Browsing by Author "Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health"
Now showing 1 - 10 of 111
Results Per Page
Sort Options
Item A Bayesian Phase I/II Design to Determine Subgroup-Specific Optimal Dose for Immunotherapy Sequentially Combined with Radiotherapy(Wiley, 2023) Guo, Beibei; Zang, Yong; Lin, Li-Hsiang; Zhang, Rui; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthSequential administration of immunotherapy following radiotherapy (immunoRT) has attracted much attention in cancer research. Due to its unique feature that radiotherapy upregulates the expression of a predictive biomarker for immunotherapy, novel clinical trial designs are needed for immunoRT to identify patient subgroups and the optimal dose for each subgroup. In this article, we propose a Bayesian phase I/II design for immunotherapy administered after standard-dose radiotherapy for this purpose. We construct a latent subgroup membership variable and model it as a function of the baseline and pre-post radiotherapy change in the predictive biomarker measurements. Conditional on the latent subgroup membership of each patient, we jointly model the continuous immune response and the binary efficacy outcome using plateau models, and model toxicity using the equivalent toxicity score approach to account for toxicity grades. During the trial, based on accumulating data, we continuously update model estimates and adaptively randomize patients to admissible doses. Simulation studies and an illustrative trial application show that our design has good operating characteristics in terms of identifying both patient subgroups and the optimal dose for each subgroup.Item A Multi-Center, Single Arm, Phase Ib study of Pembrolizumab (MK-3475) in Combination with Chemotherapy for Patients with Advanced Colorectal Cancer: HCRN GI14-186(Springer, 2021) Herting, Cameron J.; Farren, Matthew R.; Tong, Yan; Liu, Ziyue; O’Neil, Bert; Bekaii-Saab, Tanios; Noonan, Anne; McQuinn, Christopher; Mace, Thomas A.; Shaib, Walid; Wu, Christina; El-Rayes, Bassel F.; Shahda, Safi; Lesinski, Gregory B.; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthModified FOLFOX6 is an established therapy for patients with metastatic colorectal cancer (mCRC). We conducted a single-arm phase Ib study to address the hypothesis that addition of pembrolizumab to this regimen could safely and effectively improve patient outcomes (NCT02375672). The relationship between immune biomarkers and clinical response were assessed in an exploratory manner. Patients with mCRC received concurrent pembrolizumab and modified FOLFOX6. The study included safety run-in for the first six patients. The primary objective was median progression-free survival (mPFS), with secondary objectives including median overall survival, safety, and exploratory assessment of immune changes. To assess immunological impact, peripheral blood was collected at baseline and during treatment. The levels of soluble factors were measured via bioplex, while a panel of checkpoint molecules and phenotypically defined cell populations were assessed with flow cytometry and correlated with RECIST and mPFS. Due to incidences of grade 3 and grade 4 neutropenia in the safety lead-in, the dose of mFOLFOX6 was reduced in the expansion cohort. Median PFS was 8.8 months and median OS was not reached at data cutoff. Best responses of stable disease, partial response, and complete response were observed in 43.3%, 50.0%, and 6.7% of patients, respectively. Several soluble and cellular immune biomarkers were associated with improved RECIST and mPFS. Immunosuppressive myeloid and T cell subsets that were analyzed were not associated with response. Primary endpoint was not superior to historic control. Biomarkers that were associated with improved response may be informative for future regimens combining chemotherapy with immune checkpoint inhibitors.Item A multistate transition model for statin‐induced myopathy and statin discontinuation(Wiley, 2021) Zhu, Yuxi; Chiang, Chien-Wei; Wang, Lei; Brock, Guy; Milks, M. Wesley; Cao, Weidan; Zhang, Pengyue; Zeng, Donglin; Donneyong, Macarius; Li, Lang; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthThe overarching goal of this study was to simultaneously model the dynamic relationships among statin exposure, statin discontinuation, and potentially statin-related myopathic outcomes. We extracted data from the Indiana Network of Patient Care for 134,815 patients who received statin therapy between January 4, 2004, and December 31, 2008. All individuals began statin treatment, some discontinued statin use, and some experienced myopathy and/or rhabdomyolysis while taking the drug or after discontinuation. We developed a militate model to characterize 12 transition probabilities among six different states defined by use or discontinuation of statin and its associated myopathy or rhabdomyolysis. We found that discontinuation of statin therapy was common and frequently early, with 44.4% of patients discontinuing therapy after 1 month, and discontinuation is a strong indicator for statin-induced myopathy (risk ratio, 10.8; p < 0.05). Women more likely than men (p < 0.05) and patients aged 65 years and older had a higher risk than those aged younger than 65 years to discontinue statin use or experience myopathy. In conclusion, we introduce an innovative multistate model that allows clear depiction of the relationship between statin discontinuation and statin-induced myopathy. For the first time, we have successfully demonstrated and quantified the relative risk of myopathy between patients who continued and discontinued statin therapy. Age and sex were two strong risk factors for both statin discontinuation and incident myopathy.Item A Typology of Social Media Use by Human Service Nonprofits: Mixed Methods Study(JMIR, 2024-05-08) Xue, Jia; Shier, Michael L.; Chen, Junxiang; Wang, Yirun; Zheng, Chengda; Chen, Chen; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthBackground: Nonprofit organizations are increasingly using social media to improve their communication strategies with the broader population. However, within the domain of human service nonprofits, there is hesitancy to fully use social media tools, and there is limited scope among organizational personnel in applying their potential beyond self-promotion and service advertisement. There is a pressing need for greater conceptual clarity to support education and training on the varied reasons for using social media to increase organizational outcomes. Objective: This study leverages the potential of Twitter (subsequently rebranded as X [X Corp]) to examine the online communication content within a sample (n=133) of nonprofit sexual assault (SA) centers in Canada. To achieve this, we developed a typology using a qualitative and supervised machine learning model for the automatic classification of tweets posted by these centers. Methods: Using a mixed methods approach that combines machine learning and qualitative analysis, we manually coded 10,809 tweets from 133 SA centers in Canada, spanning the period from March 2009 to March 2023. These manually labeled tweets were used as the training data set for the supervised machine learning process, which allowed us to classify 286,551 organizational tweets. The classification model based on supervised machine learning yielded satisfactory results, prompting the use of unsupervised machine learning to classify the topics within each thematic category and identify latent topics. The qualitative thematic analysis, in combination with topic modeling, provided a contextual understanding of each theme. Sentiment analysis was conducted to reveal the emotions conveyed in the tweets. We conducted validation of the model with 2 independent data sets. Results: Manual annotation of 10,809 tweets identified seven thematic categories: (1) community engagement, (2) organization administration, (3) public awareness, (4) political advocacy, (5) support for others, (6) partnerships, and (7) appreciation. Organization administration was the most frequent segment, and political advocacy and partnerships were the smallest segments. The supervised machine learning model achieved an accuracy of 63.4% in classifying tweets. The sentiment analysis revealed a prevalence of neutral sentiment across all categories. The emotion analysis indicated that fear was predominant, whereas joy was associated with the partnership and appreciation tweets. Topic modeling identified distinct themes within each category, providing valuable insights into the prevalent discussions surrounding SA and related issues. Conclusions: This research contributes an original theoretical model that sheds light on how human service nonprofits use social media to achieve their online organizational communication objectives across 7 thematic categories. The study advances our comprehension of social media use by nonprofits, presenting a comprehensive typology that captures the diverse communication objectives and contents of these organizations, which provide content to expand training and education for nonprofit leaders to connect and engage with the public, policy experts, other organizations, and potential service users.Item Achieving consistency in measures of HIV-1 viral suppression across countries: derivation of an adjustment based on international antiretroviral treatment cohort data(Wiley, 2021) Johnson, Leigh F.; Kariminia, Azar; Trickey, Adam; Yiannoutsos, Constantin T.; Ekouevi, Didier K.; Minga, Albert K.; Pascom, Ana Roberta Pati; Han, Win Min; Zhang, Lei; Althoff, Keri N.; Rebeiro, Peter F.; Murenzi, Gad; Ross, Jonathan; Hsiao, Nei-Yuan; Marsh, Kimberly; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthIntroduction: The third of the Joint United Nations Programme on HIV/AIDS (UNAIDS) 90-90-90 targets is to achieve a 90% rate of viral suppression (HIV viral load <1000 HIV-1 RNA copies/ml) in patients on antiretroviral treatment (ART) by 2020. However, some countries use different thresholds when reporting viral suppression, and there is thus a need for an adjustment to standardize estimates to the <1000 threshold. We aim to propose such an adjustment, to support consistent monitoring of progress towards the "third 90" target. Methods: We considered three possible distributions for viral loads in ART patients: Weibull, Pareto and reverse Weibull (imposing an upper limit but no lower limit on the log scale). The models were fitted to data on viral load distributions in ART patients in the International epidemiology Databases to Evaluate AIDS (IeDEA) collaboration (representing seven global regions) and the ART Cohort Collaboration (representing Europe), using separate random effects models for adults and children. The models were validated using data from the World Health Organization (WHO) HIV drug resistance report and the Brazilian national ART programme. Results: Models were calibrated using 921,157 adult and 37,431 paediatric viral load measurements, over 2010-2019. The Pareto and reverse Weibull models provided the best fits to the data, but for all models, the "shape" parameters for the viral load distributions differed significantly between regions. The Weibull model performed best in the validation against the WHO drug resistance survey data, while the Pareto model produced uncertainty ranges that were too narrow, relative to the validation data. Based on these analyses, we recommend using the reverse Weibull model. For example, if a country reports an 80% rate of viral suppression at <200 copies/ml, this model estimates the proportion virally suppressed at <1000 copies/ml is 88.3% (0.800.56 ), with uncertainty range 85.5-90.6% (0.800.70 -0.800.44 ). Conclusions: Estimates of viral suppression can change substantially depending on the threshold used in defining viral suppression. It is, therefore, important that viral suppression rates are standardized to the same threshold for the purpose of assessing progress towards UNAIDS targets. We have proposed a simple adjustment that allows this, and this has been incorporated into UNAIDS modelling software.Item Active and receptive arts participation and their association with mortality among adults in the United States: a longitudinal cohort study(Elsevier, 2021) Story, Kristin M.; Yang, Ziyi; Bravata, Dawn M.; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthObjectives: The aim of the study was to explore associations between active and receptive arts participation and all-cause mortality among adults in the United States population. Study design: This was a prospective cohort study. Methods: Data were derived from the Health and Retirement Study. Separate Cox proportional hazards models were constructed for two cohorts (2012 and 2014) to examine associations between arts participation and mortality. Results: Independent of sociodemographic and health factors, participants aged ≥65 years had a higher mortality risk if they did not engage in music listening, hazard ratio (HR) 1.39 (95% confidence interval [CI]: 1.12-1.71); singing/playing an instrument, HR 1.49 (95% CI: 1.07-2.0); or doing arts and crafts, HR 1.39 (95% CI: 1.00-1.92). For participants aged <65 years, there was a higher mortality risk if they did not listen to music, HR 1.79 (95% CI: 1.07-3.01). Older participants from the 2014 cohort had a higher mortality risk if they did not engage in active arts, HR 1.73 (95% CI: 1.08-2.77). Conclusions: Engagement in the arts was associated with lower risk of mortality even after risk adjustment, especially for adults aged ≥65 years. Greater access and integration of arts in everyday life is recommended.Item ACTOR: a latent Dirichlet model to compare expressed isoform proportions to a reference panel(Oxford University Press, 2023) McCabe, Sean D.; Nobel, Andrew B.; Love, Michael I.; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthThe relative proportion of RNA isoforms expressed for a given gene has been associated with disease states in cancer, retinal diseases, and neurological disorders. Examination of relative isoform proportions can help determine biological mechanisms, but such analyses often require a per-gene investigation of splicing patterns. Leveraging large public data sets produced by genomic consortia as a reference, one can compare splicing patterns in a data set of interest with those of a reference panel in which samples are divided into distinct groups, such as tissue of origin, or disease status. We propose A latent Dirichlet model to Compare expressed isoform proportions TO a Reference panel (ACTOR), a latent Dirichlet model with Dirichlet Multinomial observations to compare expressed isoform proportions in a data set to an independent reference panel. We use a variational Bayes procedure to estimate posterior distributions for the group membership of one or more samples. Using the Genotype-Tissue Expression project as a reference data set, we evaluate ACTOR on simulated and real RNA-seq data sets to determine tissue-type classifications of genes. ACTOR is publicly available as an R package at https://github.com/mccabes292/actor.Item Adaptive phase I-II clinical trial designs identifying optimal biological doses for targeted agents and immunotherapies(Sage, 2024) Zang, Yong; Guo, Beibei; Qiu, Yingjie; Liu, Hao; Opyrchal, Mateusz; Lu, Xiongbin; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthTargeted agents and immunotherapies have revolutionized cancer treatment, offering promising options for various cancer types. Unlike traditional therapies the principle of "more is better" is not always applicable to these new therapies due to their unique biomedical mechanisms. As a result, various phase I-II clinical trial designs have been proposed to identify the optimal biological dose that maximizes the therapeutic effect of targeted therapies and immunotherapies by jointly monitoring both efficacy and toxicity outcomes. This review article examines several innovative phase I-II clinical trial designs that utilize accumulated efficacy and toxicity outcomes to adaptively determine doses for subsequent patients and identify the optimal biological dose, maximizing the overall therapeutic effect. Specifically, we highlight three categories of phase I-II designs: efficacy-driven, utility-based, and designs incorporating multiple efficacy endpoints. For each design, we review the dose-outcome model, the definition of the optimal biological dose, the dose-finding algorithm, and the software for trial implementation. To illustrate the concepts, we also present two real phase I-II trial examples utilizing the EffTox and ISO designs. Finally, we provide a classification tree to summarize the designs discussed in this article.Item ADHD-related sex differences in emotional symptoms across development(Springer, 2024) De Ronda, Alyssa C.; Rice, Laura; Zhao, Yi; Rosch, Keri S.; Mostofsky, Stewart H.; Seymour, Karen E.; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthTo investigate developmental changes in emotion dysregulation (ED) and associated symptoms of emotional lability, irritability, anxiety, and depression, among girls and boys with and without ADHD from childhood through adolescence. Data were collected from a sample of 8-18-year-old children with (n = 264; 76 girls) and without (n = 153; 56 girls) ADHD, with multiple time-points from a subsample of participants (n = 121). Parents and youth completed rating scales assessing child ED, emotional lability, irritability, anxiety, and depression. Mixed effects models were employed to examine effects and interactions of diagnosis, sex [biological sex assigned at birth], age among boys and girls with and without ADHD. Mixed effects analyses showed sexually dimorphic developmental patterns between boys and girls, such that boys with ADHD showed a greater reduction in ED, irritability, and anxiety with age compared to girls with ADHD, whose symptom levels remained elevated relative to TD girls. Depressive symptoms were persistently elevated among girls with ADHD compared to boys with ADHD, whose symptoms decreased with age, relative to same-sex TD peers. While both boys and girls with ADHD showed higher levels of ED during childhood (compared to their sex-matched TD peers), mixed effects analyses revealed substantial sexually dimorphic patterns of emotional symptom change during adolescence: Boys with ADHD showed robust improvements in emotional symptoms from childhood to adolescence while girls with ADHD continued to show high and/or increased levels of ED, emotional lability, irritability, anxiety and depression.Item Admission Factor V Predicts Transplant-Free Survival in Acute Liver Failure(Springer, 2021) Patidar, Kavish R.; Davis, Brian C.; Slaven, James E.; Ghabril, Marwan S.; Kubal, Chandrashekhar A.; Lee, William M.; Stravitz, Richard T.; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthBackground and aims: Traditional laboratory markers are insensitive in distinguishing between patients with acute liver failure (ALF) who will require urgent liver transplantation (LT) from those who will recover spontaneously, particularly within 24 h of presentation. Coagulation factor-V (FV) may improve the accuracy of outcome prediction in ALF due to its predominant synthesis in the liver and short half-life in plasma. Methods: Patients enrolled in the ALF Study Group Registry from a single site had FV determined within 24 h of presentation (Derivation-Cohort). Area under the receiver operating characteristic curves (AUROC) dichotomized by ALF etiology [acetaminophen (APAP) or non-APAP] were constructed to evaluate the diagnostic performance of FV for transplant-free-survival (TFS). Multivariate logistic regression modeling was performed using FV and other clinical variables to predict TFS. Accuracy of FV and multivariable model were performed in a Validation-Cohort from a different site. Results: 90-patients (56% with APAP) were included in the Derivation-Cohort. Median FV was significantly higher in TFS versus those who died/LT (31% vs. 15%, respectively; p = 0.001). When dichotomized by etiology, AUROC for FV was 0.77 for APAP (cutoff, sensitivity, specificity 10.5%, 79%, 69%, respectively) and 0.77 for non-APAP (22%, 85%, 67%, respectively). When the optimal cutoffs for FV in the Derivation-Cohort were applied to the Validation-Cohort (N = 51; 59% with APAP), AUROC for FV was 0.75 for APAP (sensitivity/specificity 81/44) and 0.95 for non-APAP (sensitivity/specificity 90/73). In multivariate analyses, AUROC for FV model was 0.86 in the Derivation-Cohort and 0.90 in the Validation-Cohort. Conclusion: Admission FV may improve selection of patients who are likely to improve without LT.