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Browsing by Author "Ren, Jie"
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Item Bayesian Adaptive Designs for Early Phase Clinical Trials(2023-07) Guo, Jiaying; Zang, Yong; Han, Jiali; Zhao, Yi; Ren, JieDelayed toxicity outcomes are common in phase I clinical trials, especially in oncology studies. It causes logistic difficulty, wastes resources, and prolongs the trial duration. We propose the time-to-event 3+3 (T-3+3) design to solve the delayed outcome issue for the 3+3 design. We convert the dose decision rules of the 3+3 design into a series of events. A transparent yet efficient Bayesian probability model is applied to calculate the event happening probabilities in the presence of delayed outcomes, which incorporates the informative pending patients' remaining follow-up time into consideration. The T-3+3 design only models the information for the pending patients and seamlessly reduces to the conventional 3+3 design in the absence of delayed outcomes. We further extend the proposed method to interval 3+3 (i3+3) design, an algorithm-based phase I dose-finding design which is based on simple but more comprehensive rules that account for the variabilities in the observed data. Similarly, the dose escalation/deescalation decision is recommended by comparing the event happening probabilities which are calculated by considering the ratio between the averaged follow-up time for at-risk patients and the total assessment window. We evaluate the operating characteristics of the proposed designs through simulation studies and compare them to existing methods. The umbrella trial is a clinical trial strategy that accommodates the paradigm shift towards personalized medicine, which evaluates multiple investigational drugs in different subgroups of patients with the same disease. A Bayesian adaptive umbrella trial design is proposed to select effective targeted agents for different biomarker-based subgroups of patients. To facilitate treatment evaluation, the design uses a mixture regression model that jointly models short-term and long-term response outcomes. In addition, a data-driven latent class model is employed to adaptively combine subgroups into induced latent classes based on overall data heterogeneities, which improves the statistical power of the umbrella trial. To enhance individual ethics, the design includes a response-adaptive randomization scheme with early stopping rules for futility and superiority. Bayesian posterior probabilities are used to make these decisions. Simulation studies demonstrate that the proposed design outperforms two conventional designs across a range of practical treatment-outcome scenarios.Item Coordinating cardiomyocyte interactions to direct ventricular chamber morphogenesis(SpringerNature, 2016-06-30) Han, Peidong; Bloomekatz, Joshua; Ren, Jie; Zhang, Ruilin; Grinstein, Jonathan D.; Zhao, Long; Burns, C. Geoffrey; Burns, Caroline E.; Anderson, Ryan M.; Chi, Neil C.; Department of Pediatrics, IU School of MedicineMany organs are composed of complex tissue walls that are structurally organized to optimize organ function. In particular, the ventricular myocardial wall of the heart comprises an outer compact layer that concentrically encircles the ridge-like inner trabecular layer. Although disruption in the morphogenesis of this myocardial wall can lead to various forms of congenital heart disease and non-compaction cardiomyopathies, it remains unclear how embryonic cardiomyocytes assemble to form ventricular wall layers of appropriate spatial dimensions and myocardial mass. Here we use advanced genetic and imaging tools in zebrafish to reveal an interplay between myocardial Notch and Erbb2 signalling that directs the spatial allocation of myocardial cells to their proper morphological positions in the ventricular wall. Although previous studies have shown that endocardial Notch signalling non-cell-autonomously promotes myocardial trabeculation through Erbb2 and bone morphogenetic protein (BMP) signalling, we discover that distinct ventricular cardiomyocyte clusters exhibit myocardial Notch activity that cell-autonomously inhibits Erbb2 signalling and prevents cardiomyocyte sprouting and trabeculation. Myocardial-specific Notch inactivation leads to ventricles of reduced size and increased wall thickness because of excessive trabeculae, whereas widespread myocardial Notch activity results in ventricles of increased size with a single-cell-thick wall but no trabeculae. Notably, this myocardial Notch signalling is activated non-cell-autonomously by neighbouring Erbb2-activated cardiomyocytes that sprout and form nascent trabeculae. Thus, these findings support an interactive cellular feedback process that guides the assembly of cardiomyocytes to morphologically create the ventricular myocardial wall and more broadly provide insight into the cellular dynamics of how diverse cell lineages organize to create form.Item Evaluation of Clinical, Gram Stain, and Microbiological Cure Outcomes in Men Receiving Azithromycin for Acute Nongonococcal Urethritis: Discordant Cures Are Associated With Mycoplasma genitalium Infection(Wolters Kluwer, 2022-01) Toh, Evelyn; Gao, Xiang; Williams, James A.; Batteiger, Teresa A.; Coss, Lisa A.; LaPradd, Michelle; Ren, Jie; Geisler, William M.; Xing, Yue; Dong, Qunfeng; Nelson, David E.; Jordan, Stephen J.; Microbiology and Immunology, School of MedicineBackground In men with nongonococcal urethritis (NGU), clinicians and patients rely on clinical cure to guide the need for additional testing/treatment and when to resume sex, respectively; however, discordant clinical and microbiological cure outcomes do occur. How accurately clinical cure reflects microbiological cure in specific sexually transmitted infections (STIs) is unclear. Methods Men with NGU were tested for Neisseria gonorrhoeae, Chlamydia trachomatis (CT), Mycoplasma genitalium (MG), Trichomonas vaginalis, urethrotropic Neisseria meningitidis ST-11 clade strains, and Ureaplasma urealyticum (UU). Men received azithromycin 1 g and returned for a 1-month test-of-cure visit. In MG infections, we evaluated for the presence of macrolide resistance-mediating mutations (MRMs) and investigated alternate hypotheses for microbiological treatment failure using in situ shotgun metagenomic sequencing, phylogenetic analysis, multilocus sequence typing analyses, and quantitative PCR. Results Of 280 men with NGU, 121 were included in this analysis. In the monoinfection group, 52 had CT, 16 had MG, 7 had UU, 10 had mixed infection, and 36 men had idiopathic NGU. Clinical cure rates were 85% for CT, 100% for UU, 50% for MG, and 67% for idiopathic NGU. Clinical cure accurately predicted microbiological cure for all STIs, except MG. Discordant results were significantly associated with MG-NGU and predominantly reflected microbiological failure in men with clinical cure. Mycoplasma genitalium MRMs, but not MG load or strain, were strongly associated with microbiological failure. Conclusions In azithromycin-treated NGU, clinical cure predicts microbiological cure for all STIs, except MG. Nongonococcal urethritis management should include MG testing and confirmation of microbiological cure in azithromycin-treated MG-NGU when MRM testing is unavailable.Item Identifying Gene–Environment Interactions With Robust Marginal Bayesian Variable Selection(Frontiers Media, 2021-12-08) Lu, Xi; Fan, Kun; Ren, Jie; Wu, Cen; Biostatistics & Health Data Science, School of MedicineIn high-throughput genetics studies, an important aim is to identify gene-environment interactions associated with the clinical outcomes. Recently, multiple marginal penalization methods have been developed and shown to be effective in G×E studies. However, within the Bayesian framework, marginal variable selection has not received much attention. In this study, we propose a novel marginal Bayesian variable selection method for G×E studies. In particular, our marginal Bayesian method is robust to data contamination and outliers in the outcome variables. With the incorporation of spike-and-slab priors, we have implemented the Gibbs sampler based on Markov Chain Monte Carlo (MCMC). The proposed method outperforms a number of alternatives in extensive simulation studies. The utility of the marginal robust Bayesian variable selection method has been further demonstrated in the case studies using data from the Nurse Health Study (NHS). Some of the identified main and interaction effects from the real data analysis have important biological implications.Item Interep: An R Package for High-Dimensional Interaction Analysis of the Repeated Measurement Data(MDPI, 2022-03-19) Zhou, Fei; Ren, Jie; Liu, Yuwen; Li, Xiaoxi; Wang, Weiqun; Wu, Cen; Biostatistics and Health Data Science, School of MedicineWe introduce interep, an R package for interaction analysis of repeated measurement data with high-dimensional main and interaction effects. In G × E interaction studies, the forms of environmental factors play a critical role in determining how structured sparsity should be imposed in the high-dimensional scenario to identify important effects. Zhou et al. (2019) (PMID: 31816972) proposed a longitudinal penalization method to select main and interaction effects corresponding to the individual and group structure, respectively, which requires a mixture of individual and group level penalties. The R package interep implements generalized estimating equation (GEE)-based penalization methods with this sparsity assumption. Moreover, alternative methods have also been implemented in the package. These alternative methods merely select effects on an individual level and ignore the group-level interaction structure. In this software article, we first introduce the statistical methodology corresponding to the penalized GEE methods implemented in the package. Next, we present the usage of the core and supporting functions, which is followed by a simulation example with R codes and annotations. The R package interep is available at The Comprehensive R Archive Network (CRAN).Item Robust Bayesian variable selection for gene-environment interactions(Wiley, 2022-06) Ren, Jie; Zhou, Fei; Li, Xiaoxi; Ma, Shuangge; Jiang, Yu; Wu, Cen; Biostatistics and Health Data Science, School of MedicineGene–environment (G× E) interactions have important implications to elucidate the etiology of complex diseases beyond the main genetic and environmental effects. Outliers and data contamination in disease phenotypes of G× E studies have been commonly encountered, leading to the development of a broad spectrum of robust regularization methods. Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing studies. We develop a fully Bayesian robust variable selection method for G× E interaction studies. The proposed Bayesian method can effectively accommodate heavy-tailed errors and outliers in the response variable while conducting variable selection by accounting for structural sparsity. In particular, for the robust sparse group selection, the spike-and-slab priors have been imposed on both individual and group levels to identify important main and interaction effects robustly. An efficient Gibbs sampler has been developed to facilitate fast computation. Extensive simulation studies, analysis of diabetes data with single-nucleotide polymorphism measurements from the Nurses' Health Study, and The Cancer Genome Atlas melanoma data with gene expression measurements demonstrate the superior performance of the proposed method over multiple competing alternatives.Item Sparse group variable selection for gene-environment interactions in the longitudinal study(Wiley, 2022) Zhou, Fei; Lu, Xi; Ren, Jie; Fan, Kun; Ma, Shuangge; Wu, Cen; Biostatistics and Health Data Science, School of MedicinePenalized variable selection for high dimensional longitudinal data has received much attention as it can account for the correlation among repeated measurements while providing additional and essential information for improved identification and prediction performance. Despite the success, in longitudinal studies, the potential of penalization methods is far from fully understood for accommodating structured sparsity. In this article, we develop a sparse group penalization method to conduct the bi-level gene-environment (G×E) interaction study under the repeatedly measured phenotype. Within the quadratic inference function (QIF) framework, the proposed method can achieve simultaneous identification of main and interaction effects on both the group and individual level. Simulation studies have shown that the proposed method outperforms major competitors. In the case study of asthma data from the Childhood Asthma Management Program (CAMP), we conduct G×E study by using high dimensional SNP data as genetic factors and the longitudinal trait, forced expiratory volume in one second (FEV1), as the phenotype. Our method leads to improved prediction and identification of main and interaction effects with important implications.Item Springer: An R package for bi-level variable selection of high-dimensional longitudinal data(Frontiers Media, 2023-04-06) Zhou, Fei; Liu, Yuwen; Ren, Jie; Wang, Weiqun; Wu, Cen; Biostatistics and Health Data Science, School of MedicineIn high-dimensional data analysis, the bi-level (or the sparse group) variable selection can simultaneously conduct penalization on the group level and within groups, which has been developed for continuous, binary, and survival responses in the literature. Zhou et al. (2022) (PMID: 35766061) has further extended it under the longitudinal response by proposing a quadratic inference function-based penalization method in gene–environment interaction studies. This study introduces “springer,” an R package implementing the bi-level variable selection within the QIF framework developed in Zhou et al. (2022). In addition, R package “springer” has also implemented the generalized estimating equation-based sparse group penalization method. Alternative methods focusing only on the group level or individual level have also been provided by the package. In this study, we have systematically introduced the longitudinal penalization methods implemented in the “springer” package. We demonstrate the usage of the core and supporting functions, which is followed by the numerical examples and discussions. R package “springer” is available at https://cran.r-project.org/package=springer.Item Symptom clusters in breast cancer survivors with and without type 2 diabetes over the cancer trajectory(Elsevier, 2023-11-14) Storey, Susan; Luo, Xiao; Ren, Jie; Huang, Kun; Von Ah, Diane; School of NursingObjective: This study aimed to investigate symptoms and symptom clusters in breast cancer survivors (BCS) with and without type 2 diabetes across three crucial periods during the cancer trajectory (0-6 months, 12-18 months, and 24-30 months) post-initial chemotherapy. Methods: Eight common symptoms in both BCS and individuals with diabetes were identified through natural language processing of electronic health records from January 2007 to December 2018. Exploratory factor analysis was employed to discern symptom clusters, evaluating their stability, consistency, and clinical relevance. Results: Among the 4601 BCS in the study, 20% (n = 905) had a diabetes diagnosis. Gastrointestinal symptoms and fatigue were prevalent in both groups. While BCS in both groups exhibited an equal number of clusters, the composition of these clusters differed. Symptom clusters varied over time between BCS with and without diabetes. BCS with diabetes demonstrated less stability (repeated clusters) and consistency (same individual symptoms comprising clusters) than their counterparts without diabetes. This suggests that BCS with diabetes may experience distinct symptom clusters at pivotal points in the cancer treatment trajectory. Conclusions: Healthcare providers must be attentive to BCS with diabetes throughout the cancer trajectory, considering intensified and/or unique profiles of symptoms and symptom clusters. Interdisciplinary cancer survivorship models are essential for effective diabetes management in BCS. Implementing a comprehensive diabetes management program throughout the cancer trajectory could alleviate symptoms and symptom clusters, ultimately enhancing health outcomes and potentially reducing healthcare resource utilization.Item Two Streptococcus pyogenes emm types and several anaerobic bacterial species are associated with idiopathic cutaneous ulcers in children after community-based mass treatment with azithromycin(Public Library of Science, 2022-12-19) Griesenauer, Brad; Xing, Yue; Fortney, Katherine R.; Gao, Xiang; González-Beiras, Camila; Nelson, David E.; Ren, Jie; Mitjà, Oriol; Dong, Qunfeng; Spinola, Stanley M.; Microbiology and Immunology, School of MedicineBackground: In yaws-endemic areas, two-thirds of exudative cutaneous ulcers (CU) are associated with Treponema pallidum subsp. pertenue (TP) and Haemophilus ducreyi (HD); one-third are classified as idiopathic ulcers (IU). A yaws eradication campaign on Lihir Island in Papua New Guinea utilizing mass drug administration (MDA) of azithromycin initially reduced but failed to eradicate yaws; IU rates remained constant throughout the study. Using 16S rRNA gene sequencing, we previously determined that Streptococcus pyogenes was associated with some cases of IU. Here, we applied shotgun metagenomics to the same samples we analyzed previously by 16S rRNA sequencing to verify this result, identify additional IU-associated microorganisms, and determine why S. pyogenes-associated IU might have persisted after MDA of azithromycin. Methodology/principal findings: We sequenced DNA extracted from 244 CU specimens separated into four groups based upon microorganism-specific PCR results (HD+, TP+, TP+HD+, and TP-HD- or IU). S. pyogenes was enriched in IU (24.71% relative abundance [RA]) specimens compared to other ulcer sub-groups, confirming our prior results. We bioinformatically identified the emm (M protein gene) types found in the S. pyogenes IU specimens and found matches to emm156 and emm166. Only ~39% of IU specimens contained detectable S. pyogenes, suggesting that additional organisms could be associated with IU. In the sub-set of S. pyogenes-negative IU specimens, Criibacterium bergeronii, a member of the Peptostreptococcaceae, and Fusobacterium necrophorum (7.07% versus 0.00% RA and 2.18% versus 0.00% RA, respectively), were enriched compared to the S. pyogenes-positive sub-set. Although a broad range of viruses were detected in the CU specimens, none were specifically associated with IU. Conclusions/significance: Our observations confirm the association of S. pyogenes with IU in yaws-endemic areas, and suggest that additional anaerobic bacteria, but not other microorganisms, may be associated with this syndrome. Our results should aid in the design of diagnostic tests and selective therapies for CU.