ArticleCommunications in statistics: theory and methods2019
Conditionally unbiased estimation in the normal setting with unknown variances.
Article in Communications in statistics: theory and methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Statistical Inference for a Two-Stage Adaptive Seamless Design Using Different Binary Endpoints.Statistics in medicine · 2025Article
- Point estimation for adaptive trial designs I: A methodological review.Statistics in medicine · 2023Review
- Efficient Adaptive Designs for Clinical Trials of Interventions for COVID-19.Statistics in biopharmaceutical research · 2020Article
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Authors and funding
2 authors.
Funding
Abstract
To efficiently and completely correct for selection bias in adaptive two-stage trials, uniformly minimum variance conditionally unbiased estimators (UMVCUEs) have been derived for trial designs with normally distributed data. However, a common assumption is that the variances are known exactly, which is unlikely to be the case in practice. We extend the work of Cohen and Sackrowitz (
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.