M
Melanie Quintana
Researcher at University of Southern California
Publications - 25
Citations - 776
Melanie Quintana is an academic researcher from University of Southern California. The author has contributed to research in topics: Clinical trial & Medicine. The author has an hindex of 10, co-authored 20 publications receiving 522 citations.
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Journal ArticleDOI
Association of Pathologic Complete Response to Neoadjuvant Therapy in HER2-Positive Breast Cancer With Long-Term Outcomes: A Meta-Analysis
Kristine Broglio,Melanie Quintana,Margaret J. Foster,Melissa Olinger,Anna McGlothlin,Scott M. Berry,Jean Francois Boileau,Christine Brezden-Masley,Stephen Chia,Susan Dent,Karen A. Gelmon,Alexander H.G. Paterson,Daniel Rayson,Donald A. Berry +13 more
TL;DR: Assessment of the utility of pathologic complete response (pCR) for neoadjuvant drug development in human epidermal growth factor receptor 2 (HER2]-positive breast cancer finds that it is associated with substantially longer times to recurrence and death.
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The DIAN-TU Next Generation Alzheimer's prevention trial: Adaptive design and disease progression model.
Randall J. Bateman,Tammie L.S. Benzinger,Scott M. Berry,David B. Clifford,Cynthia Duggan,Anne M. Fagan,Kathleen Fanning,Martin R. Farlow,Jason Hassenstab,Eric McDade,Susan Mills,Katrina L. Paumier,Melanie Quintana,Stephen Salloway,Anna Santacruz,Lon S. Schneider,Guoqiao Wang,Chengjie Xiong +17 more
TL;DR: With completion of enrollment of the first two drug arms, the DIAN‐TU now plans to add new drugs to the platform, designated as the Next Generation (NexGen) prevention trial.
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Bayesian Analysis: Using Prior Information to Interpret the Results of Clinical Trials.
TL;DR: The investigators used a Bayesian analysis of the treatment effect to ensure that a clinically useful result would be obtained even if traditional approaches for defining statistical significance were impractical.
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Incorporating model uncertainty in detecting rare variants: the Bayesian risk index.
TL;DR: This framework aims at constructing a risk index based on multiple rare variants within a region by using a Bayesian approach to incorporate model uncertainty in the selection of variants to include in the index as well as the direction of the associated effects.
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Integrative variable selection via Bayesian model uncertainty.
Melanie Quintana,David V. Conti +1 more
TL;DR: An integrative Bayesian model uncertainty (iBMU) method, which formally incorporates multiple sources of data via a second‐stage probit model on the probability that any predictor is associated with the outcome of interest, is developed.