ReviewGenetic epidemiology2018
The evidential statistical paradigm in genetics.
Review in Genetic epidemiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- The PPLD has advantages over conventional regression methods in application to moderately sized genome-wide association studies.PloS one · 2021Article
- Presidential address: Six open questions to genetic epidemiologists.Genetic epidemiology · 2019Article
- The evidential statistical paradigm in genetics.Genetic epidemiology · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
Concerns over reproducibility in research has reinvigorated the discourse on P-values as measures of statistical evidence. In a position statement by the American Statistical Association board of directors, they warn of P-value misuse and refer to the availability of alternatives. Despite the common practice of comparing P-values across different hypothesis tests in genetics, it is well-appreciated that P-values must be interpreted alongside the sample size and experimental design used for their computation. Here, we discuss the evidential statistical paradigm (EP), an alternative to Bayesian and Frequentist paradigms, that has been implemented in human genetics studies. Using applications in Cystic Fibrosis genetic association analyses, and describing recent theoretical developments, we review how to measure statistical evidence using the EP in the presence of covariates, model misspecification, and for composite hypotheses. Novel graphical displays are presented, and software for their computation is highlighted. The implications of multiple hypothesis testing for the EP are delineated in the analyses, demonstrating a view more consistent with scientific reasoning; the EP provides a theoretical justification for replication that is a requirement in genetic association studies. As genetic studies grow in size and complexity, a fresh look at measures of statistical evidence that are sensible amid the analysis of big data are required.
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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.