Publications by authors named "Shannon T Holloway"

Article Synopsis
  • The text discusses a new reinforcement learning method designed to optimize dynamic treatment plans for survival outcomes while handling dependent censoring.
  • This method allows for flexibility in treatment options and stages, and aims to improve either average survival time or the likelihood of survival at a specific moment.
  • Based on simulations and real data from a health study, the new approach shows better expected results compared to previous methods.
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Background: The relationship between host conditions and microbiome profiles, typically characterized by operational taxonomic units (OTUs), contains important information about the microbial role in human health. Traditional association testing frameworks are challenged by the high dimensionality and sparsity of typical microbiome profiles. Phylogenetic information is often incorporated to address these challenges with the assumption that evolutionarily similar taxa tend to behave similarly.

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In many randomized clinical trials of therapeutics for COVID-19, the primary outcome is an ordinal categorical variable, and interest focuses on the odds ratio (OR; active agent vs control) under the assumption of a proportional odds model. Although at the final analysis the outcome will be determined for all subjects, at an interim analysis, the status of some participants may not yet be determined, for example, because ascertainment of the outcome may not be possible until some prespecified follow-up time. Accordingly, the outcome from these subjects can be viewed as censored.

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