Browsing by Author "Cheong, Taul"

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    Effect Specification, Identification, Estimation, and Inference in a Fractional Outcome Regression Model with an Endogenous Causal Variable
    (2024-08) Cheong, Taul; Terza, Joseph V.; Gupta, Sumedha; Steinberg, Richard; Liu, Ziyue
    Empirical economic research is primarily driven by the desire to offer scientific evidence that helps assess policy relevant cause-and-effect. The approach most often applied in pursuit of this objective involves regression modeling and estimation. In this dissertation, we focus on the specification, identification, estimation, and causal inference of a causal effect (CE) in the context of the fractional regression model (FRM) for which the support of the outcome variable of interest is restricted to the unit interval. Empirical applications of such models abound in health economics, health services research and health policy literatures. Examples from other disciplines include labor economics, development economics, political economics, commerce or finance. Various full information maximum likelihood and quasi-maximum likelihood regression estimators and nonlinear least squares approach have been proposed to account for the inherent nonlinearity in the FRM due to the unit interval support restriction (UISR) on the outcome variable. Additional nonlinearity is induced in the FRM when the presumed causal variable is subject to unobservable confounding (UC) [i.e., when the presumed causal variable is endogenous]. In such cases, the additional analytic and implementation effort required to account for both sources of nonlinearity (fractional outcome and UC) while avoiding UC bias (which precludes causal interpretability) can be daunting. We seek to develop and implement regression model specifications that account for the inherent nonlinearity implied by this restriction, as well as the nonlinearity that could be additionally imposed by the endogeneity of the presumed causal variable. We focus on the case where the presumed causal variable is continuous. We develop new models for FRM-based CE estimation that implement two-stage residual inclusion (2SRI) methods, as suggested by Terza et al. (2008). We assess the accuracy of our proposed new methods and compare them with extant 2SRI approaches using simulation study. An empirical application demonstrates the working of our proposed method.
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    Quantitative Evaluation of the Economic Impact of Antimicrobial Resistance on the Treatment of Community-Acquired Acute Pyelonephritis in Korea
    (Korean Society of Infectious Diseases, 2022) Cheong, Taul; Ahn, Jungmo; Kim, Yun Seop; Pai, Hyunjoo; Kim, Bongyoung; Economics, School of Liberal Arts
    Background: The proportion of antimicrobial-resistant Enterobacteriales as a causative pathogen of community-acquired acute pyelonephritis (APN) has been increasing. The aim of this study was to quantitatively evaluate the impact of antimicrobial resistance on medical costs and length of hospital stay for the treatment of APN. Materials and methods: A single-center retrospective cohort study was conducted between January 2018 and December 2019. All hospitalized patients aged ≥19 years who were diagnosed with community-acquired APN were recruited, and those diagnosed with Enterobacteriales as a causative pathogen were included. Log-linear regression analysis was performed to determine the risk factors for medical costs and length of hospital stay. Results: A total of 241 patients participated in this study. Of these, 75 (31.1%) and 87 (36.1%) had extended-spectrum beta-lactamase (ESBL)-producing pathogens and ciprofloxacin-resistant pathogens as the causative pathogen, respectively. Based on the log-linear regression model, ESBL-producing Enterobacteriales is a causative pathogen that is, on average, 27.0%, or United States Dollar (USD) 1,211 (P = 0.026) more expensive than non-ESBL-producing Enterobacteriales. A patient who is a year older would incur USD 23 (P = 0.040) more, those having any structural problems in the urinary tract would incur USD 1,231 (P = 0.015) more, and those with a unit increase in the Pitt bacteremia score would incur USD 767 (P <0.001) more, with all other variables constant. Having a case in which ESBL-producing Enterobacteriales is a causative pathogen would explain staying 22.0% longer or 2 more days (P = 0.050) in the hospital than non-ESBL-producing Enterobacteriales. A patient who is 10 years older would, on average, would have to stay for half a day longer (P = 0.045). Any structural problems in the urinary tract explain a longer stay (2.4 days longer; P = 0.032), and moving from 0 to 5 on the Pitt bacteremia score would explain four more days (P = 0.038) in the hospital. Conclusion: Patients with community-acquired APN with ESBL-producing Enterobacteriale as the causative pathogen would incur, on average, 27.0% higher medical costs and 22.0% longer hospitalization days than patients detected with non-ESBL-producing pathogens.