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50 results on '"Chan, Wenyaw"'

3. Treatment Sequences After Discontinuing a Tumor Necrosis Factor Inhibitor in Patients With Rheumatoid Arthritis: A Comparison of Cycling Versus Swapping Strategies

Author

Karpes Matusevich, Aliza R., primary, Duan, Zhigang, additional, Zhao, Hui, additional, Lal, Lincy S., additional, Chan, Wenyaw, additional, Suarez‐Almazor, María E., additional, Giordano, Sharon H., additional, Swint, J. Michael, additional, and Lopez‐Olivo, Maria A., additional

Published
2021
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21. P2‐031: Biomarkers associated with cardiovascular risk in the texas Alzheimer's Disease Research and Care Consortium (TARCC) Cohort: LpPla2 and Homocysteine

Author

Doody, Rachelle, primary, Chan, Wenyaw, additional, Ballantyne, Christie, additional, Pavlik, Valory, additional, Barber, Robert, additional, O'Bryant, Sid, additional, Darby, Eveleen, additional, Diaz‐Arrastia, Ramon, additional, Fairchild, Thomas, additional, Royall, Donald, additional, and Adams, Perrie, additional

Published
2011
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35. Effect of different types of smoking and synergism with hepatitis C virus on risk of hepatocellular carcinoma in American men and women: Case-control study

Author

Hassan, Manal M., primary, Spitz, Margaret R., additional, Thomas, Melanie B., additional, El-Deeb, Adel S., additional, Glover, Katrina Y., additional, Nguyen, Nga T., additional, Chan, Wenyaw, additional, Kaseb, Ahmed, additional, Curley, Steven A., additional, Vauthey, Jean-Nicolas, additional, Ellis, Lee M., additional, Abdalla, Eddie, additional, Lozano, Richard D., additional, Patt, Yehuda Z., additional, Brown, Thomas D., additional, Abbruzzese, James L., additional, and Li, Donghui, additional

Published
2008
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49. Latent classification model for censored longitudinal binary outcome.

Author

Kuo JC, Chan W, Leon-Novelo L, Lairson DR, Brown A, and Fujimoto K

Subjects
Humans, Longitudinal Studies, Computer Simulation, Models, Statistical, Texas epidemiology, SARS-CoV-2, Female, COVID-19 epidemiology, Markov Chains, Latent Class Analysis, Algorithms
Abstract

Latent classification model is a class of statistical methods for identifying unobserved class membership among the study samples using some observed data. In this study, we proposed a latent classification model that takes a censored longitudinal binary outcome variable and uses its changing pattern over time to predict individuals' latent class membership. Assuming the time-dependent outcome variables follow a continuous-time Markov chain, the proposed method has two primary goals: (1) estimate the distribution of the latent classes and predict individuals' class membership, and (2) estimate the class-specific transition rates and rate ratios. To assess the model's performance, we conducted a simulation study and verified that our algorithm produces accurate model estimates (ie, small bias) with reasonable confidence intervals (ie, achieving approximately 95% coverage probability). Furthermore, we compared our model to four other existing latent class models and demonstrated that our approach yields higher prediction accuracies for latent classes. We applied our proposed method to analyze the COVID-19 data in Houston, Texas, US collected between January first 2021 and December 31st 2021. Early reports on the COVID-19 pandemic showed that the severity of a SARS-CoV-2 infection tends to vary greatly by cases. We found that while demographic characteristics explain some of the differences in individuals' experience with COVID-19, some unaccounted-for latent variables were associated with the disease., (© 2024 John Wiley & Sons Ltd.)

Published
2024
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50. Racial disparities in the use of hospice services according to geographic residence and socioeconomic status in an elderly cohort with nonsmall cell lung cancer.

Author

Hardy D, Chan W, Liu CC, Cormier JN, Xia R, Bruera E, and Du XL

Subjects
Aged, Aged, 80 and over, Female, Humans, Male, Odds Ratio, Rural Population, Carcinoma, Non-Small-Cell Lung ethnology, Carcinoma, Non-Small-Cell Lung therapy, Ethnicity, Health Status Disparities, Hospice Care, Social Class
Abstract

Background: The authors investigated whether there were racial disparities in the receipt of hospice services within geographic residence and socioeconomic status (SES) levels., Methods: In total, 117,894 patients aged ≥66 years with nonsmall cell lung cancer (NSCLC) were identified from the Surveillance, Epidemiology, and End Results-Medicare linked database who had received hospice services in the last 6 months before death and who died between January 1, 1991 and December 31, 2005. Multivariate odds ratios (ORs) with 95% confidence intervals (CIs) using mixed-effects logistic regression models were used to describe relations., Results: In urban areas, there were significant disparity trends in age/sex-adjusted rates for blacks and Asians/Pacific Islanders compared with whites (P = .003 and P = .036, respectively). Blacks (OR, 0.79; 95% CI, 0.75-0.82), Asians/Pacific Islanders (OR, 0.42; 95% CI, 0.39-0.46), and Hispanics (OR, 0.81; 95% CI, 0.73-0.91) were less likely to receive hospice services. In rural areas, blacks were 21% less likely to receive hospice services (OR, 0.79; 95% CI, 0.63-0.98). Patients in the poorest socioeconomic status (SES) quartile were 7% less likely to receive hospice services (OR, 0.93; 95% CI, 0.86-1.00). Moreover, within stratified SES quartiles, blacks and Asians/Pacific Islanders had lower receipt of hospice services, and Asians/Pacific Islanders were the least likely to receive hospice services, particularly those in the poorest SES quartile. In general, older patients and women were more likely to receive hospice services., Conclusions: There were substantial disparities in the receipt of hospice services among ethnic minorities within urban and rural areas and within SES levels. The results indicated that efforts are needed to identify barriers, enhance support, and educate patients on the benefits of hospice services., (Copyright © 2010 American Cancer Society.)

Published
2011
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