The evidential statistical paradigm in genetics.
The evidential statistical paradigm in genetics.
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DOI：
10.1002/gepi.22151
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发表时间：
2018-10
影响因子：
2.1
通讯作者：
Strug LJ
中科院分区：
文献类型：
--
作者：
Strug LJ
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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影响因子：
16.6
作者：
Corvol H;Blackman SM;Boëlle PY;Gallins PJ;Pace RG;Stonebraker JR;Accurso FJ;Clement A;Collaco JM;Dang H;Dang AT;Franca A;Gong J;Guillot L;Keenan K;Li W;Lin F;Patrone MV;Raraigh KS;Sun L;Zhou YH;O'Neal WK;Sontag MK;Levy H;Durie PR;Rommens JM;Drumm ML;Wright FA;Strug LJ;Cutting GR;Knowles MR
通讯作者：
Knowles MR
影响因子：
1.8
作者：
Freedman, David A.
通讯作者：
Freedman, David A.
影响因子：
8.8
作者：
Dupuis, Annie;Keenan, Katherine;Gonska, Tanja
通讯作者：
Gonska, Tanja
影响因子：
1
作者：
ANSCOMBE, FJ
通讯作者：
ANSCOMBE, FJ
影响因子：
1.4
作者：
Bickel, David R.
通讯作者：
Bickel, David R.