Browsing by Author "Li, Ruohong"
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Item Acceptance and commitment therapy for symptom interference in metastatic breast cancer patients: a pilot randomized trial(Springer Nature, 2018-06) Mosher, Catherine E.; Secinti, Ekin; Li, Ruohong; Hirsh, Adam T.; Bricker, Jonathan; Miller, Kathy D.; Schneider, Bryan; Storniolo, Anna Maria; Mina, Lida; Newton, Erin V.; Champion, Victoria L.; Johns, Shelley A.; Psychology, School of SciencePURPOSE: Breast cancer is the leading cause of cancer mortality in women worldwide. With medical advances, metastatic breast cancer (MBC) patients often live for years with many symptoms that interfere with activities. However, there is a paucity of efficacious interventions to address symptom-related suffering and functional interference. Thus, this study examined the feasibility and preliminary efficacy of telephone-based acceptance and commitment therapy (ACT) for symptom interference with functioning in MBC patients. METHODS: Symptomatic MBC patients (N = 47) were randomly assigned to six telephone sessions of ACT or six telephone sessions of education/support. Patients completed measures of symptom interference and measures assessing the severity of pain, fatigue, sleep disturbance, depressive symptoms, and anxiety. RESULTS: The eligibility screening rate (64%) and high retention (83% at 8 weeks post-baseline) demonstrated feasibility. When examining within-group change, ACT participants showed decreases in symptom interference (i.e., fatigue interference and sleep-related impairment; Cohen's d range = - 0.23 to - 0.31) at 8 and 12 weeks post-baseline, whereas education/support participants showed minimal change in these outcomes (d range = - 0.03 to 0.07). Additionally, at 12 weeks post-baseline, ACT participants showed moderate decreases in fatigue and sleep disturbance (both ds = - 0.43), whereas education/support participants showed small decreases in these outcomes (ds = - 0.24 and - 0.18 for fatigue and sleep disturbance, respectively). Both the ACT and education/support groups showed reductions in depressive symptoms (ds = - 0.27 and - 0.28) at 12 weeks post-baseline. Group differences in all outcomes were not statistically significant. CONCLUSIONS: ACT shows feasibility and promise in improving fatigue and sleep-related outcomes in MBC patients and warrants further investigation.Item Age-specific rates of hospital transfers in long-stay nursing home residents(Oxford Academic, 2022-01) Tu, Wanzhu; Li, Ruohong; Stump, Timothy E.; Fowler, Nicole R.; Carnahan, Jennifer L.; Blackburn, Justin; Sachs, Greg A.; Hickman, Susan E.; Unroe, Kathleen T.; Biostatistics, School of Public HealthIntroduction hospital transfers and admissions are critical events in the care of nursing home residents. We sought to determine hospital transfer rates at different ages. Methods a cohort of 1,187 long-stay nursing home residents who had participated in a Centers for Medicare and Medicaid demonstration project. We analysed the number of hospital transfers of the study participants recorded by the Minimum Data Set. Using a modern regression technique, we depicted the annual rate of hospital transfers as a smooth function of age. Results transfer rates declined with age in a nonlinear fashion. Rates were the highest among residents younger than 60 years of age (1.30-2.15 transfers per year), relatively stable between 60 and 80 (1.17-1.30 transfers per year) and lower in those older than 80 (0.77-1.17 transfers per year). Factors associated with increased risk of transfers included prior diagnoses of hip fracture (annual incidence rate ratio or IRR: 2.057, 95% confidence interval (CI): [1.240, 3.412]), dialysis (IRR: 1.717, 95% CI: [1.313, 2.246]), urinary tract infection (IRR: 1.755, 95% CI: [1.361, 2.264]), pneumonia (IRR: 1.501, 95% CI: [1.072, 2.104]), daily pain (IRR: 1.297, 95% CI: [1.055,1.594]), anaemia (IRR: 1.229, 95% CI [1.068, 1.414]) and chronic obstructive pulmonary disease (IRR: 1.168, 95% CI: [1.010,1.352]). Transfer rates were lower in residents who had orders reflecting preferences for comfort care (IRR: 0.79, 95% CI: [0.665, 0.936]). Discussion younger nursing home residents may require specialised interventions to reduce hospital transfers; declining transfer rates with the oldest age groups may reflect preferences for comfort-focused care.Item Environments and situations as correlates of eating and drinking among women living with obesity and urban poverty(Wiley, 2021-09-01) Clark, Daniel O.; Keith, NiCole R.; Ofner, Susan; Hackett, Jason; Li, Ruohong; Agarwal, Neeta; Tu, Wanzhu; Medicine, School of MedicineObjective: One path to improving weight management may be to lessen the self-control burden of physical activity and healthier food choices. Opportunities to lessen the self-control burden might be uncovered by assessing the spatiotemporal experiences of individuals in daily context. This report aims to describe the time, place, and social context of eating and drinking and 6-month weight change among 209 midlife women (n = 113 African-American) with obesity receiving safety-net primary care. Methods: Participants completed baseline and 6-month weight measures, observations and interviews regarding obesogenic cues in the home environment, and up to 12 ecological momentary assessments (EMA) per day for 30 days inquiring about location, social context, and eating and drinking. Results: Home was the most common location (62%) at times of EMA notifications. Participants reported "yes" to eating or drinking at the time of nearly one in three (31.1% ± 13.2%) EMA notifications. Regarding social situations, being alone was significantly associated with less frequent eating and drinking (OR = 0.75) unless at work in which case being alone was significantly associated with a greater frequency of eating or drinking (OR = 1.43). At work, eating was most common late at night, whereas at home eating was most frequent in the afternoon and evening hours. However, eating and drinking frequency was not associated with 6-month weight change. Conclusions: Home and work locations, time of day, and whether alone may be important dimensions to consider in the pursuit of more effective weight loss interventions. Opportunities to personalize weight management interventions, whether digital or human, and lessen in-the-moment self-control burden might lie in identifying times and locations most associated with caloric consumption.Item Poverty, Comorbidity, and Ethnicity: COVID-19 Outcomes in a Safety Net Health System(Ethnicity & Disease, Inc., 2022-04-21) Smith, Joseph P.; Kressel, Amy B.; Grout, Randall W.; Weaver, Bree; Cheatham, Megan; Tu, Wanzhu; Li, Ruohong; Crabb, David W.; Harris, Lisa E.; Carlos, William G.; Medicine, School of MedicineObjective: To determine if race-ethnicity is correlated with case-fatality rates among low-income patients hospitalized for COVID-19. Research design: Observational cohort study using electronic health record data. Patients: All patients assessed for COVID-19 from March 2020 to January 2021 at one safety net health system. Measures: Patient demographic and clinical characteristics, and hospital care processes and outcomes. Results: Among 25,253 patients assessed for COVID-19, 6,357 (25.2%) were COVID-19 positive: 1,480 (23.3%) hospitalized; 334 (22.6%) required intensive care; and 106 (7.3%) died. More Hispanic patients tested positive (51.8%) than non-Hispanic Black (31.4%) and White patients (16.7%, P<.001]. Hospitalized Hispanic patients were younger, more often uninsured, and less likely to have comorbid conditions. Non-Hispanic Black patients had significantly more diabetes, hypertension, obesity, chronic kidney disease, and asthma (P<.05). Non-Hispanic White patients were older and had more cigarette smoking history, COPD, and cancer. Non-Hispanic White patients were more likely to receive intensive care (29.6% vs 21.1% vs 20.8%, P=.007) and more likely to die (12% vs 7.3% vs 3.5%, P<.001) compared with non-Hispanic Black and Hispanic patients, respectively. Length of stay was similar for all groups. In logistic regression models, Medicaid insurance status independently correlated with hospitalization (OR 3.67, P<.001) while only age (OR 1.076, P<.001) and cerebrovascular disease independently correlated with in-hospital mortality (OR 2.887, P=.002). Conclusions: Observed COVID-19 in-hospital mortality rate was lower than most published rates. Age, but not race-ethnicity, was independently correlated with in-hospital mortality. Safety net health systems are foundational in the care of vulnerable patients suffering from COVID-19, including patients from under-represented and low-income groups.Item Robust estimation of heterogeneous treatment effects using electronic health record data(Wiley, 2021-05) Li, Ruohong; Wang, Honglang; Tu, Wanzhu; Biostatistics, School of Public HealthEstimation of heterogeneous treatment effects is an essential component of precision medicine. Model and algorithm-based methods have been developed within the causal inference framework to achieve valid estimation and inference. Existing methods such as the A-learner, R-learner, modified covariates method (with and without efficiency augmentation), inverse propensity score weighting, and augmented inverse propensity score weighting have been proposed mostly under the square error loss function. The performance of these methods in the presence of data irregularity and high dimensionality, such as that encountered in electronic health record (EHR) data analysis, has been less studied. In this research, we describe a general formulation that unifies many of the existing learners through a common score function. The new formulation allows the incorporation of least absolute deviation (LAD) regression and dimension reduction techniques to counter the challenges in EHR data analysis. We show that under a set of mild regularity conditions, the resultant estimator has an asymptotic normal distribution. Within this framework, we proposed two specific estimators for EHR analysis based on weighted LAD with penalties for sparsity and smoothness simultaneously. Our simulation studies show that the proposed methods are more robust to outliers under various circumstances. We use these methods to assess the blood pressure-lowering effects of two commonly used antihypertensive therapies.Item Robust estimation of heterogeneous treatment effects: an algorithm-based approach(Taylor & Francis, 2021) Li, Ruohong; Wang, Honglang; Zhao, Yi; Su, Jing; Tu, Wanzhu; Biostatistics, School of Public HealthHeterogeneous treatment effect estimation is an essential element in the practice of tailoring treatment to suit the characteristics of individual patients. Most existing methods are not sufficiently robust against data irregularities. To enhance the robustness of the existing methods, we recently put forward a general estimating equation that unifies many existing learners. But the performance of model-based learners depends heavily on the correctness of the underlying treatment effect model. This paper addresses this vulnerability by converting the treatment effect estimation to a weighted supervised learning problem. We combine the general estimating equation with supervised learning algorithms, such as the gradient boosting machine, random forest, and artificial neural network, with appropriate modifications. This extension retains the estimators’ robustness while enhancing their flexibility and scalability. Simulation shows that the algorithm-based estimation methods outperform their model-based counterparts in the presence of nonlinearity and non-additivity. We developed an R package, RCATE, for public access to the proposed methods. To illustrate the methods, we present a real data example to compare the blood pressure-lowering effects of two classes of antihypertensive agents.Item Treatment Effect Estimation and Therapeutic Optimization Using Observational Data(2021-05) Li, Ruohong; Tu, Wanzhu; Wang, Honglang; Zhao, Yi; Huang, Kun; Hasan, Mohammad AlIn this dissertation, I address two essential questions of modern therapeutics: (1) to quantify the e ects of pharmacological agents as functions of patient's clinical characteristics; (2) to optimize individual treatment regimen in the presence of multiple treatment options. To address the rst question, I proposed a uni ed framework for the estimation of heterogeneous treatment e ect (x), which is expressed as a function of the patient characteristics x. The proposed framework not only covers most of the existing advantage-learning methods in the literature, but also enhances the robustness of di erent learning methods against outliers by allowing the selection of appropriate loss functions. To cope with high-dimensionality in x, I incorporated into the method modern machine learning algorithms including random forests, gradient boosting machines, and neural networks, for a more scalable implementation. To facilitate the wider use of the developed methods, I developed an R package RCATE, which is now posted on Github for public access. For therapeutic optimization, I developed a treatment recommendation system using o ine reinforcement learning. O ine reinforcement learning is a type of machine learning method that enables an agent to learn an optimal policy in the absence of an interactive environment, such as those encountered in the analysis of therapeutics data. The recommendation system optimizes long-term reward, while accounting for the safety of treatment regimens. I tested the method using data from the Systolic Blood Pressure Trial (SPRINT), which included multiple years of follow-up data from thousands of patients on many di erent antihypertensive drugs. Using the SPRINT data, I developed a treatment recommendation system for antihypertensive therapies.