Topic
Bonferroni correction
About: Bonferroni correction is a research topic. Over the lifetime, 1155 publications have been published within this topic receiving 77787 citations.
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TL;DR: This paper advances the view, widely held by epidemiologists, that Bonferroni adjustments are, at best, unnecessary and, at worst, deleterious to sound statistical inference.
Abstract: When more than one statistical test is performed in analysing the data from a clinical study, some statisticians and journal editors demand that a more stringent criterion be used for “statistical significance” than the conventional P<0051 Many well meaning researchers, eager for methodological rigour, comply without fully grasping what is at stake Recently, adjustments for multiple tests (or Bonferroni adjustments) have found their way into introductory texts on medical statistics, which has increased their apparent legitimacy This paper advances the view, widely held by epidemiologists, that Bonferroni adjustments are, at best, unnecessary and, at worst, deleterious to sound statistical inference
#### Summary points
Adjusting statistical significance for the number of tests that have been performed on study data—the Bonferroni method—creates more problems than it solves
The Bonferroni method is concerned with the general null hypothesis (that all null hypotheses are true simultaneously), which is rarely of interest or use to researchers
The main weakness is that the interpretation of a finding depends on the number of other tests performed
The likelihood of type II errors is also increased, so that truly important differences are deemed non-significant
Simply describing what tests of significance have been performed, and why, is generally the best way of dealing with multiple comparisons
Bonferroni adjustments are based on the following reasoning1-3 If a null hypothesis is true (for instance, two treatment groups in a randomised trial do not differ in terms of cure rates), a significant difference (P<005) will be observed by chance once in 20 trials This is the type I error, or α When 20 independent tests are performed (for example, study groups are compared with regard to 20 unrelated variables) and the null hypothesis holds for all 20 comparisons, the chance of at least one test being significant is no longer 005, but 064 …
5,471 citations
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TL;DR: This paper introduces to the neuroscience literature statistical procedures for controlling the false discovery rate (FDR) and demonstrates this approach using both simulations and functional magnetic resonance imaging data from two simple experiments.
4,838 citations
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TL;DR: In this article, a simple procedure for multiple tests of significance based on individual p-values is derived, which is sharper than Holm's (1979) sequentially rejective procedure.
Abstract: SUMMARY A simple procedure for multiple tests of significance based on individual p-values is derived. This simple procedure is sharper than Holm's (1979) sequentially rejective procedure. Both procedures contrast the ordered p-values with the same set of critical values. Holm's procedure rejects an hypothesis only if its p-value and each of the smaller p-values are less than their corresponding critical-values. The new procedure rejects all hypotheses with smaller or- equal p-values to that of any one found less than its critical value.
4,610 citations
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TL;DR: A joinpoint regression model is applied to describe continuous changes in the recent trend and the grid-search method is used to fit the regression function with unknown joinpoints assuming constant variance and uncorrelated errors.
Abstract: The identification of changes in the recent trend is an important issue in the analysis of cancer mortality and incidence data. We apply a joinpoint regression model to describe such continuous changes and use the grid-search method to fit the regression function with unknown joinpoints assuming constant variance and uncorrelated errors. We find the number of significant joinpoints by performing several permutation tests, each of which has a correct significance level asymptotically. Each p-value is found using Monte Carlo methods, and the overall asymptotic significance level is maintained through a Bonferroni correction. These tests are extended to the situation with non-constant variance to handle rates with Poisson variation and possibly autocorrelated errors. The performance of these tests are studied via simulations and the tests are applied to U.S. prostate cancer incidence and mortality rates.
3,950 citations
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TL;DR: The False Discovery Rate (FDR) is the expected proportion of false discoveries among the discoveries, and controlling the FDR goes a long way towards controlling the increased error from multiplicity while losing less in the ability to discover real differences.
3,504 citations