cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.6220343
title: Mixtures of <i><i>g</i></i>-Priors in Generalized Linear Models

description: Mixtures of Zellner’s <i>g</i>-priors have been studied extensively in linear models and have been shown to have numerous desirable properties for Bayesian variable selection and model averaging. Several extensions of <i>g</i>-priors to generalized linear models (GLMs) have been proposed in the literature; however, the choice of prior distribution of <i>g</i> and resulting properties for inference have received considerably less attention. In this article, we unify mixtures of <i>g</i>-priors in GLMs by assigning the truncated Compound Confluent Hypergeometric (tCCH) distribution to 1/(1 + <i>g</i>), which encompasses as special cases several mixtures of <i>g</i>-priors in the literature, such as the hyper-<i>g</i>, Beta-prime, truncated Gamma, incomplete inverse-Gamma, benchmark, robust, hyper-<i>g</i>/<i>n</i>, and intrinsic priors. Through an integrated Laplace approximation, the posterior distribution of 1/(1 + <i>g</i>) is in turn a tCCH distribution, and approximate marginal likelihoods are thus available analytically, leading to “Compound Hypergeometric Information Criteria” for model selection. We discuss the local geometric properties of the <i>g</i>-prior in GLMs and show how the desiderata for model selection proposed by Bayarri et al., such as asymptotic model selection consistency, intrinsic consistency, and measurement invariance may be used to justify the prior and specific choices of the hyper parameters. We illustrate inference using these priors and contrast them to other approaches via simulation and real data examples. The methodology is implemented in the R package BAS and freely available on CRAN. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.6220343.v2
title: Mixtures of <i><i>g</i></i>-Priors in Generalized Linear Models

description: Mixtures of Zellner’s <i>g</i>-priors have been studied extensively in linear models and have been shown to have numerous desirable properties for Bayesian variable selection and model averaging. Several extensions of <i>g</i>-priors to generalized linear models (GLMs) have been proposed in the literature; however, the choice of prior distribution of <i>g</i> and resulting properties for inference have received considerably less attention. In this article, we unify mixtures of <i>g</i>-priors in GLMs by assigning the truncated Compound Confluent Hypergeometric (tCCH) distribution to 1/(1 + <i>g</i>), which encompasses as special cases several mixtures of <i>g</i>-priors in the literature, such as the hyper-<i>g</i>, Beta-prime, truncated Gamma, incomplete inverse-Gamma, benchmark, robust, hyper-<i>g</i>/<i>n</i>, and intrinsic priors. Through an integrated Laplace approximation, the posterior distribution of 1/(1 + <i>g</i>) is in turn a tCCH distribution, and approximate marginal likelihoods are thus available analytically, leading to “Compound Hypergeometric Information Criteria” for model selection. We discuss the local geometric properties of the <i>g</i>-prior in GLMs and show how the desiderata for model selection proposed by Bayarri et al., such as asymptotic model selection consistency, intrinsic consistency, and measurement invariance may be used to justify the prior and specific choices of the hyper parameters. We illustrate inference using these priors and contrast them to other approaches via simulation and real data examples. The methodology is implemented in the R package BAS and freely available on CRAN. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.6220343.v1
title: Mixtures of <i>g</i>-Priors in Generalized Linear Models

description: Mixtures of Zellner's <i>g</i>-priors have been studied extensively in linear models and have been shown to have numerous desirable properties for Bayesian variable selection and model averaging. Several extensions of <i>g</i>-priors to Generalized Linear Models (GLMs) have been proposed in the literature; however, the choice of prior distribution of <i>g</i> and resulting properties for inference have received considerably less attention. In this paper, we unify mixtures of <i>g</i>-priors in GLMs by assigning the truncated Compound Confluent Hypergeometric (tCCH) distribution to 1/(1 + <i>g</i>), which encompasses as special cases several mixtures of <i>g</i>-priors in the literature, such as the hyper-<i>g</i>, Beta-prime, truncated Gamma, incomplete inverse-Gamma, benchmark, robust, hyper-<i>g</i>/<i>n</i>, and intrinsic priors. Through an integrated Laplace approximation, the posterior distribution of 1/(1 + <i>g</i>) is in turn a tCCH distribution, and approximate marginal likelihoods are thus available analytically, leading to “Compound Hypergeometric Information Criteria” for model selection. We discuss the local geometric properties of the <i>g</i>-prior in GLMs and show how the desiderata for model selection proposed by Bayarri et al, such as asymptotic model selection consistency, intrinsic consistency, and measurement invariance may be used to justify the prior and specific choices of the hyper parameters. We illustrate inference using these priors and contrast them to other approaches via simulation and real data examples. An R package on CRAN is available to implement the methodology. The methodology is implemented in the R package BAS and freely available on CRAN.
DOI: 10.6084/m9.figshare.6220325.v1
title: Robust Bayesian inference via coarsening

description: The standard approach to Bayesian inference is based on the assumption that the distribution of the data belongs to the chosen model class. However, even a small violation of this assumption can have a large impact on the outcome of a Bayesian procedure. We introduce a novel approach to Bayesian inference that improves robustness to small departures from the model: rather than conditioning on the event that the observed data are generated by the model, one conditions on the event that the model generates data close to the observed data, in a distributional sense. When closeness is defined in terms of relative entropy, the resulting “coarsened” posterior can be approximated by simply tempering the likelihood—that is, by raising the likelihood to a fractional power—thus, inference can usually be implemented via standard algorithms, and one can even obtain analytical solutions when using conjugate priors. Some theoretical properties are derived, and we illustrate the approach with real and simulated data using mixture models and autoregressive models of unknown order.
DOI: 10.6084/m9.figshare.6220325.v2
title: Robust Bayesian Inference via Coarsening

description: The standard approach to Bayesian inference is based on the assumption that the distribution of the data belongs to the chosen model class. However, even a small violation of this assumption can have a large impact on the outcome of a Bayesian procedure. We introduce a novel approach to Bayesian inference that improves robustness to small departures from the model: rather than conditioning on the event that the observed data are generated by the model, one conditions on the event that the model generates data close to the observed data, in a distributional sense. When closeness is defined in terms of relative entropy, the resulting “coarsened” posterior can be approximated by simply tempering the likelihood—that is, by raising the likelihood to a fractional power—thus, inference can usually be implemented via standard algorithms, and one can even obtain analytical solutions when using conjugate priors. Some theoretical properties are derived, and we illustrate the approach with real and simulated data using mixture models and autoregressive models of unknown order. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.6220325.v3
title: Robust Bayesian Inference via Coarsening

description: The standard approach to Bayesian inference is based on the assumption that the distribution of the data belongs to the chosen model class. However, even a small violation of this assumption can have a large impact on the outcome of a Bayesian procedure. We introduce a novel approach to Bayesian inference that improves robustness to small departures from the model: rather than conditioning on the event that the observed data are generated by the model, one conditions on the event that the model generates data close to the observed data, in a distributional sense. When closeness is defined in terms of relative entropy, the resulting “coarsened” posterior can be approximated by simply tempering the likelihood—that is, by raising the likelihood to a fractional power—thus, inference can usually be implemented via standard algorithms, and one can even obtain analytical solutions when using conjugate priors. Some theoretical properties are derived, and we illustrate the approach with real and simulated data using mixture models and autoregressive models of unknown order. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.6220322
title: Capture-Recapture Methods for Data on the Activation of Applications on Mobile Phones

description: This work is concerned with the analysis of marketing data on the activation of applications (apps) on mobile devices. Each application has a hashed identification number that is specific to the device on which it has been installed. This number can be registered by a platform at each activation of the application. Activations on the same device are linked together using the identification number. By focusing on activations that took place at a business location, one can create a capture-recapture dataset about devices, that is, users, that “visited” the business: the units are owners of mobile devices and the capture occasions are time intervals such as days. A unit is captured when she activates an application, provided that this activation is recorded by the platform providing the data. Statistical capture-recapture techniques can be applied to the app data to estimate the total number of users that visited the business over a time period, thereby providing an indirect estimate of foot traffic. This article argues that the robust design, a method for dealing with a nested mark-recapture experiment, can be used in this context. A new algorithm for estimating the parameters of a robust design with a fairly large number of capture occasions and a simple parametric bootstrap variance estimator are proposed. Moreover, new estimation methods and new theoretical results are introduced for a wider application of the robust design. This is used to analyze a dataset about the mobile devices that visited the auto-dealerships of a major auto brand in a U.S. metropolitan area over a period of 1 year and a half. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.6220322.v1
title: Capture-Recapture Methods for Data on the Activation of Applications on Mobile Phones

description: This work is concerned with the analysis of marketing data on the activation of applications (apps) on mobile devices. Each application has a hashed identification number that is specific to the device on which it has been installed. This number can be registered by a platform at each activation of the application. Activations on the same device are linked together using the identification number. By focusing on activations that took place at a business location, one can create a capture-recapture dataset about devices, that is, users, that “visited” the business: the units are owners of mobile devices and the capture occasions are time intervals such as days. A unit is captured when she activates an application, provided that this activation is recorded by the platform providing the data. Statistical capture-recapture techniques can be applied to the app data to estimate the total number of users that visited the business over a time period, thereby providing an indirect estimate of foot traffic. This article argues that the robust design, a method for dealing with a nested mark-recapture experiment, can be used in this context. A new algorithm for estimating the parameters of a robust design with a fairly large number of capture occasions and a simple parametric bootstrap variance estimator are proposed. Moreover, new estimation methods and new theoretical results are introduced for a wider application of the robust design. This is used to analyze a dataset about the mobile devices that visited the auto-dealerships of a major auto brand in a U.S. metropolitan area over a period of 1 year and a half. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.6220319
title: Estimating the Malaria Attributable Fever Fraction Accounting for Parasites Being Killed by Fever and Measurement Error

description: Malaria is a major health problem in many tropical regions. Fever is a characteristic symptom of malaria. The fraction of fevers that are attributable to malaria, the malaria attributable fever fraction (MAFF), is an important public health measure in that the MAFF can be used to calculate the number of fevers that would be avoided if malaria was eliminated. Despite such causal interpretation, the MAFF has not been considered in the framework of causal inference. We define the MAFF using the potential outcome framework, and define causal assumptions that current estimation methods rely on. Furthermore, we demonstrate that one of the assumptions—that the parasite density is correctly measured—generally does not hold because (i) fever kills some parasites and (ii) parasite density is measured with error. In the presence of these problems, we reveal that current MAFF estimators can be significantly biased. To develop a consistent estimator, we propose a novel maximum likelihood estimation method based on exponential family g-modeling. Under the assumption that the measurement error mechanism and the magnitude of the fever killing effect are known, we show that our proposed method provides approximately unbiased estimates of the MAFF in simulation studies. A sensitivity analysis is developed to assess the impact of different magnitudes of fever killing and different measurement error mechanisms. Finally, we apply our proposed method to estimate the MAFF in Kilombero, Tanzania. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.6220319.v1
title: Estimating the Malaria Attributable Fever Fraction Accounting for Parasites Being Killed by Fever and Measurement Error

description: Malaria is a major health problem in many tropical regions. Fever is a characteristic symptom of malaria. The fraction of fevers that are attributable to malaria, the malaria attributable fever fraction (MAFF), is an important public health measure in that the MAFF can be used to calculate the number of fevers that would be avoided if malaria was eliminated. Despite such causal interpretation, the MAFF has not been considered in the framework of causal inference. We define the MAFF using the potential outcome framework, and define causal assumptions that current estimation methods rely on. Furthermore, we demonstrate that one of the assumptions—that the parasite density is correctly measured—generally does not hold because (i) fever kills some parasites and (ii) parasite density is measured with error. In the presence of these problems, we reveal that current MAFF estimators can be significantly biased. To develop a consistent estimator, we propose a novel maximum likelihood estimation method based on exponential family g-modeling. Under the assumption that the measurement error mechanism and the magnitude of the fever killing effect are known, we show that our proposed method provides approximately unbiased estimates of the MAFF in simulation studies. A sensitivity analysis is developed to assess the impact of different magnitudes of fever killing and different measurement error mechanisms. Finally, we apply our proposed method to estimate the MAFF in Kilombero, Tanzania. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.6220310
title: Survivor-Complier Effects in the Presence of Selection on Treatment, With Application to a Study of Prompt ICU Admission

description: Pretreatment selection or censoring (“selection on treatment”) can occur when two treatment levels are compared ignoring the third option of neither treatment, in “censoring by death” settings where treatment is only defined for those who survive long enough to receive it, or in general in studies where the treatment is only defined for a subset of the population. Unfortunately, the standard instrumental variable (IV) estimand is not defined in the presence of such selection, so we consider estimating a new survivor-complier causal effect. Although this effect is generally not identified under standard IV assumptions, it is possible to construct sharp bounds. We derive these bounds and give a corresponding data-driven sensitivity analysis, along with nonparametric yet efficient estimation methods. Importantly, our approach allows for high-dimensional confounding adjustment, and valid inference even after employing machine learning. Incorporating covariates can tighten bounds dramatically, especially when they are strong predictors of the selection process. We apply the methods in a UK cohort study of critical care patients to examine the mortality effects of prompt admission to the intensive care unit, using ICU bed availability as an instrument. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.6220310.v2
title: Survivor-Complier Effects in the Presence of Selection on Treatment, With Application to a Study of Prompt ICU Admission

description: Pretreatment selection or censoring (“selection on treatment”) can occur when two treatment levels are compared ignoring the third option of neither treatment, in “censoring by death” settings where treatment is only defined for those who survive long enough to receive it, or in general in studies where the treatment is only defined for a subset of the population. Unfortunately, the standard instrumental variable (IV) estimand is not defined in the presence of such selection, so we consider estimating a new survivor-complier causal effect. Although this effect is generally not identified under standard IV assumptions, it is possible to construct sharp bounds. We derive these bounds and give a corresponding data-driven sensitivity analysis, along with nonparametric yet efficient estimation methods. Importantly, our approach allows for high-dimensional confounding adjustment, and valid inference even after employing machine learning. Incorporating covariates can tighten bounds dramatically, especially when they are strong predictors of the selection process. We apply the methods in a UK cohort study of critical care patients to examine the mortality effects of prompt admission to the intensive care unit, using ICU bed availability as an instrument. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.6220310.v1
title: Survivor-complier effects in the presence of selection on treatment, with application to a study of prompt ICU admission

description: Pre-treatment selection or censoring (‘selection on treatment’) can occur when two treatment levels are compared ignoring the third option of neither treatment, in ‘censoring by death’ settings where treatment is only defined for those who survive long enough to receive it, or in general in studies where the treatment is only defined for a subset of the population. Unfortunately, the standard instrumental variable (IV) estimand is not defined in the presence of such selection, so we consider estimating a new survivor-complier causal effect. Although this effect is generally not identified under standard IV assumptions, it is possible to construct sharp bounds. We derive these bounds and give a corresponding data-driven sensitivity analysis, along with nonparametric yet efficient estimation methods. Importantly, our approach allows for high-dimensional confounding adjustment, and valid inference even after employing machine learning. Incorporating covariates can tighten bounds dramatically, especially when they are strong predictors of the selection process. We apply the methods in a UK cohort study of critical care patients to examine the mortality effects of prompt admission to the intensive care unit, using ICU bed availability as an instrument.
DOI: 10.6084/m9.figshare.6220241
title: SUDDEN CARDIAC ARREST DURING SPINAL ANESTHESIA

description: Case Report and a review of the Literature on Sudden cardiac Arrest during anesthesia
DOI: 10.6084/m9.figshare.6220217
title: Activation of Class I histone deacetylases contributes to mitochondrial dysfunction in cardiomyocytes with altered complex activities

description: Histone deacetylases (HDACs) play vital roles in the pathophysiology of heart failure, which is associated with mitochondrial dysfunction. Tumor necrosis factor-α (TNF-α) contributes to the genesis of heart failure and impairs mitochondria. This study evaluated the role of HDACs in TNF-α-induced mitochondrial dysfunction and investigated their therapeutic potential and underlying mechanisms. We measured mitochondrial oxygen consumption rate (OCR) and ATP production using Seahorse XF24 extracellular flux analyzer and bioluminescent assay in control and TNF-α (10 ng/ml, 24 h)-treated HL-1 cells with or without HDAC inhibition. TNF-α increased Class I and II (but not Class IIa) HDAC activities (assessed by Luminescent) with enhanced expressions of Class I (HDAC1, HDAC2, HDAC3, and HDAC8) but not Class IIb HDAC (HDAC6 and HDAC10) proteins in HL-1 cells. TNF-α induced mitochondrial dysfunction with impaired basal, ATP-linked, and maximal respiration, decreased cellular ATP synthesis, and increased mitochondrial superoxide production (measured by MitoSOX red fluorescence), which were rescued by inhibiting HDACs with MPT0E014 (1 μM, a Class I and IIb inhibitor), or MS-275 (1 μM, a Class I inhibitor). MPT0E014 reduced TNF-α-decreased complex I and II enzyme (but not III or IV) activities (by enzyme activity microplate assays). Our results suggest that Class I HDAC actions contribute to TNF-α-induced mitochondrial dysfunction in cardiomyocytes with altered complex I and II enzyme regulation. HDAC inhibition improves dysfunctional mitochondrial bioenergetics with attenuation of TNF-α-induced oxidative stress, suggesting the therapeutic potential of HDAC inhibition in cardiac dysfunction.