ArticleBiostatistics (Oxford, England)2010
Multiplicity-calibrated Bayesian hypothesis tests.
Article in Biostatistics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Trial
- A MARGINAL STRUCTURAL MODEL FOR PARTIAL COMPLIANCE IN SMARTS.The annals of applied statistics · 2024Article
- Using Bayesian statistics in confirmatory clinical trials in the regulatory setting: a tutorial review.BMC medical research methodology · 2024Review
- A Bayesian Spatial Scan Statistic for Multinomial Data.Statistics & probability letters · 2024Article
- A scoping review of smoking cessation pharmacogenetic studies to advance future research across racial, ethnic, and ancestral populations.Frontiers in genetics · 2023Article
- Two-stage genome-wide search for epistasis with implementation to Recombinant Inbred Lines (RIL) populations.PloS one · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
When testing multiple hypotheses simultaneously, there is a need to adjust the levels of the individual tests to effect control of the family-wise error rate (FWER). Standard frequentist adjustments control the error rate but are typically both conservative and oblivious to prior information. We propose a Bayesian testing approach-multiplicity-calibrated Bayesian hypothesis testing-that sets individual critical values to reflect prior information while controlling the FWER via the Bonferroni inequality. If the prior information is specified correctly, in the sense that those null hypotheses considered most likely to be false in fact are false, the power of our method is substantially greater than that of standard frequentist approaches. We illustrate our method using data from a pharmacogenetic trial and a preclinical cancer study. We demonstrate its error rate control and power advantage by simulation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.