by follow-up data on chronic kidney disease (CKD), which is a major global health problem. Numerous studies have been conducted in the literature to analyse and assess the kidney function of CKD patients using cross-sectional data. However, joint models on CKD follow-up data have not been extensively studied in the literature. In the construction of joint models on CKD data, most previous studies proposed mixed-effects submodels with Gaussian distributions for longitudinal outcomes. However, longitudinal outcomes may have asymmetric (skewed) distributions. Proposing a normal distribution for skewed longitudinal data may yield biased results and invalid statistical inferences. In this paper, therefore, we propose a mixed-effects joint model with a skew-t distribution for longitudinal and time-to-event data under the Bayesian approach. We assessed the performance of the proposed joint model using simulation studies and applied the model to real CKD data. The proposed joint model with a skew-t distribution was compared with joint models with skew-normal and normal distributions of model errors. The findings of the simulation and application studies showed that the proposed joint model with skew-t distribution performed well.

Objective Over the past decades, it has been understood that the availability of screening tests has contributed to a steady decline in incidence of colorectal cancer (CRC). However, it is also seen that there is a geographic disparity in the use of such tests across small areas. The aim of this study is to examine small-area level barrier factors that may impact CRC screening uptake and to delineate coldspot (low uptake of screening) counties in Florida. Methods Data on the percentages of county-level CRC screening uptakes in 2016 and county-level barrier factors for screening were obtained from the Florida Department of Health, Division of Public Health Statistics & Performance Management. Bayesian spatial beta models were used to produce posterior probability of deceedance to identify coldspots for CRC screening rates. Results Unadjusted screening rates using sigmoidoscopy or colonoscopy test ranged from 56.8 to 85%. Bayesian spatial beta models were fitted to the proportion data. At an ecological level, we found that an increasing rate of CRC screening uptake for either of the test types (colon/rectum exam, stool-based test) was strongly associated with a higher health insurance coverage, and lower percentage of population that speak English less than very well (immigration) at county level. Eleven coldspot counties out of 67 total were also identified. Conclusion This study suggests that health insurance disparities in the use of CRC screening tests are an important factor that may need more attention for resource allocation and health policy targeting small areas with low uptake of screening.

Growth curve models are often used to describe a developmental course of a longitudinal response. This paper extends such models to assess multiphasic patterns of developmental trajectories for multivariate response variables. The multiphasic patterns are identified using a bivariate bent-cable model in the context of multivariate growth models. The approach allows for the simultaneous estimation of parameters of multiphasic changes in each response, and also takes into account the correlations among outcomes and random effects for repeated observations over time. The proposed methods are demonstrated using real data from an AIDS clinical study.

Supplementary Table 2 from Inherited Variants in Mitochondrial Biogenesis Genes May Influence Epithelial Ovarian Cancer Risk

Objective: To understand the risk of unplanned hysterectomy (UH) in pregnant women better in association with maternal sociodemographic characteristics, cardiovascular disease (CVD) risk factors, and current pregnancy complications. Design: Using Florida birth data from 2005 to 2014, we investigated the possible interactions between known risk factors of having UH, including maternal sociodemographic characteristics, maternal medical history, and other pregnancy complications. Logistic regression models were constructed. Adjusted odds ratios and 95% confidence intervals were reported. Results: Several interactions were observed that significantly affected odds of UH. Compared to non-Hispanic White women, Hispanic minority women were more likely to have an UH. The overall risk of UH for women with preterm birth (<37 weeks) and concurrently had premature rupture of membranes (PRoM), uterine rupture, or a previous cesarean delivery was significantly higher than women who delivered to term and had no pregnancy complications. Women who delivered via cesarean who also had preeclampsia, PRoM, or uterine rupture had an overall increased risk of UH. Significantly decreased risk of UH was seen for Black women less than 20 years old, women of other minority races with either less than a high school degree or a college degree or greater, women of other minority races with PRoM, and women with preterm birth and diabetes compared to respective reference groups. Conclusions: Maternal race, ethnicity, CVD risk factors, and current pregnancy complications affect the risk of UH in pregnant women through complex interactions that would not be seen in unadjusted models of risk analysis.

Supplementary Figure S1 from LIN28B Polymorphisms Influence Susceptibility to Epithelial Ovarian Cancer

Background: Ultra-processed foods (UPFs) contribute to almost 60% of energy intake in the American diet. The longitudinal association between UPF consumption and cardiometabolic risk is less known, especially in people with type 1 diabetes mellites (T1DM) who are at a higher risk of developing cardiometabolic diseases than people without diabetes (non-DM). Methods: We performed a longitudinal analysis of data from the Coronary Artery Calcification in Type 1 Diabetes study (T1DM: n=652; non-DM: n=764) collected at baseline and years 3, 6, and 14. Baseline age was 37.8±9.3 years. Dietary intake was assessed using a validated semi-quantitative food frequency questionnaire, and cardiometabolic biomarkers (BMI, waist circumference, triglycerides, HDL-C, LDL-C, and systolic and diastolic blood pressure) were measured at all four time points. NOVA food classification was used to compute UPF consumption (servings/d). Linear mixed-effects models were used to estimate longitudinal associations of UPF consumption with cardiometabolic risk, adjusting for potential confounders (race, age, sex, education, physical activity, smoking, energy intake, antihypertensive and lipid-lowering drugs, and diabetes duration for T1DM). Results: People with T1DM consumed more UPFs, including processed meat, soft drinks, salty snacks, and margarine, than non-DM at baseline (8.7±7.8 vs. 7.3±6.7 servings/d, P<0.01). Higher UPF consumption was associated with higher anthropometric measures (BMI: β=0.01±0.01, waist circumference: β=0.03±0.01), worse lipid levels (triglycerides: β=0.21±0.09, HDL-C: β=-0.04±0.02), and higher systolic blood pressure (β=0.05±0.02) when adjusted for diabetes status and other covariates (all P<0.05). Conclusion: UPF consumption significantly worsened the cardiometabolic profile over time in people with and without T1DM. Decreasing UPF intake may reduce the risk of developing cardiometabolic diseases across the lifespan. Disclosure T.Pang: None. A.C.Alman: None. H.L.Gray: None. G.Dagne: None. A.Basu: None. A.W.Buro: None. J.K.Snell-bergeon: None. Funding American Diabetes Association (7-13-CD-10 to J.K.S-B.), (7-13-CE-02 to A.C.A.); National Heart, Lung, and Blood Institute (R01HL079611, R01HL113029); Diabetes Endocrinology Research Center (P30DK57516, P30DK1116073)

Supplementary Figure 1 from Inherited Variants in Mitochondrial Biogenesis Genes May Influence Epithelial Ovarian Cancer Risk