Adjusting for multiple testing--when and how?

By R. Bender, S. Lange. Journal of Clinical Epidemiology, 2001

2,819 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/36d92d095a0136228595ae6e1f144ce87c9e4c69

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Citations in the corpus

A Nearby Repeating Fast Radio Burst in the Direction of M81 (2021, methodology): “To account for this, we use the Bonferroni correction procedure (Bender & Lange 2001; Armstrong 2014).”
An introduction to multiplicity issues in clinical trials: the what, why, when and how. (2016, methodology): “For example, if the global objective is to assess whether two new treatments (T1, T2) are ‘both superior to the control arm (C)’, it is necessary to adjust for multiplicity - because the global comparison essentially involves testing two connected primary hypotheses regarding the superiority of each”
Exploring User Perceptions of Discrimination in Online Targeted Advertising (2017, background): “Ultimately the question of when to aggregate and adjust p-values or not to comes down judgements about the individual hypotheses being interesting each on their own [20] and the goals of the study [7].”
Multiple hypothesis testing in genomics (2014, background): “A recurring problem in multiple testing is to define what the proper collection, or family of hypotheses is over which multiple testing correction needs to be done [10].”
Fair Statistical Communication in HCI (2016, methodology): “Multiplicity correction procedures are far from having reached a consensus (Bender and Lange, 2001) and there are strong arguments against their systematic use (Wilson, 1962; Stewart-Oaten, 1995, p.”
A general introduction to adjustment for multiple comparisons. (2017, background): “Therefore, it is obvious that the proper adjustment of statistical inference is required for multiple comparisons (2).”
Do p Values Lose Their Meaning in Exploratory Analyses? It Depends How You Define the Familywise Error Rate (2017, background): “In summary, several researchers have argued that p values lose their meaning in exploratory analyses (Bender & Lange, 2001; Dahl et al., 2008; de Groot, 1956/2014; Forstmeier et al., 2016; Nosek et al., 2017; Nosek & Lakens, 2014; Wagenmakers, 2016).”
A method for meta‐analysis of molecular association studies (2005, methodology): “These methods protect against in ated type one error in the context of multiple tests [17, 18].”
Shot through with voices: Dissociation mediates the relationship between varieties of inner speech and auditory hallucination proneness (2014, methodology): “For confirmatory studies, Bonferroni corrections are generally viewed as appropriate (Bender & Lange, 2001).”
Risk of myocardial infarction in patients with HIV infection exposed to specific individual antiretroviral drugs from the 3 major drug classes: the data collection on adverse events of anti-HIV drugs (D:A:D) study. (2010, result): “This is in line with many other analyses of cohort studies in which the value of such adjustments is debated [42, 43].”
Escaping the Bonferroni iron claw in ecological studies (2004, methodology): “A more exact way of adjusting p-value for experimentalwise error rate, regardless of the dependence structure in the data, is provided by resampling techniques (Westfall and Young 1993, Bender and Lange 2001), now available in some widely used statistical software (Westfall et al. 1999).”
LOPSIDED REASONING ON LOPSIDED TESTS AND MULTIPLE COMPARISONS (2012, background): “New York: Chapman and Hall.”