4期

最新文章

• DEMO: DOSE EXPLORATION, MONITORING, AND OPTIMIZATION USING BIOLOGICAL AND CLINICAL OUTCOMES AI翻译
  作者：Yang, Cheng-Han; Thall, Peter F.; Lin, Ruitao
  作者单位：Yale University; University of Texas System; UTMD Anderson Cancer Center
  摘要：Phase 1-2 designs provide a methodological advance over phase 1 designs for dose finding by incorporating both early clinical response and toxicity. However, a phase 1-2 trial may still fail to select the truly optimal dose because early response is not a perfect surrogate for long-term therapeutic success. To address this problem, a generalized phase 1-2 design first uses a phase 1-2 design's components to identify a set of candidate doses, adaptively randomizes patients among the candidates,...
• LEVERAGING AN OBSERVED-DATA LIKELIHOOD IMPROVES THE USE OF MACHINE LEARNING LABELS IN A BAYESIAN HIERARCHICAL MODEL FOR BIOACOUSTIC DATA AI翻译
  作者：Oram, Jacob K.; Banner, Katharine M.; Stratton, Christian; Hoegh, Andrew; Irvine, Kathryn M.
  作者单位：Montana State University System; Montana State University Bozeman; Middlebury College; United States Department of the Interior; United States Geological Survey
  摘要：Classification of massive datasets by machine learning (ML) algorithms is promising for many scientific domains, especially wildlife monitoring programs that rely on passive acoustic surveys for detecting species. However, treating ML-predicted class labels (e.g., species identity) as truth biases inferences of focal parameters within common modeling frameworks. One solution is to model the misclassification process explicitly using human-validated true-class labels for a subset of observation...
• BOOTSTRAPPING THE CROSS-VALIDATION ESTIMATE AI翻译
  作者：Cai, Bryan; Luo, Yuanhui; Guo, Xinzhou; Pellegrini, Fabio; Pang, Menglan; De Moor, Carl; Shen, Changyu; Charu, Vivek; Tian, Lu
  作者单位：Stanford University; Hong Kong University of Science & Technology; Biogen; Stanford Medicine; Stanford University; Stanford University
  摘要：Cross-validation is a widely used technique for evaluating the performance of prediction models, ranging from simple binary classification to complex precision medicine strategies. It helps correct for optimism bias in error estimates, which can be significant for models built using complex statistical learning algorithms. However, since the cross-validation estimate is a random value dependent on observed data, it is essential to accurately quantify the uncertainty associated with the estimat...
• SIMULTANEOUS ADJUSTMENT OF NONIGNORABLE VERIFICATION BIAS AND IMPERFECT GOLD STANDARD BIAS IN DIAGNOSTIC TEST STUDIES, WITH AN APPLICATION TO ALZHEIMER'S DISEASE DETECTION AI翻译
  作者：Tai, Qianchen; Zhou, Xiao-Hua
  作者单位：Peking University; Peking University; Peking University
  摘要：Imperfect gold standard bias arises when an imperfect reference test is used as if it were a gold standard to evaluate the accuracy of diagnostic tests. Additionally, if this imperfect reference standard is only verified in a sub-population that is not representative of the whole sample and the missingness mechanism depends on the unknown disease status, nonignorable verification bias emerges. Some existing works explored the simultaneous adjustment of verification bias and imperfect gold stan...
• INFERRING EPIDEMICS FROM MULTIPLE DEPENDENT DATA VIA PSEUDO-MARGINAL METHODS AI翻译
  作者：Corbella, Alice; Presanis, Anne M.; Birrell, Paul J.; De Angelis, Daniela
  作者单位：University of Warwick; University of Cambridge; MRC Biostatistics Unit; UK Health Security Agency (UKHSA)
  摘要：Health-policy planning requires evidence on the burden that epidemics place on healthcare systems. Multiple, often dependent, datasets provide a noisy and fragmented signal from the unobserved epidemic process including transmission and severity dynamics. This paper explores important challenges to the use of state-space models for epidemic inference when multiple dependent datasets are analysed. We propose a new semistochastic model that exploits deterministic approximations for large-scale t...
• A FINITE POPULATION PARAMETRIC BAYESIAN BOOTSTRAP FOR QUICK COUNTS AI翻译
  作者：Rodriguez, Carlos E.; Walker, Stephen G.; Mena, Ramses H.
  作者单位：Universidad Nacional Autonoma de Mexico; University of Texas System; University of Texas Austin
  摘要：The theoretical backdrop to this paper is the finite population Bayesian bootstrap and recent developments on martingale posterior distributions. From these ideas we introduce a novel method for Bayesian parametric finite population inference. The proposed approach, which is model-based and designed to facilitate uncertainty quantification, allows for the estimation of any quantity of interest from the finite population. Specifically, the unobserved part of the finite population is imputed via...
• SPATIAL HYPERSPHERIC MODELS FOR COMPOSITIONAL DATA AI翻译
  作者：Schwob, Michael R.; Hooten, Mevin B.; Calzada, Nicholas M.; Keitt, Timothy H.
  作者单位：Virginia Polytechnic Institute & State University; University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin; University System of Ohio; Ohio State University
  摘要：Compositional observations are an increasingly prevalent data source in spatial statistics. Analysis of such data is typically done on log-ratio transformations or via Dirichlet regression. However, these approaches often make unnecessarily strong assumptions (e.g., strictly positive components, exclusively negative correlations). An alternative approach uses square-root transformed compositions and directional distributions. Such distributions naturally allow for zero-valued components and po...
• NETWORK INFERENCE VIA APPROXIMATE BAYESIAN COMPUTATION. ILLUSTRATION ON A STOCHASTIC MULTIPOPULATION NEURAL MASS MODEL AI翻译
  作者：Itlevsen, Usanne; Amborrino, Assimiliano; Ubikanec, Rene
  作者单位：University of Copenhagen; University of Warwick; Johannes Kepler University Linz
  摘要：In this article we propose an adapted sequential Monte Carlo approximate Bayesian computation (SMC-ABC) algorithm for network inference in coupled stochastic differential equations (SDEs) used for multivariate time series modeling. Our approach is motivated by neuroscience, specifically the challenge of estimating brain connectivity before and during epileptic seizures. To this end, we make four key contributions. First, we introduce a 6N-dimensional SDE to model the activity of N coupled neur...
• TREE-REGULARIZED BAYESIAN LATENT CLASS ANALYSIS FOR IMPROVING WEAKLY SEPARATED DIETARY PATTERN SUBTYPING IN SMALL-SIZED SUBPOPULATIONS AI翻译
  作者：Li, Mengbing; Stephenson, Briana; Wu, Zhenke
  作者单位：University of Michigan System; University of Michigan; Harvard University; Harvard T.H. Chan School of Public Health
  摘要：Dietary patterns synthesize multiple related diet components, which can be used by nutrition researchers to examine diet-disease relationships. Latent class models (LCMs) have been used to derive dietary patterns from dietary intake assessment, where each class profile represents the probabilities of exposure to a set of diet components. However, LCM-derived dietary patterns can exhibit strong similarities, or weak separation, resulting in numerical and inferential instabilities that challenge...
• HEAPING AND SEEPING, GAITD REGRESSION AND DOUBLY CONSTRAINED REDUCED-RANK VECTOR GENERALIZED LINEAR MODELS IN SMOKING STUDIES AI翻译
  作者：Yee, Thomas W.; Frigau, Luca; Ma, Chenchen
  作者单位：University of Auckland; University of Cagliari; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS
  摘要：Large-scale health surveys suitable for addiction studies furnish self-reported data that consequently suffer from a form of measurement error called heaping, which statisticians have been grappling with for decades. Also known as digit preference, the aberration is often characterized by spikes at multiples of 10 or 5 upon rounding. To date, methods and software for heaped (and seeped) data have been largely wanting. Identifying three generic problems for simple addiction studies, we solve th...