Non-Parametric Causal Discovery for Discrete and Continuous Data

Subject-matter experts typically think of their datasets as causes and effects between many variables, forming a large, complex causal system. Directed acyclic graphs (DAG), also called Bayesian networks, provide a natural way to conceptualize these systems. In contrast, regression modeling can provide strong evidence for the local, causal neighborhood of an outcome within the causal system, butproviding structure for the larger system is challenging with regression. Despite its value as exploratory data analysis or in conjunction with regression models to refine causal understanding, methods for estimating the causal structure underlying a dataset, causal discovery, are rare in fields such as epidemiology, possibly due to the difficulty handling data with continuous and discrete random variables.This thesis focuses on developing a causal discovery method for researchers whose data typically are comprised of both discrete and continuous variables. Its primarycontribution is the development of an estimator for graph divergence, the Kullback-Leibler divergence between the full, joint distribution and the Bayesian factorizationindicated by a DAG. Graph divergence is a generalization of conditional mutual information: it quantifies the ?t of a DAG to the data, with greater divergence indicating worse ?t and a divergence of zero indicating a perfect characterization ofthe conditional independence relationships among the variables. Its nearest neighbor approach gives the estimator the capability to handle mixed data. We show that the estimator is consistent and its convergence separately for the continuous and discrete case under some assumptions.Last, we demonstrate a way to use graph divergence with a greedy Markov equivalence search algorithm in practice. Though this work is not complete, we estimate causal relationships between personal demographics, sexual risk behaviors, and HIV Pre-exposure prophylaxis among men who have sex with men (MSM) on the American Men's Internet Survey data. This work may be able to inform publichealth initiatives and guidelines surrounding sexual health of MSM.</div

Using graph learning to understand adverse pregnancy outcomes and stress pathways.

To identify pathways between stress indicators and adverse pregnancy outcomes, we applied a nonparametric graph-learning algorithm, PC-KCI, to data from an observational prospective cohort study. The Measurement of Maternal Stress study (MOMS) followed 744 women with a singleton intrauterine pregnancy recruited between June 2013 and May 2015. Infant adverse pregnancy outcomes were prematurity (<37 weeks' gestation), infant days spent in hospital after birth, and being small for gestational age (percentile gestational weight at birth). Maternal adverse pregnancy outcomes were pre-eclampsia, gestational diabetes, and gestational hypertension. PC-KCI replicated well-established pathways, such as the relationship between gestational weeks and preterm premature rupture of membranes. PC-KCI also identified previously unobserved pathways to adverse pregnancy outcomes, including 1) a link between hair cortisol levels (at 12-21 weeks of pregnancy) and pre-eclampsia; 2) two pathways to preterm birth depending on race, with one linking Hispanic race, pre-gestational diabetes and gestational weeks, and a second pathway linking black race, hair cortisol, preeclampsia, and gestational weeks; and 3) a relationship between maternal childhood trauma, perceived social stress in adulthood, and low weight for gestational age. Our approach confirmed previous findings and identified previously unobserved pathways to adverse pregnancy outcomes. It presents a method for a global assessment of a clinical problem for further study of possible causal pathways

The Association between Sexually Transmitted Infections, Length of Service and Other Demographic Factors in the U.S. Military.

Numerous studies have found higher rates of sexually transmitted infections (STIs) among military personnel than the general population, but the cumulative risk of acquiring STIs throughout an individual's military career has not been described.Using ICD-9 diagnosis codes, we analyzed the medical records of 100,005 individuals from all service branches, divided in equal cohorts (n = 6,667) between 1997 and 2011. As women receive frequent STI screening compared to men, these groups were analyzed separately. Incidence rates were calculated for pathogen-specific STIs along with syndromic diagnoses. Descriptive statistics were used to characterize the individuals within each accession year cohort; repeat infections were censored.The total sample included 29,010 females and 70,995 males. The STI incidence rates (per 100 person-years) for women and men, respectively, were as follows: chlamydia (3.5 and 0.7), gonorrhea (1.1 and 0.4), HIV (0.04 and 0.07) and syphilis (0.14 and 0.15). During the study period, 22% of women and 3.3% of men received a pathogen-specific STI diagnosis; inclusion of syndromic diagnoses increased STI prevalence to 41% and 5.5%, respectively. In multivariate analyses, factors associated with etiologic and syndromic STIs among women included African American race, younger age and fewer years of education. In the overall sample, increasing number of years of service was associated with an increased likelihood of an STI diagnosis (p<0.001 for trend).In this survey of military personnel, we found very high rates of STI acquisition throughout military service, especially among women, demonstrating that STI-related risk is significant and ongoing throughout military service. Lower STI incidence rates among men may represent under-diagnosis and demonstrate a need for enhancing male-directed screening and diagnostic interventions

Kaplan-Meier plots demonstrating the time to AIDS or death for HIV controllers (HIC) compared to non-controllers by hepatitis B virus (HBV) vaccine antibody response status (A and B) and outcomes for non-controllers alone (C).

<p>Among HBV vaccine responders (A) and non-responders (B), the time to AIDS or death was significantly longer for HIC compared to non-controllers (Log Rank P<0.001 for both). (C). Examination of non-controllers alone by HBV response status showed a longer time to AIDS or death for vaccine responders compared to non-responders (P<0.001). Anti-HBs, antibody to HBV surface antigen.</p

Baseline Characteristics of HIV Controllers and Non-controllers With or Without Viral Load-Suppressive HAART for HBV Vaccine Seroresponse.

<p>HAART, highly active antiretroviral therapy; HBV, hepatitis B virus.</p>a<p>P-value, HIV controllers vs. HAART-naïve non-controllers.</p>b<p>P-value HIV controllers vs. non-controllers on HAART.</p><p>AIDS, acquired immunodeficiency syndrome; VL, viral load; HCV, hepatitis C virus; anti-HBs, antibody to hepatitis B surface antigen.</p><p>Data are number (%) or median (Interquartile Range).</p><p>Positive HBV vaccine seroresponse defined as anti-HBs ≥10 IU/L measured ≥1 month after last HBV vaccination.</p

Factors Associated with Positive HBV Vaccine Seroresponse for HIV Controllers Compared to Non-controllers With or Without Viral Load-Suppressive HAART.

<p>HBV, hepatitis B virus; HAART, highly active antiretroviral therapy; OR, odds ratio; CI, confidence interval; AIDS, acquired immunodeficiency syndrome.</p><p>Data are number (%) or median (Interquartile Range).</p><p>Positive HBV vaccine seroresponse defined as antibody to HBV surface antigen ≥10 IU/L measured ≥1 month after last HBV vaccination.</p

Baseline Characteristics for HIV Controllers and Non-controllers for Time to AIDS or Death Analysis.

<p>VL, viral load; HBV, hepatitis B virus; AIDS, acquired immunodeficiency syndrome; HAART, highly active antiretroviral therapy.</p><p>Values are number (%) or median (Interquartile Range).</p><p>Positive HBV vaccine seroresponse defined as antibody to HBV surface antigen (anti-HBs) ≥10 IU/L following last HBV vaccination. For those vaccinated before HIV, the first available anti-HBs determination after HIV infection was used for categorization.</p

Cox proportional hazard model for factors associated with risk of AIDS or death event.

<p>HR, hazard ratio; CI, confidence interval; HBV, hepatitis B virus; AIDS, acquired immunodeficiency syndrome.</p><p>Model stratified by era of HIV diagnosis (before or after 1996).</p