A5030899955- SARS-CoV-2 and COVID-19 Research
- Statistical Methods and Bayesian Inference
- Influenza Virus Research Studies
- Statistical Methods and Inference
- Viral Infections and Immunology Research
- Viral Infectious Diseases and Gene Expression in Insects
- Meta-analysis and systematic reviews
- Economic and Environmental Valuation
- Statistical Methods in Clinical Trials
- Hemodynamic Monitoring and Therapy
- Vaccine Coverage and Hesitancy
- Advanced Causal Inference Techniques
- Animal Disease Management and Epidemiology
- SARS-CoV-2 detection and testing
University of Michigan
2023
University of Washington
2020
The test-negative design (TND) has become a standard approach to evaluate vaccine effectiveness against the risk of acquiring infectious diseases in real-world settings, such as Influenza, Rotavirus, Dengue fever, and more recently COVID-19. In TND study, individuals who experience symptoms seek care are recruited tested for disease which defines cases controls. Despite TND's potential reduce unobserved differences, healthcare seeking behavior (HSB) between vaccinated unvaccinated subjects,...
Abstract The widespread testing for severe acute respiratory syndrome coronavirus 2 infection has facilitated the use of test-negative designs (TNDs) modeling disease 2019 (COVID-19) vaccination and outcomes. Despite comprehensive literature on TND, TND in COVID-19 studies is relatively new calls robust design analysis to adapt a rapidly changing dynamically evolving pandemic account changes reporting practices. In this commentary, we aim draw attention researchers COVID-specific challenges...
Meta‐analysis of 2 × tables is common and useful in research topics including analysis adverse events survey data. Fixed‐effects inference typically centers on measures association such as the Cochran‐Mantel‐Haenszel statistic or Woolf's estimator, but relies assuming exact homogeneity across studies, which often unrealistic. By showing that estimators several widely‐used methods have meaningful estimands even presence heterogeneity, we derive improved confidence intervals for them under...
Test-negative designs are widely used for post-market evaluation of vaccine effectiveness. Different from classical test-negative where only healthcare-seekers with symptoms included, recent have involved individuals various reasons testing, especially in an outbreak setting. While including these data can increase sample size and hence improve precision, concerns been raised about whether they will introduce bias into the current framework designs, thereby demanding a formal statistical...
The test-negative design (TND) has become a standard approach to evaluate vaccine effectiveness against the risk of acquiring infectious diseases in real-world settings, such as Influenza, Rotavirus, Dengue fever, and more recently COVID-19. In TND study, individuals who experience symptoms seek care are recruited tested for disease which defines cases controls. Despite TND's potential reduce unobserved differences healthcare seeking behavior (HSB) between vaccinated unvaccinated subjects,...
Large-sample Bayesian analogs exist for many frequentist methods, but are less well-known the widely-used 'sandwich' or 'robust' variance estimates. We review existing approaches to of sandwich estimates and propose a new analog, as Bayes rule under form balanced loss function, that combines elements standard parametric inference with fidelity data model. Our development is general, essentially any regression setting independent outcomes. Being large-sample equivalent its counterpart, we...
Unmeasured confounding and selection bias are often of concern in observational studies may invalidate a causal analysis if not appropriately accounted for. Under outcome-dependent sampling, latent factor that has effects on the treatment, outcome, sample process cause both unmeasured bias, rendering standard parameters unidentifiable without additional assumptions. an odds ratio model for treatment effect, Li et al. 2022 established proximal identification estimation by leveraging pair...