Layla Parast on Muck Rack

Layla Parast

Austin
As seen in: Wiley Online Library, BMJ Open Diabetes Research & Care, Taylor & Francis Online, The Hill, U.S. News and World Report, JAMA (The Journal of the American Medical Association), Pediatrics, Health Affairs, RAND Corporation, Academic Pediatrics and
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Articles

Efficient Testing Using Surrogate Information

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By Rebecca Kathryn Knowlton, Layla Parast
| Wiley Online Library
Conflicts of Interest The authors declare no conflicts of interest. Supporting Information Filename Description bimj70086-sup-0001-Datacode.zip10.8 MB Supporting Information References , , , , and . 2000. “The Validation of Surrogate Endpoints in Meta-Analyses of Randomized Experiments.” Biostatistics 1, no. 1: 49–67. , , and . 2025. “Semi-Supervised Triply Robust Inductive Transfer Learning.” Journal of the American Statistical Association 120, no. 550: 1037–1047. , , , et al. 2007.
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Model‐Free Approach to Evaluate a Censored Intermediate Outcome as a Surrogate for Overall Survival

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By Xuan Wang, Tianxi Cai, Lu Tian, Layla Parast
| Wiley Online Library
Clinical trials or studies oftentimes require long-term and/or costly follow-up of participants to evaluate a novel treatment/drug/vaccine. There has been increasing interest in the past few decades in using short-term surrogate outcomes as a replacement for the primary outcome, that is, in using the surrogate outcome, which can potentially be observed sooner, to make inferences about the treatment effect on the long-term primary outcome.
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RISE: Two‐Stage Rank‐Based Identification of High‐Dimensional Surrogate Markers Applied to Vaccinology

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By Arthur Hughes, Layla Parast, Rodolphe Thiébaut, Boris P Hejblum
| Wiley Online Library
1 Introduction The goal of a randomized clinical trial is typically to evaluate the efficacy of a treatment on a primary outcome. However, measuring this primary outcome can be time-consuming, costly, impractical, or unethical. Consequently, there is significant interest in identifying and validating surrogate markers that can accurately infer treatment effects on the primary outcome without its direct observation [1].
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