Controlling for Confounding when Association is Quantified by Area Under the ROC Curve

In the medical literature, there has been an increased interest in evaluating association between exposure and outcomes using nonrandomized observational studies. However, because assignments to exposure are not done randomly in observational studies, comparisons of outcomes between exposed and non-exposed subjects must account for the effect of confounders. Propensity score methods have been widely used to control for confounding, when estimating exposure effect. Previous studies have shown that conditioning on the propensity score results in biased estimation of odds ratio and hazard ratio. However, there is a lack of research into the performance of propensity score methods for estimating the area under the ROC curve (AUC). In this dissertation, we propose AUC as measure of effect when outcomes are continuous. The AUC is interpreted as the probability that a randomly selected non-exposed subject has a better response than a randomly selected exposed subject. The aim of this research is to examine methods to control for confounding when association between exposure and outcomes is quantified by AUC. We look at the performance of the propensity score, including determining the optimal choice of variables for the propensity score model. Choices include covariates related to exposure group, covariates related to outcome, covariates related to both exposure and outcome, and all measured covariates. Additionally, we compare the propensity score approach to that of the conventional regression approach to adjust for AUC. We conduct a series of simulations to assess the performance of the methodology where the choice of the best estimator depends on bias, relative bias, mean squared error, and coverage of 95% confidence intervals. Furthermore, we examine the impact of model misspecification in conventional regression adjustment for AUC by incorrectly modelling the covariates in the data. These modelling errors include omitting covariates, dichotomizing continuous covariates, modelling quadratic covariates as linear, and excluding interactions terms from the model. Finally, a dataset from the shock research unit at the University of Southern California is used to illustrate the estimation of the adjusted AUC using the proposed approaches

Controlling for Confounding Via Propensity Score Methods Can Result in Biased Estimation of the Conditional AUC: A Simulation Study

In the medical literature, there has been an increased interest in evaluating association between exposure and outcomes using nonrandomized observational studies. However, because assignments to exposure are not random in observational studies, comparisons of outcomes between exposed and nonexposed subjects must account for the effect of confounders. Propensity score methods have been widely used to control for confounding, when estimating exposure effect. Previous studies have shown that conditioning on the propensity score results in biased estimation of conditional odds ratio and hazard ratio. However, research is lacking on the performance of propensity score methods for covariate adjustment when estimating the area under the ROC curve (AUC). In this paper, AUC is proposed as measure of effect when outcomes are continuous. The AUC is interpreted as the probability that a randomly selected nonexposed subject has a better response than a randomly selected exposed subject. A series of simulations has been conducted to examine the performance of propensity score methods when association between exposure and outcomes is quantified by AUC; this includes determining the optimal choice of variables for the propensity score models. Additionally, the propensity score approach is compared with that of the conventional regression approach to adjust for covariates with the AUC. The choice of the best estimator depends on bias, relative bias, and root mean squared error. Finally, an example looking at the relationship of depression/anxiety and pain intensity in people with sickle cell disease is used to illustrate the estimation of the adjusted AUC using the proposed approaches

Predictors of Biologic Use and Satisfaction Among Patients With Psoriasis: An Analysis and Geographic Visualization of the 2016 and 2017 National Psoriasis Foundation Annual Surveys

Background: There are an increasing number of biologic therapies approved for the treatment of psoriasis. Previous reports have identified undertreatment as a concern in the United States. Undertreatment has been associated with decreased patient satisfaction and increased morbidity. Objectives: Assess biologic use and satisfaction among respondents to the 2016 and 2017 National Psoriasis Foundation (NPF) Annual Surveys. Methods: Retrospective data analysis of the 2016 and 2017 NPF Annual Survey responses from individuals with psoriasis. ArcGIS Pro software was utilized to generate maps and perform an optimized hot spot analysis of moderate-to-severe psoriasis and biologic use. Results: There were 427 patients with psoriasis involving the skin alone. Biologics were used in3%. Respondents with BSA Conclusion: Despite the increasing number of Food and Drug Administration–approved biologic medications, the proportion of respondents on biologic therapy remained small. Treatment with biologics correlated with less residual disease and increased satisfaction. Geographic variation in state legislation as well as state and federal health insurance did not impact biologic use. However, using GIS, we identify a greater burden of moderate-to severe disease among respondents in the Southeastern United States and a lack of commensurate use of biologics in those areas

Fetal Myocardial Performance Index in the Third Trimester of Pregnancy: Feasibility and Reproducibility of Conventional Spectral Doppler versus Spectral Tissue Doppler Technique

Objective: This study aims to compare completion rates and reproducibility of myocardial performance index (MPI) using conventional spectral Doppler versus tissue Doppler in an unselected high-risk third trimester population. Study Design: This was a prospective cross-sectional study of high-risk pregnancies at ≥28 + 0 weeks’ gestation. Conventional spectral and tissue Doppler MPI of the left ventricle (LV) and right ventricle (RV) was attempted on all patients. Results: Seventy-nine pregnancies were evaluated. LV tissue Doppler MPI was completed more frequently than LV conventional spectral Doppler MPI (63/79, 79.7% vs. 45/79, 55.7%), p-value Conclusion: Tissue Doppler had statistically higher completion rates than conventional spectral Doppler, including the obese subgroup, with evidence of strong reproducibility in the third trimester

Macrophages but not Astrocytes Harbor HIV DNA in the Brains of HIV-1-Infected Aviremic Individuals on Suppressive Antiretroviral Therapy

The question of whether the human brain is an anatomical site of persistent HIV-1 infection during suppressive antiretroviral therapy (ART) is critical, but remains unanswered. The presence of virus in the brains of HIV patients whose viral load is effectively suppressed would demonstrate not only the potential for CNS to act as an anatomical HIV reservoir, but also the urgent need to understand the factors contributing to persistent HIV behind the blood-brain barrier. Here, we investigated for the first time the presence of cells harboring HIV DNA and RNA in the brains from subjects with undetectable plasma viral load and sustained viral suppression, as identified by the National NeuroAIDS Tissue Consortium. Using new, highly sensitive in situ hybridization techniques, RNAscope and DNAscope, in combination with immunohistochemistry, we were able to detect HIV-1 in the brains of all virally suppressed cases and found that brain macrophages and microglia, but not astrocytes, were the cells harboring HIV DNA in the brain. This study demonstrated that HIV reservoirs persist in brain macrophages/microglia during suppressive ART, which cure/treatment strategies will need to focus on targeting