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584 results on '"Satten, Glen A."'

2. A Bottom-up Approach to Testing Hypotheses That Have a Branching Tree Dependence Structure, with False Discovery Rate Control

Author

Li, Yunxiao, Hu, Yi-Juan, and Satten, Glen A.

Subjects
Statistics - Methodology
Abstract

Modern statistical analyses often involve testing large numbers of hypotheses. In many situations, these hypotheses may have an underlying tree structure that not only helps determine the order that tests should be conducted but also imposes a dependency between tests that must be accounted for. Our motivating example comes from testing the association between a trait of interest and groups of microbes that have been organized into operational taxonomic units (OTUs) or amplicon sequence variants (ASVs). Given p-values from association tests for each individual OTU or ASV, we would like to know if we can declare that a certain species, genus, or higher taxonomic grouping can be considered to be associated with the trait. For this problem, a bottom-up testing algorithm that starts at the lowest level of the tree (OTUs or ASVs) and proceeds upward through successively higher taxonomic groupings (species, genus, family etc.) is required. We develop such a bottom-up testing algorithm that controls the error rate of decisions made at higher levels in the tree, conditional on findings at lower levels in the tree. We further show this algorithm controls the false discovery rate based on the global null hypothesis that no taxa are associated with the trait. By simulation, we also show that our approach is better at finding driver taxa, the highest level taxa below which there are dense association signals. We illustrate our approach using data from a study of the microbiome among patients with ulcerative colitis and healthy controls.

Published
2019

7. Ranked severe maternal morbidity index for population-level surveillance at delivery hospitalization based on hospital discharge data

Author

Kuklina, Elena V., primary, Ewing, Alexander C., additional, Satten, Glen A., additional, Callaghan, William M., additional, Goodman, David A., additional, Ferre, Cynthia D., additional, Ko, Jean Y., additional, Womack, Lindsay S., additional, Galang, Romeo R., additional, and Kroelinger, Charlan D., additional

Published
2023
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13. MERIT: Controlling Monte‐Carlo error rate in large‐scale Monte‐Carlo hypothesis testing.

Author

Li, Yunxiao, Hu, Yi‐Juan, and Satten, Glen A.

Subjects
ERROR rates, DECISION making, STATISTICAL hypothesis testing, HYPOTHESIS, FALSE discovery rate
Abstract

The use of Monte‐Carlo (MC) p$$ p $$‐values when testing the significance of a large number of hypotheses is now commonplace. In large‐scale hypothesis testing, we will typically encounter at least somep$$ p $$‐values near the threshold of significance, which require a larger number of MC replicates than p$$ p $$‐values that are far from the threshold. As a result, some incorrect conclusions can be reached due to MC error alone; for hypotheses near the threshold, even a very large number (eg, 106$$ 1{0}^6 $$) of MC replicates may not be enough to guarantee conclusions reached using MC p$$ p $$‐values. Gandy and Hahn (GH)6‐8 have developed the only method that directly addresses this problem. They defined a Monte‐Carlo error rate (MCER) to be the probability that any decisions on accepting or rejecting a hypothesis based on MC p$$ p $$‐values are different from decisions based on idealp$$ p $$‐values; their method then makes decisions by controlling the MCER. Unfortunately, the GH method is frequently very conservative, often making no rejections at all and leaving a large number of hypotheses "undecided". In this article, we propose MERIT, a method for large‐scale MC hypothesis testing that also controls the MCER but is more statistically efficient than the GH method. Through extensive simulation studies, we demonstrate that MERIT controls the MCER while making more decisions that agree with the ideal p$$ p $$‐values than GH does. We also illustrate our method by an analysis of gene expression data from a prostate cancer study. [ABSTRACT FROM AUTHOR]

Published
2024
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35. Impact of experimental bias on compositional analysis of microbiome data

Author

Hu, Yingtian, Satten, Glen A., and Hu, Yi-Juan

Subjects
Article
Abstract

Microbiome data are subject to experimental bias that is caused by DNA extraction, PCR amplification among other sources, but this important feature is often ignored when developing statistical methods for analyzing microbiome data. McLaren, Willis and Callahan (2019) proposed a model for how such bias affects the observed taxonomic profiles, which assumes main effects of bias without taxon-taxon interactions. Our newly developed method, LOCOM (logistic regression for compositional analysis) for testing differential abundance of taxa, is the first method that accounted for experimental bias and is robust to the main effect biases. However, there is also evidence for taxon-taxon interactions. In this report, we formulated a model for interaction biases and used simulations based on this model to evaluate the impact of interaction biases on the performance of LOCOM as well as other available compositional analysis methods. Our simulation results indicated that LOCOM remained robust to a reasonable range of interaction biases. The other methods tended to have inflated FDR even when there were only main effect biases. LOCOM maintained the highest sensitivity even when the other methods cannot control the FDR. We thus conclude that LOCOM outperforms the other methods for compositional analysis of microbiome data considered here.

Published
2023

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