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作者：Sun, Yifei; He, Xuming; Hu, Jianhua
作者单位：Columbia University; University of Michigan System; University of Michigan
摘要：Late-stage clinical trials have been conducted primarily to establish the efficacy of a new treatment in an intended population. A corollary of population heterogeneity in clinical trials is that a treatment might be effective for one or more subgroups, rather than for the whole population of interest. As an example, the phase III clinical trial of panitumumab in metastatic colorectal cancer patients failed to demonstrate its efficacy in the overall population, but a subgroup associated with t...
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作者：Bargagli-Stoffi, Falco J.; de Witte, Kristof; Gnecco, Giorgio
作者单位：Harvard University; Harvard T.H. Chan School of Public Health; KU Leuven; IMT School for Advanced Studies Lucca
摘要：This paper introduces an innovative Bayesian machine learning algorithm to draw interpretable inference on heterogeneous causal effects in the presence of imperfect compliance (e.g., under an irregular assignment mechanism). We show, through Monte Carlo simulations, that the proposed Bayesian Causal Forest with Instrumental Variable (BCF-IV) methodology outperforms other machine learning techniques tailored for causal inference in discovering and estimating the heterogeneous causal effects whi...
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作者：Chakraborty, Antik; Ovaskainen, Otso; Dunson, David B.
作者单位：Purdue University System; Purdue University; Duke University; University of Jyvaskyla; University of Helsinki; Norwegian University of Science & Technology (NTNU)
摘要：We introduce a new class of semiparametric latent variable models for long memory discretized event data. The proposed methodology is motivated by a study of bird vocalizations in the Amazon rain forest; the timings of vocalizations exhibit self-similarity and long range dependence. This rules out Poisson process based models where the rate function itself is not long range dependent. The proposed class of FRActional Probit (FRAP) models is based on thresholding, a latent process. This latent ...
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作者：Fisher, Thomas J.; Zhang, Jing; Colegate, Stephen P.; Vanni, Michael J.
作者单位：University System of Ohio; Miami University; University System of Ohio; University of Cincinnati; University System of Ohio; Miami University
摘要：We propose a framework to detect and model shifts in a time series of continuous proportions, that is, a vector of proportions measuring the parts of a whole. By reparameterizing the shape of a Dirichlet distribution, we can model the location and scale separately through generalized linear models. A hidden Markov model allows the coefficients of the generalized linear models to change, thus allowing for the time series to undergo multiple regimes. This framework allows a practitioner to adequ...
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作者：Casa, Alessandro; O'callaghan, Tom f.; Murphy, Thomas brendan
作者单位：University College Dublin; University College Cork
摘要：In recent years, within the dairy sector, animal diet and management practices have been receiving increased attention, in particular, examining the impact of pasture-based feeding strategies on the composition and quality of milk and dairy products in line with the prevalence of premium grass-fed dairy products appearing on market shelves. To date, methods to thoroughly investigate the more relevant differences induced by the diet on milk chemi-cal features are limited; enhanced statistical t...
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作者：Wu, Qiuyu; Luo, Xiangyu
作者单位：Renmin University of China
摘要：Inferring gene regulatory networks can elucidate how genes work coop-eratively. The gene-gene collaboration information is often learned by Gaus-sian graphical models (GGM) that aim to identify whether the expression levels of any pair of genes are dependent, given other genes' expression values. One basic assumption that guarantees the validity of GGM is data normality, and this often holds for bulk-level expression data which aggregate biological signals from a collection of cells. However, ...
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作者：Dirmeier, Simon; Beerenwinkel, Niko
作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich
摘要：Genetic perturbation screening is an experimental method in biology to study cause and effect relationships between different biological entities. However, knocking out or knocking down genes is a highly error-prone process that complicates estimation of the effect sizes of the interventions. Here, we introduce a family of generative models, called the structured hierarchical model (SHM) for probabilistic inference of causal effects from perturbation screens. SHMs utilize classical hierarchica...
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作者：Guo, Xiaoyang; Bal, Aditi Basu; Needham, Tom; Srivastava, Anuj
作者单位：State University System of Florida; Florida State University; State University System of Florida; Florida State University
摘要：The arterial networks in the human brain, termed brain arterial networks or BANs, are complex arrangements of individual arteries, branching patterns, and interconnectivity. BANs play an essential role in characterizing and understanding brain physiology, and one would like tools for statistically analyzing the shapes of BANs. These tools include quantifying shape differences, comparing populations of subjects, and studying the effects of covariates on these shapes. This paper mathematically r...
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作者：Rafei, Ali; Flannagan, Carol A. C.; West, Brady T.; Elliott, Michael R.
作者单位：University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
摘要：Big Data often presents as massive nonprobability samples. Not only is the selection mechanism often unknown but larger data volume amplifies the relative contribution of selection bias to total error. Existing bias adjustment approaches assume that the conditional mean structures have been correctly specified for the selection indicator or key substantive measures. In the presence of a reference probability sample, these methods rely on a pseudolike-lihood method to account for the sampling w...
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作者：Larsen, Alexandra; Yang, Shu; Reich, Brian J.; Rappold, Ana G.
作者单位：Duke University; North Carolina State University; United States Environmental Protection Agency
摘要：Wildland fire smoke contains hazardous levels of fine particulate mat-ter (PM2.5), a pollutant shown to adversely effect health. Estimating fire at-tributable PM2.5 concentrations is key to quantifying the impact on air quality and subsequent health burden. This is a challenging problem since only to-tal PM2.5 is measured at monitoring stations and both fire-attributable PM2.5 and PM2.5 from all other sources are correlated in space and time. We propose a framework for estimating fire-contribu...
