ScholarWorksIndianapolis
  • Communities & Collections
  • Browse ScholarWorks
  • English
  • Català
  • Čeština
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • Italiano
  • Latviešu
  • Magyar
  • Nederlands
  • Polski
  • Português
  • Português do Brasil
  • Suomi
  • Svenska
  • Türkçe
  • Tiếng Việt
  • Қазақ
  • বাংলা
  • हिंदी
  • Ελληνικά
  • Yкраї́нська
  • Log In
    or
    New user? Click here to register.Have you forgotten your password?
  1. Home
  2. Browse by Author

Browsing by Author "Mwangi, Ann W."

Now showing 1 - 2 of 2
Results Per Page
Sort Options
  • Loading...
    Thumbnail Image
    Item
    Active Tuberculosis Is Associated with Worse Clinical Outcomes in HIV-Infected African Patients on Antiretroviral Therapy
    (Public Library of Science, 2013) Siika, Abraham M.; Yiannoutsos, Constantin T.; Wools-Kaloustian, Kara K.; Musick, Beverly S.; Mwangi, Ann W.; Diero, Lameck O.; Kimaiyo, Sylvester N.; Tierney, William M.; Carter, Jane E.; Medicine, School of Medicine
    Objective: This cohort study utilized data from a large HIV treatment program in western Kenya to describe the impact of active tuberculosis (TB) on clinical outcomes among African patients on antiretroviral therapy (ART). Design: We included all patients initiating ART between March 2004 and November 2007. Clinical (signs and symptoms), radiological (chest radiographs) and laboratory (mycobacterial smears, culture and tissue histology) criteria were used to record the diagnosis of TB disease in the program's electronic medical record system. Methods: We assessed the impact of TB disease on mortality, loss to follow-up (LTFU) and incident AIDS-defining events (ADEs) through Cox models and CD4 cell and weight response to ART by non-linear mixed models. Results: We studied 21,242 patients initiating ART-5,186 (24%) with TB; 62% female; median age 37 years. There were proportionately more men in the active TB (46%) than in the non-TB (35%) group. Adjusting for baseline HIV-disease severity, TB patients were more likely to die (hazard ratio--HR = 1.32, 95% CI 1.18-1.47) or have incident ADEs (HR = 1.31, 95% CI: 1.19-1.45). They had lower median CD4 cell counts (77 versus 109), weight (52.5 versus 55.0 kg) and higher ADE risk at baseline (CD4-adjusted odds ratio = 1.55, 95% CI: 1.31-1.85). ART adherence was similarly good in both groups. Adjusting for gender and baseline CD4 cell count, TB patients experienced virtually identical rise in CD4 counts after ART initiation as those without. However, the overall CD4 count at one year was lower among patients with TB (251 versus 269 cells/µl). Conclusions: Clinically detected TB disease is associated with greater mortality and morbidity despite salutary response to ART. Data suggest that identifying HIV patients co-infected with TB earlier in the HIV-disease trajectory may not fully address TB-related morbidity and mortality.
  • Loading...
    Thumbnail Image
    Item
    A pseudo-likelihood method for estimating misclassification probabilities in competing-risks settings when true event data are partially observed
    (Wiley, 2020) Mpofu, Philani B.; Bakoyannis, Giorgos; Yiannoutsos, Constantin T.; Mwangi, Ann W.; Mburu, Margaret; Biostatistics, School of Public Health
    Outcome misclassification occurs frequently in binary-outcome studies and can result in biased estimation of quantities such as the incidence, prevalence, cause-specific hazards, cumulative incidence functions etc. A number of remedies have been proposed to address the potential misclassification of the outcomes in such data. The majority of these remedies lie in the estimation of misclassification probabilities, which are in turn used to adjust analyses for outcome misclassification. A number of authors advocate using a gold-standard procedure on a sample internal to the study to learn about the extent of the misclassification. With this type of internal validation, the problem of quantifying the misclassification also becomes a missing data problem as, by design, the true outcomes are only ascertained on a subset of the entire study sample. Although, the process of estimating misclassification probabilities appears simple conceptually, the estimation methods proposed so far have several methodological and practical shortcomings. Most methods rely on missing outcome data to be missing completely at random (MCAR), a rather stringent assumption which is unlikely to hold in practice. Some of the existing methods also tend to be computationally-intensive. To address these issues, we propose a computationally-efficient, easy-to-implement, pseudo-likelihood estimator of the misclassification probabilities under a missing at random (MAR) assumption, in studies with an available internal validation sample. We present the estimator through the lens of studies with competing-risks outcomes, though the estimator extends beyond this setting. We describe the consistency and asymptotic distributional properties of the resulting estimator, and derive a closed-form estimator of its variance. The finite-sample performance of this estimator is evaluated via simulations. Using data from a real-world study with competing risks outcomes, we illustrate how the proposed method can be used to estimate misclassification probabilities. We also show how the estimated misclassification probabilities can be used in an external study to adjust for possible misclassification bias when modeling cumulative incidence functions.
About IU Indianapolis ScholarWorks
  • Accessibility
  • Privacy Notice
  • Copyright © 2025 The Trustees of Indiana University