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Creators/Authors contains: "Zhou, Xiner"

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  1. ; ; ; (, Journal of the Royal Statistical Society Series A: Statistics in Society)
    Abstract Incorporating external data, such as external controls, holds the promise of improving the efficiency of traditional randomized controlled trials especially when treating rare diseases or diseases with unmet needs. To this end, we propose novel weighting estimators grounded in the causal inference framework. As an alternative framework, Bayesian methods are also discussed. From trial design perspective, operating characteristics including Type I error and power are particularly important and are assessed in our realistic simulation studies representing a variety of practical scenarios. Our proposed weighting estimators achieve significant power gain, while maintaining Type I error close to the nominal value of 0.05. An empirical application of the methods is demonstrated through a Phase III clinical trial in rare disease. 
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  2. ; ; ; ; (, Journal of Biopharmaceutical Statistics)
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  3. ; (, PLOS ONE)
    Guimerà, Roger (Ed.)
    We study the U.S. Supreme Court dynamics by analyzing the temporal evolution of the underlying policy positions of the Supreme Court Justices as reflected by their actual voting data, using functional data analysis methods. The proposed fully flexible nonparametric method makes it possible to dissect the time-dynamics of policy positions at the level of individual Justices, as well as providing a comprehensive view of the ideology evolution over the history of Supreme Court since its establishment. In addition to quantifying individual Justice’s policy positions, we uncover average changes over time and also the major patterns of change over time. Additionally, our approach allows for representing highly complex dynamic trajectories by a few principal components which complements other models of analyzing and predicting court behavior. 
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  4. ; ; ; ; ; ; ; ; (, Scientific Reports)
    Abstract We apply tools from functional data analysis to model cumulative trajectories of COVID-19 cases across countries, establishing a framework for quantifying and comparing cases and deaths across countries longitudinally. It emerges that a country’s trajectory during an initial first month “priming period” largely determines how the situation unfolds subsequently. We also propose a method for forecasting case counts, which takes advantage of the common, latent information in the entire sample of curves, instead of just the history of a single country. Our framework facilitates to quantify the effects of demographic covariates and social mobility on doubling rates and case fatality rates through a time-varying regression model. Decreased workplace mobility is associated with lower doubling rates with a roughly 2 week delay, and case fatality rates exhibit a positive feedback pattern. 
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