Browsing by Author "Ren, Jie"
Now showing 1 - 10 of 16
Results Per Page
Sort Options
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 Gene Expression Alterations in Peripheral Blood Following Sport-Related Concussion in a Prospective Cohort of Collegiate Athletes: A Concussion Assessment, Research and Education (CARE) Consortium Study(Springer Nature, 2024-04) Simpson, Edward; Reiter, Jill L.; Ren, Jie; Zhang, Zhiqi; Nudelman, Kelly N.; Riggen, Larry D., Jr.; Menser, Michael D.; Harezlak, Jaroslaw; Foroud, Tatiana M.; Saykin, Andrew J.; Brooks, Alison; Cameron, Kenneth L.; Duma, Stefan M.; McGinty, Gerald; Rowson, Steven; Svoboda, Steven J.; Broglio, Steven P.; McCrea, Michael A.; Pasquina, Paul F.; McAllister, Thomas W.; Liu, Yunlong; CARE Consortium Investigators; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthBackground Molecular-based approaches to understanding concussion pathophysiology provide complex biological information that can advance concussion research and identify potential diagnostic and/or prognostic biomarkers of injury. Objective The aim of this study was to identify gene expression changes in peripheral blood that are initiated following concussion and are relevant to concussion response and recovery. Methods We analyzed whole blood transcriptomes in a large cohort of concussed and control collegiate athletes who were participating in the multicenter prospective cohort Concussion Assessment, Research, and Education (CARE) Consortium study. Blood samples were collected from collegiate athletes at preseason (baseline), within 6 h of concussion injury, and at four additional prescribed time points spanning 24 h to 6 months post-injury. RNA sequencing was performed on samples from 230 concussed, 130 contact control, and 102 non-contact control athletes. Differential gene expression and deconvolution analysis were performed at each time point relative to baseline. Results Cytokine and immune response signaling pathways were activated immediately after concussion, but at later time points these pathways appeared to be suppressed relative to the contact control group. We also found that the proportion of neutrophils increased and natural killer cells decreased in the blood following concussion. Conclusions Transcriptome signatures in the blood reflect the known pathophysiology of concussion and may be useful for defining the immediate biological response and the time course for recovery. In addition, the identified immune response pathways and changes in immune cell type proportions following a concussion may inform future treatment strategies.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 Is Seeing Believing? A Practitioner’s Perspective on High-Dimensional Statistical Inference in Cancer Genomics Studies(MDPI, 2024-09-16) Fan, Kun; Subedi, Srijana; Yang, Gongshun; Lu, Xi; Ren, Jie; Wu, Cen; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthVariable selection methods have been extensively developed for and applied to cancer genomics data to identify important omics features associated with complex disease traits, including cancer outcomes. However, the reliability and reproducibility of the findings are in question if valid inferential procedures are not available to quantify the uncertainty of the findings. In this article, we provide a gentle but systematic review of high-dimensional frequentist and Bayesian inferential tools under sparse models which can yield uncertainty quantification measures, including confidence (or Bayesian credible) intervals, p values and false discovery rates (FDR). Connections in high-dimensional inferences between the two realms have been fully exploited under the "unpenalized loss function + penalty term" formulation for regularization methods and the "likelihood function × shrinkage prior" framework for regularized Bayesian analysis. In particular, we advocate for robust Bayesian variable selection in cancer genomics studies due to its ability to accommodate disease heterogeneity in the form of heavy-tailed errors and structured sparsity while providing valid statistical inference. The numerical results show that robust Bayesian analysis incorporating exact sparsity has yielded not only superior estimation and identification results but also valid Bayesian credible intervals under nominal coverage probabilities compared with alternative methods, especially in the presence of heavy-tailed model errors and outliers.Item Natural Clearance of Chlamydia trachomatis Infection Is Associated With Distinct Differences in Cervicovaginal Metabolites(Oxford University Press, 2023) Jordan, Stephen J.; Wilson, Landon; Ren, Jie; Gupta, Kanupriya; Barnes, Stephen; Geisler, William M.; Medicine, School of MedicineBackground: Natural clearance of Chlamydia trachomatis in women occurs in the interval between screening and treatment. In vitro, interferon-γ (IFN-γ)-mediated tryptophan depletion results in C. trachomatis clearance, but whether this mechanism occurs in vivo remains unclear. We previously found that women who naturally cleared C. trachomatis had lower cervicovaginal levels of tryptophan and IFN-γ compared to women with persisting infection, suggesting IFN-γ-independent pathways may promote C. trachomatis clearance. Methods: Cervicovaginal lavages from 34 women who did (n = 17) or did not (n = 17) naturally clear C. trachomatis were subjected to untargeted high-performance liquid chromatography mass-spectrometry to identify metabolites and metabolic pathways associated with natural clearance. Results: In total, 375 positively charged metabolites and 149 negatively charged metabolites were annotated. Compared to women with persisting infection, C. trachomatis natural clearance was associated with increased levels of oligosaccharides trehalose, sucrose, melezitose, and maltotriose, and lower levels of indoline and various amino acids. Metabolites were associated with valine, leucine, and isoleucine biosynthesis pathways. Conclusions: The cervicovaginal metabolome in women who did or did not naturally clear C. trachomatis is distinct. In women who cleared C. trachomatis, depletion of various amino acids, especially valine, leucine, and isoleucine, suggests that amino acids other than tryptophan impact C. trachomatis survival in vivo.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 Robust Bayesian variable selection for gene–environment interactions(Oxford University Press, 2023) Ren, Jie; Zhou, Fei; Li, Xiaoxi; Ma, Shuangge; Jiang, Yu; Wu, Cen; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthGene-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.