Shih, Ya‐Chen Tina

In this article, we present a flexible model for microbiome count data. We consider a quasi‐likelihood framework, in which we do not make any assumptions on the distribution of the microbiome count except that its variance is an unknown but smooth function of the mean. By comparing our model to the negative binomial generalized linear model (GLM) and Poisson GLM in simulation studies, we show that our flexible quasi‐likelihood method yields valid inferential results. Using a real microbiome study, we demonstrate the utility of our method by examining the relationship between adenomas and microbiota. We also provide an R package “fql” for the application of our method.

AbstractThe legitimacy of findings from cancer health economics research depends on study design and methods. A breakout session, Methods and Study Design for Cancer Health Economics Research, was convened at the Future of Cancer Health Economics Research Conference to discuss 2 commonly used analytic tools for cancer health economics research: observational studies and decision-analytic modeling. Observational studies include analysis of data collected with the primary purpose of supporting economic evaluation or secondary use of data collected for another purpose. Modeling studies develop a parametrized structure, such as a decision tree, to estimate hypothetical impact. Whereas observational studies focus on what has happened and why, modeling studies address what may happen. We summarize the discussion at this breakout session, focusing on 3 key elements of high-quality cancer health economics research: study design, analytical methods, and addressing uncertainty.