Publications by authors named "Fangya Mao"
Article Synopsis
- Epidemiological studies using 2-phase designs allow researchers to efficiently allocate resources when measuring costly covariates is impractical, typically involving an inexpensive data collection phase followed by a subsampling phase for expensive measurements.
- The study introduces advanced design strategies that cater to scenarios with long-term survivors who are nonsusceptible, incorporating mixture models and three regression frameworks to analyze different groups based on susceptibility.
- The new bivariate residual-dependent designs specifically address challenges in scenarios where two parameters of interest are involved, and simulations show that this method outperforms existing subsampling techniques, with practical applications demonstrated in cancer screening trials.
We consider the design and analysis of two-phase studies aiming to assess the relation between a fixed (eg, genetic) marker and an event time under current status observation. We consider a common setting in which a phase I sample is comprised of a large cohort of individuals with outcome (ie, current status) data and a vector of inexpensive covariates. Stored biospecimens for individuals in the phase I sample can be assayed to record the marker of interest for individuals selected in a phase II sub-sample.
View Article and Find Full Text PDFImportant scientific insights into chronic diseases affecting several organ systems can be gained from modeling spatial dependence of sites experiencing damage progression. We describe models and methods for studying spatial dependence of joint damage in psoriatic arthritis (PsA). Since a large number of joints may remain unaffected even among individuals with a long disease history, spatial dependence is first modeled in latent joint-specific indicators of susceptibility.
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