Browsing by Author "Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health"
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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 generalized phase 1-2-3 design integrating dose optimization with confirmatory treatment comparison(Oxford University Press, 2024) Zang, Yong; Thall, Peter F.; Yuan, Ying; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthA generalized phase 1-2-3 design, Gen 1-2-3, that includes all phases of clinical treatment evaluation is proposed. The design extends and modifies the design of Chapple and Thall (2019), denoted by CT. Both designs begin with a phase 1-2 trial including dose acceptability and optimality criteria, and both select an optimal dose for phase 3. The Gen 1-2-3 design has the following key differences. In stage 1, it uses phase 1-2 criteria to identify a set of candidate doses rather than 1 dose. In stage 2, which is intermediate between phase 1-2 and phase 3, it randomizes additional patients fairly among the candidate doses and an active control treatment arm and uses survival time data from both stage 1 and stage 2 patients to select an optimal dose. It then makes a Go/No Go decision of whether or not to conduct phase 3 based on the predictive probability that the selected optimal dose will provide a specified substantive improvement in survival time over the control. A simulation study shows that the Gen 1-2-3 design has desirable operating characteristics compared to the CT design and 2 conventional designs.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 New Method of Peak Detection for Analysis of Comprehensive Two-Dimensional Gas Chromatography Mass Spectrometry Data(Duke University Press, 2014) Kim, Seongho; Ouyang, Ming; Jeong, Jaesik; Shen, Changyu; Zhang, Xiang; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthWe develop a novel peak detection algorithm for the analysis of comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOF MS) data using normal-exponential-Bernoulli (NEB) and mixture probability models. The algorithm first performs baseline correction and denoising simultaneously using the NEB model, which also defines peak regions. Peaks are then picked using a mixture of probability distribution to deal with the co-eluting peaks. Peak merging is further carried out based on the mass spectral similarities among the peaks within the same peak group. The algorithm is evaluated using experimental data to study the effect of different cut-offs of the conditional Bayes factors and the effect of different mixture models including Poisson, truncated Gaussian, Gaussian, Gamma, and exponentially modified Gaussian (EMG) distributions, and the optimal version is introduced using a trial-and-error approach. We then compare the new algorithm with two existing algorithms in terms of compound identification. Data analysis shows that the developed algorithm can detect the peaks with lower false discovery rates than the existing algorithms, and a less complicated peak picking model is a promising alternative to the more complicated and widely used EMG mixture models.Item A phase II study evaluating safety and efficacy of niraparib in patients with previously treated homologous recombination defective metastatic esophageal/gastroesophageal junction/proximal gastric adenocarcinoma(Frontiers Media, 2024-11-21) Khalid, Ahmed Bilal; Fountzilas, Christos; Burney, Heather N.; Mamdani, Hirva; Schneider, Bryan P.; Fausel, Christopher; Perkins, Susan M.; Jalal, Shadia; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthIntroduction: Esophageal adenocarcinoma (EAC) remains a devastating disease and second line treatment options in the metastatic space are limited. Homologous recombination (HR) defects have been described in EAC in up to 40% of patients. Poly (ADP-ribose) polymerase (PARP)1 and PARP2 inhibitors have shown efficacy in HR defective prostate and ovarian cancers. Here, we describe the activity of the PARP inhibitor niraparib in metastatic EAC with HR defects. Methods: In this single arm Simon two-stage Phase II study, we assessed the safety and efficacy of niraparib in patients with metastatic EAC previously treated with platinum containing chemotherapy harboring defective HR. Defective HR was defined as deleterious alterations in the following HR genes: BRCA1/2, PALB2, ATM, BARD1, BRIP1, CDK12, CHEK2, FANCA, RAD51, RAD51B, RAD51C, RAD51D, RAD54L, NBN, ARID1A and GEN1. Results: 14 patients were enrolled in this study. The trial was stopped early due to slow accrual. 3 patients did not have post-treatment scans because of rapid clinical decline. The overall response rate (ORR) (95% exact CI) was 0/11 = 0% (0%, 28.49%). The disease control rate (DCR) (95% exact CI) was 2/11 = 18.2% (2.3%, 51.8%). The median PFS was 1.8 months (95% CI = 1.0-3.7). The median OS for evaluable patients was 6.6 months (95% CI =2.7-11.4) and 5.7 months for all patients (95% CI =2.7-10.1). The most common adverse events seen were anemia, fatigue, and thrombocytopenia. Conclusion: In patients with metastatic EAC, single agent niraparib as second line therapy is not an effective option.Item A pilot study protocol of a relational coordination training intervention among healthcare professionals in an Army medical center(Springer Nature, 2025-03-04) House, Sherita; Perkins, Susan M.; Miller, Melissa; Taylor‑Clark, Tanekkia; Newhouse, Robin; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthBackground: As patient care becomes more complex, high-quality communication and relationships among healthcare professionals are critical to coordinating care. Relational coordination (RC), a process of high-quality communication supported by shared goals, shared knowledge, and mutual respect, is positively associated with better patient (e.g., quality of care) and staff (e.g., job satisfaction, and retention) outcomes. A few researchers have found that communication skills training improves RC in civilian hospitals. However, researchers have not tested the feasibility of conducting communication skills training based on the RC framework among healthcare professionals in military hospitals. To address this gap, we propose conducting an RC training intervention in a military hospital. The primary aim of the proposed pilot study is to determine the feasibility (e.g., recruitment, retention, and completion rates) of conducting an RC training intervention in an Army medical center. The secondary aim is to explore the acceptability and usability of the RC training intervention. We will also explore changes in RC, quality of care, job satisfaction, and intent to stay among participants following the RC training intervention. Methods: A single-group feasibility study will be conducted among nurses and physicians from three units (intensive care unit, medical-surgical, and labor and delivery unit). A convenience sample of licensed practical nurses (LPNs), registered nurses (RNs), resident physicians, and physicians from the participating units will be invited to complete a 1-h RC training intervention once a month for 3 months. Participants will complete RC, quality of care, job satisfaction, and intent to stay measures at baseline and 2 weeks after each RC training intervention session. To assess the feasibility of conducting an RC training intervention, we will examine recruitment/retention rates, intervention session completion rates, and survey measure completion rates. Acceptability will be assessed qualitatively through focus group interviews, and results will be used to refine the intervention and determine if the selected measures align with participant experiences. For our secondary aim, we will explore the acceptability of the RC training intervention through focus group interviews. We will also explore changes in outcome measures using descriptive statistics with 95% confidence intervals. Discussion: Findings will establish the feasibility and acceptability of conducting an RC intervention in a military hospital and inform refinement of the intervention and study procedures prior to conducting a larger randomized controlled trial to establish efficacy.Item A randomized double-blind placebo-controlled trial of intravenous thiamine for prevention of delirium following allogeneic hematopoietic stem cell transplantation(Elsevier, 2021) Nakamura, Zev M.; Deal, Allison M.; Park, Eliza M.; Quillen, Laura J.; Chien, Stephanie A.; Stanton, Kate E.; McCabe, Sean D.; Heiling, Hillary M.; Wood, William A.; Shea, Thomas C.; Rosenstein, Donald L.; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthObjective: To determine if high dose intravenous (IV) thiamine can prevent delirium during hospitalization following allogeneic HSCT. Secondarily, we evaluated the effects of high dose IV thiamine on thiamine levels and explored risk factors for delirium. Methods: Randomized, double-blind, placebo-controlled trial in patients undergoing allogeneic HSCT at a U.S. academic medical center between October 2017 and March 2020. 64 participants were randomized 1:1 to thiamine 200 mg IV three times daily for 7 days or placebo. We used the Delirium Rating Scale to assess for delirium. Delirium incidence was compared between groups using the chi-square test. Group differences in time to onset and duration of delirium were compared using the Kaplan-Meier method. Fisher's Exact and Wilcoxon Rank Sum tests were used to examine associations between pre-transplantation variables and delirium. Results: 61 participants were analyzed. Delirium incidence (25% vs. 21%, Chi-square (df = 1) = 0.12, p = 0.73), time to onset, duration, and severity were not different between study arms. Immediately following the intervention, thiamine levels were higher in the thiamine arm (275 vs. 73 nmol/L, t-test (df = 57) = 13.63, p < 0.0001), but not predictive of delirium. Variables associated with delirium in our sample included disease severity, corticosteroid exposure, infection, and pre-transplantation markers of nutrition. Conclusion: High dose IV thiamine did not prevent delirium in patients receiving allogeneic HSCT. Given the multiple contributors to delirium in this population, further research regarding the efficacy of multicomponent interventions may be needed.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 Accounting for network noise in graph-guided Bayesian modeling of structured high-dimensional data(Oxford University Press, 2024) Li, Wenrui; Chang, Changgee; Kundu, Suprateek; Long, Qi; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthThere is a growing body of literature on knowledge-guided statistical learning methods for analysis of structured high-dimensional data (such as genomic and transcriptomic data) that can incorporate knowledge of underlying networks derived from functional genomics and functional proteomics. These methods have been shown to improve variable selection and prediction accuracy and yield more interpretable results. However, these methods typically use graphs extracted from existing databases or rely on subject matter expertise, which are known to be incomplete and may contain false edges. To address this gap, we propose a graph-guided Bayesian modeling framework to account for network noise in regression models involving structured high-dimensional predictors. Specifically, we use 2 sources of network information, including the noisy graph extracted from existing databases and the estimated graph from observed predictors in the dataset at hand, to inform the model for the true underlying network via a latent scale modeling framework. This model is coupled with the Bayesian regression model with structured high-dimensional predictors involving an adaptive structured shrinkage prior. We develop an efficient Markov chain Monte Carlo algorithm for posterior sampling. We demonstrate the advantages of our method over existing methods in simulations, and through analyses of a genomics dataset and another proteomics dataset for Alzheimer's disease.