Semiparametric and Nonparametric Methods for Network Data

This dissertation studies two frameworks for incorporating network data into economic modeling.In the first chapter I consider the latent space framework of Holland and Leinhardt (1981) in which the existence of a link between two agents depends on their position in a latent space. I use this framework to estimate the parameters of a linear model in which the regressors and errors covary with the agents latent positions. Neither the endogenous relationship between the regressors and errors nor the distribution of network links are restricted parametrically. Instead, the model is identified by variation in the regressors unexplained by the agents latent positions. I first demonstrate that agents with similar columns of the squared adjacency matrix, the ijth entry of which contains the number of other agents linked to both agents i and j, necessarily have a similar distribution of network links. I then propose a semi parametric estimator based on matching pairs of agents with similar columns of the squared adjacency matrix. I find sufficient conditions for the estimator to be consistent and asymptotically normal, and provide a consistent estimator for its asymptotic variance.In the second chapter I consider the rooted network framework of Aldous and Steele (2004). I use this framework to specify a nonparametric regression of a scalar outcome on a sparse network. The main assumption is that the outcome depends predominately on the configuration of agents and links nearby a distinguished agent. I first establish notion of distance between such configurations and then use it to construct a nearest-neighbor estimator of the regression function.In the third chapter I revisit the latent space setting of the first chapter. I first specify a semi parametric model of link formation in which the existence of a link between a pair of agents depend on their positions in some latent space, an idiosyncratic error, and some linear combination of observed link covariates. I then proposes an estimator for the infinite- dimensional component of the model using a variation on the matching strategy outlined in the first chapter ands characterize the rate of convergence of the estimator using large- deviation arguments

Association Between a Serotonin Transporter Gene Variant and Hopelessness Among Men in the Heart and Soul Study

Hopelessness is associated with mortality in patients with cardiac disease even after accounting for severity of depression. We sought to determine whether a polymorphism in the promoter region of the serotonin transporter gene (5-HTTLPR) is associated with increased hopelessness, and whether this effect is modified by sex, age, antidepressant use or depression in patients with coronary heart disease. We conducted a cross-sectional study of 870 patients with stable coronary heart disease. Our primary outcomes were hopelessness score (range 0-8) and hopeless category (low, moderate and high) as measured by the Everson hopelessness scale. Analysis of covariance and ordinal logistic regression were used to examine the independent association of genotype with hopelessness. Compared to patients with l/l genotype, adjusted odds of a higher hopeless category increased by 35% for the l/s genotype and 80% for s/s genotype (p-value for trend = 0.004). Analysis of covariance demonstrated that the effect of 5-HTTLPR genotype on hopelessness was modified by sex (.04), but not by racial group (p = 0.63). Among men, odds of higher hopeless category increased by 40% for the l/s genotype and by 2.3-fold for s/s genotype (p-value p < 0.001), compared to no effect in the smaller female sample (p = 0.42). Results stratified by race demonstrated a similar dose-response effect of the s allele on hopelessness across racial groups. We found that the 5-HTTLPR is independently associated with hopelessness among men with cardiovascular disease

Expert-augmented machine learning.

Machine learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption is limited by the level of trust afforded by given models. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of humans and machines. Here, we present expert-augmented machine learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We used a large dataset of intensive-care patient data to derive 126 decision rules that predict hospital mortality. Using an online platform, we asked 15 clinicians to assess the relative risk of the subpopulation defined by each rule compared to the total sample. We compared the clinician-assessed risk to the empirical risk and found that, while clinicians agreed with the data in most cases, there were notable exceptions where they overestimated or underestimated the true risk. Studying the rules with greatest disagreement, we identified problems with the training data, including one miscoded variable and one hidden confounder. Filtering the rules based on the extent of disagreement between clinician-assessed risk and empirical risk, we improved performance on out-of-sample data and were able to train with less data. EAML provides a platform for automated creation of problem-specific priors, which help build robust and dependable machine-learning models in critical applications

Supporting the Quadruple Aim Using Simulation and Human Factors During COVID-19 Care

This article is made available for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.The health care sector has made radical changes to hospital operations and care delivery in response to the coronavirus disease (COVID-19) pandemic. This article examines pragmatic applications of simulation and human factors to support the Quadruple Aim of health system performance during the COVID-19 era. First, patient safety is enhanced through development and testing of new technologies, equipment, and protocols using laboratory-based and in situ simulation. Second, population health is strengthened through virtual platforms that deliver telehealth and remote simulation that ensure readiness for personnel to deploy to new clinical units. Third, prevention of lost revenue occurs through usability testing of equipment and computer-based simulations to predict system performance and resilience. Finally, simulation supports health worker wellness and satisfaction by identifying optimal work conditions that maximize productivity while protecting staff through preparedness training. Leveraging simulation and human factors will support a resilient and sustainable response to the pandemic in a transformed health care landscape

ChIP-chip versus ChIP-seq: Lessons for experimental design and data analysis

<p>Abstract</p> <p>Background</p> <p>Chromatin immunoprecipitation (ChIP) followed by microarray hybridization (ChIP-chip) or high-throughput sequencing (ChIP-seq) allows genome-wide discovery of protein-DNA interactions such as transcription factor bindings and histone modifications. Previous reports only compared a small number of profiles, and little has been done to compare histone modification profiles generated by the two technologies or to assess the impact of input DNA libraries in ChIP-seq analysis. Here, we performed a systematic analysis of a modENCODE dataset consisting of 31 pairs of ChIP-chip/ChIP-seq profiles of the coactivator CBP, RNA polymerase II (RNA PolII), and six histone modifications across four developmental stages of <it>Drosophila melanogaster</it>.</p> <p>Results</p> <p>Both technologies produce highly reproducible profiles within each platform, ChIP-seq generally produces profiles with a better signal-to-noise ratio, and allows detection of more peaks and narrower peaks. The set of peaks identified by the two technologies can be significantly different, but the extent to which they differ varies depending on the factor and the analysis algorithm. Importantly, we found that there is a significant variation among multiple sequencing profiles of input DNA libraries and that this variation most likely arises from both differences in experimental condition and sequencing depth. We further show that using an inappropriate input DNA profile can impact the average signal profiles around genomic features and peak calling results, highlighting the importance of having high quality input DNA data for normalization in ChIP-seq analysis.</p> <p>Conclusions</p> <p>Our findings highlight the biases present in each of the platforms, show the variability that can arise from both technology and analysis methods, and emphasize the importance of obtaining high quality and deeply sequenced input DNA libraries for ChIP-seq analysis.</p