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The Compensation For Few Clusters In Clustered Randomized Trials With Binary Outcomes, Lily Stalter

Mathematics & Statistics ETDs

Cluster randomized trials are increasingly popular in epidemiological and medical research. When analyzing the data from such studies it is imperative that the hierarchical structure of the data be taken into account. Multilevel logistic regression is used to analyze clustered data with binary outcomes. Previous literature shows that a greater number of clusters is more important than a large number of subjects per cluster. This paper investigates if it is possible to compensate for the increased bias found for parameter estimates when the number of clusters is decreased. A simulation study was conducted where the absolute percent relative bias for …

Using Statistical Techniques To Estimate Rooted Species Trees From Unrooted Gene Trees, Ayed Rheal Alanzi

Mathematics & Statistics ETDs

Methods for inferring species trees from gene trees motivated by incomplete lineage

sorting typically use either rooted gene trees to infer a rooted species tree, or use

unrooted gene trees to infer an unrooted species tree, which is then typically rooted

using one or more outgroups. Theoretically, however, it has been known since 2011

that it is possible to infer the root of the species tree directly from unrooted gene

trees without assuming an outgroup. The present work is the first that we know of

which attempts to infer the root of a species tree using unrooted gene trees as …