cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.1627945.v1
title: Matching and Weighting with Functions of Error-Prone Covariates for Causal Inference

description: Matching estimators are commonly used to estimate causal effects in non-experimental settings. Covariate measurement error can be problematic for matching estimators when observational treatment groups differ on latent quantities observed only through error-prone surrogates. We establish necessary and sufficient conditions for matching and weighting with functions of observed covariates to yield unconfounded causal effect estimators, generalizing results from the standard (i.e. no measurement error) case. We establish that in common covariate measurement error settings, including continuous variables with continuous measurement error, discrete variables with misclassification, and factor and item response theory models, no single function of the observed covariates computed for all units in a study is appropriate for matching. However, we demonstrate that in some circumstances, it is possible to create different functions of the observed covariates for treatment and control units to construct a variable appropriate for matching. We also demonstrate the counterintuitive result that in some settings, it is possible to selectively contaminate the covariates with additional measurement error to construct a variable appropriate for matching. We discuss the implications of our results for the choice between matching and weighting estimators with error-prone covariates. We provide proofs of additional theorems in online supplemental material.
DOI: 10.6084/m9.figshare.1627940
title: Teaching and Learning Data Visualization: Ideas and Assignments

description: This article discusses how to make statistical graphics a more prominent element of the undergraduate statistics curricula. The focus is on several different types of assignments that exemplify how to incorporate graphics into a course in a pedagogically meaningful way. These assignments include having students deconstruct and reconstruct plots, copy masterful graphs, create one-minute visual revelations, convert tables into “pictures,” and develop interactive visualizations, for example, with the virtual earth as a plotting canvas. In addition to describing the goals and details of each assignment, we also discuss the broader topic of graphics and key concepts that we think warrant inclusion in the statistics curricula. We advocate that more attention needs to be paid to this fundamental field of statistics at all levels, from introductory undergraduate through graduate level courses. With the rapid rise of tools to visualize data, for example, Google trends, GapMinder, ManyEyes, and Tableau, and the increased use of graphics in the media, understanding the principles of good statistical graphics, and having the ability to create informative visualizations is an ever more important aspect of statistics education. Supplementary materials containing code and data for the assignments are available online.
DOI: 10.6084/m9.figshare.1627942.v1
title: Sparse Approximate Inference for Spatio-Temporal Point Process Models

description: Spatio-temporal log-Gaussian Cox process models play a central role in the analysis of spatially distributed systems in several disciplines. Yet, scalable inference remains computationally challenging both due to the high-resolution modeling generally required and the analytically intractable likelihood function. Here, we exploit the sparsity structure typical of (spatially) discretized log-Gaussian Cox process models by using approximate message-passing algorithms. The proposed algorithms scale well with the state dimension and the length of the temporal horizon with moderate loss in distributional accuracy. They hence provide a flexible and faster alternative to both nonlinear filtering-smoothing type algorithms and to approaches that implement the Laplace method or expectation propagation on (block) sparse latent Gaussian models. We infer the parameters of the latent Gaussian model using a structured variational Bayes approach. We demonstrate the proposed framework on simulation studies with both Gaussian and point-process observations and use it to reconstruct the conflict intensity and dynamics in Afghanistan from the WikiLeaks Afghan War Diary. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1627945
title: Matching and Weighting With Functions of Error-Prone Covariates for Causal Inference

description: Matching estimators are commonly used to estimate causal effects in nonexperimental settings. Covariate measurement error can be problematic for matching estimators when observational treatment groups differ on latent quantities observed only through error-prone surrogates. We establish necessary and sufficient conditions for matching and weighting with functions of observed covariates to yield unconfounded causal effect estimators, generalizing results from the standard (i.e., no measurement error) case. We establish that in common covariate measurement error settings, including continuous variables with continuous measurement error, discrete variables with misclassification, and factor and item response theory models, no single function of the observed covariates computed for all units in a study is appropriate for matching. However, we demonstrate that in some circumstances, it is possible to create different functions of the observed covariates for treatment and control units to construct a variable appropriate for matching. We also demonstrate the counterintuitive result that in some settings, it is possible to selectively contaminate the covariates with additional measurement error to construct a variable appropriate for matching. We discuss the implications of our results for the choice between matching and weighting estimators with error-prone covariates. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1627945.v3
title: Matching and Weighting With Functions of Error-Prone Covariates for Causal Inference

description: Matching estimators are commonly used to estimate causal effects in nonexperimental settings. Covariate measurement error can be problematic for matching estimators when observational treatment groups differ on latent quantities observed only through error-prone surrogates. We establish necessary and sufficient conditions for matching and weighting with functions of observed covariates to yield unconfounded causal effect estimators, generalizing results from the standard (i.e., no measurement error) case. We establish that in common covariate measurement error settings, including continuous variables with continuous measurement error, discrete variables with misclassification, and factor and item response theory models, no single function of the observed covariates computed for all units in a study is appropriate for matching. However, we demonstrate that in some circumstances, it is possible to create different functions of the observed covariates for treatment and control units to construct a variable appropriate for matching. We also demonstrate the counterintuitive result that in some settings, it is possible to selectively contaminate the covariates with additional measurement error to construct a variable appropriate for matching. We discuss the implications of our results for the choice between matching and weighting estimators with error-prone covariates. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1627945.v2
title: Matching and Weighting With Functions of Error-Prone Covariates for Causal Inference

description: Matching estimators are commonly used to estimate causal effects in nonexperimental settings. Covariate measurement error can be problematic for matching estimators when observational treatment groups differ on latent quantities observed only through error-prone surrogates. We establish necessary and sufficient conditions for matching and weighting with functions of observed covariates to yield unconfounded causal effect estimators, generalizing results from the standard (i.e., no measurement error) case. We establish that in common covariate measurement error settings, including continuous variables with continuous measurement error, discrete variables with misclassification, and factor and item response theory models, no single function of the observed covariates computed for all units in a study is appropriate for matching. However, we demonstrate that in some circumstances, it is possible to create different functions of the observed covariates for treatment and control units to construct a variable appropriate for matching. We also demonstrate the counterintuitive result that in some settings, it is possible to selectively contaminate the covariates with additional measurement error to construct a variable appropriate for matching. We discuss the implications of our results for the choice between matching and weighting estimators with error-prone covariates. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1627938
title: False Discovery Versus Familywise Error Rate Approaches to Outlier Detection

description: Outliers, in general, are observations that deviate sufficiently from a base distribution. This study deals with outlier detection approaches for large samples from continuous univariate distributions. Investigated are the properties of a practical newer outlier detection approach based on use of a false discovery rate (FDR) method in conjunction with a robustly estimated Tukey g-and-h base distribution. Compared are the properties of a boxplot type outlier detection approach that controls the familywise error rate (FWER) with a newer FDR approach. These options are compared in terms of error rates and effects of moving the outliers gradually further from base distribution center while using 5% and 1% FDR or FWERs. Two microarray datasets are used as examples where the assumed null distributions do not fit the data well. In such cases, the proposed estimated Tukey g-and-h null distribution approach leads to superior outlier detection performance. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1627937
title: Discrete Optimization for Interpretable Study Populations and Randomization Inference in an Observational Study of Severe Sepsis Mortality

description: Motivated by an observational study of the effect of hospital ward versus intensive care unit admission on severe sepsis mortality, we develop methods to address two common problems in observational studies: (1) when there is a lack of covariate overlap between the treated and control groups, how to define an interpretable study population wherein inference can be conducted without extrapolating with respect to important variables; and (2) how to use randomization inference to form confidence intervals for the average treatment effect with binary outcomes. Our solution to problem (1) incorporates existing suggestions in the literature while yielding a study population that is easily understood in terms of the covariates themselves, and can be solved using an efficient branch-and-bound algorithm. We address problem (2) by solving a linear integer program to use the worst-case variance of the average treatment effect among values for unobserved potential outcomes that are compatible with the null hypothesis. Our analysis finds no evidence for a difference between the 60-day mortality rates if all individuals were admitted to the ICU and if all patients were admitted to the hospital ward among less severely ill patients and among patients with cryptic septic shock. We implement our methodology in R, providing scripts in the supplementary material.
DOI: 10.6084/m9.figshare.1627937.v1
title: Discrete Optimization for Interpretable Study Populations and Randomization Inference in an Observational Study of Severe Sepsis Mortality

description: Motivated by an observational study of the effect of hospital ward versus intensive care unit admission on severe sepsis mortality, we develop methods to address two common problems in observational studies: (1) when there is a lack of covariate overlap between the treated and control groups, how to define an interpretable study population wherein inference can be conducted without extrapolating with respect to important variables; and (2) how to use randomization inference to form confidence intervals for the average treatment effect with binary outcomes. Our solution to problem (1) incorporates existing suggestions in the literature while yielding a study population that is easily understood in terms of the covariates themselves, and can be solved using an efficient branch-and-bound algorithm. We address problem (2) by solving a linear integer program to use the worst-case variance of the average treatment effect among values for unobserved potential outcomes that are compatible with the null hypothesis. Our analysis finds no evidence for a difference between the 60-day mortality rates if all individuals were admitted to the ICU and if all patients were admitted to the hospital ward among less severely ill patients and among patients with cryptic septic shock. We implement our methodology in R, providing scripts in the supplementary material.
DOI: 10.6084/m9.figshare.1627938.v2
title: False Discovery Versus Familywise Error Rate Approaches to Outlier Detection

description: Outliers, in general, are observations that deviate sufficiently from a base distribution. This study deals with outlier detection approaches for large samples from continuous univariate distributions. Investigated are the properties of a practical newer outlier detection approach based on use of a false discovery rate (FDR) method in conjunction with a robustly estimated Tukey g-and-h base distribution. Compared are the properties of a boxplot type outlier detection approach that controls the familywise error rate (FWER) with a newer FDR approach. These options are compared in terms of error rates and effects of moving the outliers gradually further from base distribution center while using 5% and 1% FDR or FWERs. Two microarray datasets are used as examples where the assumed null distributions do not fit the data well. In such cases, the proposed estimated Tukey g-and-h null distribution approach leads to superior outlier detection performance. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1627939.v1
title: Statistical matching analysis for complex survey data with applications

description: The goal of statistical matching is the estimation of a joint distribution having observed only samples from its marginals. The lack of joint observations on the variables of interest is the reason of uncertainty about the joint population distribution function. In the present paper, the notion of matching error is introduced, and upper-bounded <i>via</i> an appropriate measure of uncertainty. Then, an estimate of the distribution function for the variables not jointly observed is constructed on the basis of a modification of the conditional independence assumption in the presence of logical constraints. The corresponding measure of uncertainty is estimated <i>via</i> sample data. Finally, a simulation study is performed, and an application to a real case is provided.
DOI: 10.6084/m9.figshare.1627933
title: Microsatellite and Mitochondrial Diversity Analysis of Native Pigs of Indo-Burma Biodiversity Hotspot

description: Assessment of genetic diversity in indigenous animals is an important and essential task for animal genetic improvement studies as well as conservation decision-making. The genetic diversity and evolutionary relationships among geographically and phenotypically distinct three pig breeds/types native to Indo-Burma and Eastern Himalayan global biodiversity hotspots were determined by genotyping with a panel of 22 ISAG recommended microsatellite loci as well as sequencing partial <i>MTRNR1</i>gene. The mean number of alleles per locus, effective number of alleles and observed heterozygosity were found to be 11.27 ± 0.85, 5.29 ± 0.34, and 0.795 ± 0.01, respectively. The moderate <i>F</i><sub>ST</sub> value (0.115 ± 0.01) indicated a fair degree of genetic differentiation among the native breeds. The Nei’s unbiased genetic identity estimates indicated less genetic distance (0.2909) between Niang Megha and Tenyi Vo pigs than the both individually with Ghoongroo breed. The divergence time was also estimated from the microsatellite analysis. Analysis of <i>MTRNR1</i>gene revealed distinct clustering of native Indian pigs with Chinese pigs over European pigs. The study revealed the abundance of genetic variation within native Indian pigs and their relationships as well as genetic distances.
DOI: 10.6084/m9.figshare.1627933.v1
title: Microsatellite and Mitochondrial Diversity Analysis of Native Pigs of Indo-Burma Biodiversity Hotspot

description: Assessment of genetic diversity in indigenous animals is an important and essential task for animal genetic improvement studies as well as conservation decision-making. The genetic diversity and evolutionary relationships among geographically and phenotypically distinct three pig breeds/types native to Indo-Burma and Eastern Himalayan global biodiversity hotspots were determined by genotyping with a panel of 22 ISAG recommended microsatellite loci as well as sequencing partial <i>MTRNR1</i>gene. The mean number of alleles per locus, effective number of alleles and observed heterozygosity were found to be 11.27 ± 0.85, 5.29 ± 0.34, and 0.795 ± 0.01, respectively. The moderate <i>F</i><sub>ST</sub> value (0.115 ± 0.01) indicated a fair degree of genetic differentiation among the native breeds. The Nei’s unbiased genetic identity estimates indicated less genetic distance (0.2909) between Niang Megha and Tenyi Vo pigs than the both individually with Ghoongroo breed. The divergence time was also estimated from the microsatellite analysis. Analysis of <i>MTRNR1</i>gene revealed distinct clustering of native Indian pigs with Chinese pigs over European pigs. The study revealed the abundance of genetic variation within native Indian pigs and their relationships as well as genetic distances.
DOI: 10.6084/m9.figshare.1627939.v3
title: Statistical Matching Analysis for Complex Survey Data With Applications

description: The goal of statistical matching is the estimation of a joint distribution having observed only samples from its marginals. The lack of joint observations on the variables of interest is the reason of uncertainty about the joint population distribution function. In the present article, the notion of matching error is introduced, and upper-bounded via an appropriate measure of uncertainty. Then, an estimate of the distribution function for the variables not jointly observed is constructed on the basis of a modification of the conditional independence assumption in the presence of logical constraints. The corresponding measure of uncertainty is estimated via sample data. Finally, a simulation study is performed, and an application to a real case is provided. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1627934
title: ARTIK-52 induces replication-dependent DNA damage and p53 activation exclusively in cells of prostate and breast cancer origin

description: The realization, that the androgen receptor (AR) is essential for prostate cancer (PC) even after relapse following androgen deprivation therapy motivated the search for novel types of AR inhibitors. We proposed that targeting AR expression versus its function would work in cells having either wild type or mutant AR as well as be independent of androgen synthesis pathways. Previously, using a phenotypic screen in androgen-independent PC cells we identified a small molecule inhibitor of AR, ARTIK-52. Treatment with ARTIK-52 caused the loss of AR protein and death of AR-positive, but not AR-negative, PC cells. Here we present data that ARTIK-52 induces degradation of AR mRNA through a mechanism that we were unable to establish. However, we found that ARTIK-52 is toxic to breast cancer (BC) cells expressing AR, although they were not sensitive to AR knockdown, suggesting an AR-independent mechanism of toxicity. Using different approaches we detected that ARTIK-52 induces replication-dependent double strand DNA breaks exclusively in cancer cells of prostate and breast origin, while not causing DNA damage, or any toxicity, in normal cells, as well as in non-PC and non-BC tumor cells, independent of their proliferation status. This amazing specificity, combined with such a basic mechanism of toxicity, makes ARTIK-52 a potentially useful tool to discover novel attractive targets for the treatment of BC and PC. Thus, phenotypic screening allowed us to identify a compound, whose properties cannot be predicted based on existing knowledge and moreover, uncover a barely known link between AR and DNA damage response in PC and BC epithelial cells.
DOI: 10.6084/m9.figshare.1627939.v2
title: Statistical Matching Analysis for Complex Survey Data With Applications

description: The goal of statistical matching is the estimation of a joint distribution having observed only samples from its marginals. The lack of joint observations on the variables of interest is the reason of uncertainty about the joint population distribution function. In the present article, the notion of matching error is introduced, and upper-bounded via an appropriate measure of uncertainty. Then, an estimate of the distribution function for the variables not jointly observed is constructed on the basis of a modification of the conditional independence assumption in the presence of logical constraints. The corresponding measure of uncertainty is estimated via sample data. Finally, a simulation study is performed, and an application to a real case is provided. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1627934.v1
title: ARTIK-52 induces replication-dependent DNA damage and p53 activation exclusively in cells of prostate and breast cancer origin

description: The realization, that the androgen receptor (AR) is essential for prostate cancer (PC) even after relapse following androgen deprivation therapy motivated the search for novel types of AR inhibitors. We proposed that targeting AR expression versus its function would work in cells having either wild type or mutant AR as well as be independent of androgen synthesis pathways. Previously, using a phenotypic screen in androgen-independent PC cells we identified a small molecule inhibitor of AR, ARTIK-52. Treatment with ARTIK-52 caused the loss of AR protein and death of AR-positive, but not AR-negative, PC cells. Here we present data that ARTIK-52 induces degradation of AR mRNA through a mechanism that we were unable to establish. However, we found that ARTIK-52 is toxic to breast cancer (BC) cells expressing AR, although they were not sensitive to AR knockdown, suggesting an AR-independent mechanism of toxicity. Using different approaches we detected that ARTIK-52 induces replication-dependent double strand DNA breaks exclusively in cancer cells of prostate and breast origin, while not causing DNA damage, or any toxicity, in normal cells, as well as in non-PC and non-BC tumor cells, independent of their proliferation status. This amazing specificity, combined with such a basic mechanism of toxicity, makes ARTIK-52 a potentially useful tool to discover novel attractive targets for the treatment of BC and PC. Thus, phenotypic screening allowed us to identify a compound, whose properties cannot be predicted based on existing knowledge and moreover, uncover a barely known link between AR and DNA damage response in PC and BC epithelial cells.