Controlling the type 1 error rate in non-inferiority trials

2008 ◽  
Vol 27 (3) ◽  
pp. 371-381 ◽  
Author(s):  
Steven Snapinn ◽  
Qi Jiang
Keyword(s):  
Error Rate ◽  
Type 1 Error

scholarly journals Maximum type 1 error rate inflation in multiarmed clinical trials with adaptive interim sample size modifications

2014 ◽  
Vol 56 (4) ◽  
pp. 614-630 ◽  
Author(s):  
Alexandra C. Graf ◽  
Peter Bauer ◽  
Ekkehard Glimm ◽  
Franz Koenig
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Error Rate ◽  
Type 1 Error ◽  
Multiarmed Clinical Trials

scholarly journals Empirical Investigation of Type 1 Error Rate of Univariate Tests of Normality

2016 ◽  
Vol 148 (8) ◽  
pp. 24-31
Author(s):  
Kayode Ayinde ◽  
John Olatunde ◽  
Gbenga Sunday
Keyword(s):  
Error Rate ◽  
Empirical Investigation ◽  
Type 1 Error

Statistical Power in Psychiatric Research

1986 ◽  
Vol 20 (2) ◽  
pp. 189-200 ◽  
Author(s):  
Kevin D. Bird ◽  
Wayne Hall
Keyword(s):  
Sample Size ◽  
Error Rate ◽  
Statistical Power ◽  
Error Rates ◽  
Psychiatric Research ◽  
Type 1 Error ◽  
Type 2 Error ◽  
Power Analyses

Statistical power is neglected in much psychiatric research, with the consequence that many studies do not provide a reasonable chance of detecting differences between groups if they exist in the population. This paper attempts to improve current practice by providing an introduction to the essential quantities required for performing a power analysis (sample size, effect size, type 1 and type 2 error rates). We provide simplified tables for estimating the sample size required to detect a specified size of effect with a type 1 error rate of α and a type 2 error rate of β, and for estimating the power provided by a given sample size for detecting a specified size of effect with a type 1 error rate of α. We show how to modify these tables to perform power analyses for multiple comparisons in univariate and some multivariate designs. Power analyses for each of these types of design are illustrated by examples.

scholarly journals Robustness of testing procedures for confirmatory subpopulation analyses based on a continuous biomarker

2018 ◽  
Vol 28 (6) ◽  
pp. 1879-1892 ◽  
Author(s):  
Alexandra Christine Graf ◽  
Gernot Wassmer ◽  
Tim Friede ◽  
Roland Gerard Gera ◽  
Martin Posch
Keyword(s):  
Error Rate ◽  
Dependence Structure ◽  
Sequential Designs ◽  
Group Sequential ◽  
Testing Procedures ◽  
Prognostic Effect ◽  
Type 1 Error ◽  
The Family ◽  
Sequential Trials

With the advent of personalized medicine, clinical trials studying treatment effects in subpopulations are receiving increasing attention. The objectives of such studies are, besides demonstrating a treatment effect in the overall population, to identify subpopulations, based on biomarkers, where the treatment has a beneficial effect. Continuous biomarkers are often dichotomized using a threshold to define two subpopulations with low and high biomarker levels. If there is insufficient information on the dependence structure of the outcome on the biomarker, several thresholds may be investigated. The nested structure of such subpopulations is similar to the structure in group sequential trials. Therefore, it has been proposed to use the corresponding critical boundaries to test such nested subpopulations. We show that for biomarkers with a prognostic effect that is not adjusted for in the statistical model, the variability of the outcome may vary across subpopulations which may lead to an inflation of the family-wise type 1 error rate. Using simulations we quantify the potential inflation of testing procedures based on group sequential designs. Furthermore, alternative hypotheses tests that control the family-wise type 1 error rate under minimal assumptions are proposed. The methodological approaches are illustrated by a trial in depression.

scholarly journals Accurate error control in high dimensional association testing using conditional false discovery rates

2018 ◽  
Author(s):  
James Liley ◽  
Chris Wallace
Keyword(s):  
Error Rate ◽  
Error Control ◽  
Association Studies ◽  
New Method ◽  
High Dimensional ◽  
Biomedical Sciences ◽  
Type 1 Error ◽  
False Discovery ◽  
Multiple Covariates

AbstractHigh-dimensional hypothesis testing is ubiquitous in the biomedical sciences, and informative covariates may be employed to improve power. The conditional false discovery rate (cFDR) is widely-used approach suited to the setting where the covariate is a set of p-values for the equivalent hypotheses for a second trait. Although related to the Benjamini-Hochberg procedure, it does not permit any easy control of type-1 error rate, and existing methods are over-conservative. We propose a new method for type-1 error rate control based on identifying mappings from the unit square to the unit interval defined by the estimated cFDR, and splitting observations so that each map is independent of the observations it is used to test. We also propose an adjustment to the existing cFDR estimator which further improves power. We show by simulation that the new method more than doubles potential improvement in power over unconditional analyses compared to existing methods. We demonstrate our method on transcriptome-wide association studies, and show that the method can be used in an iterative way, enabling the use of multiple covariates successively. Our methods substantially improve the power and applicability of cFDR analysis.

scholarly journals Too True to be Bad: When Sets of Studies with Significant and Non-Significant Findings Are Probably True

2017 ◽  
Author(s):  
Daniel Lakens ◽  
Alexander Etz
Keyword(s):  
Publication Bias ◽  
Error Rate ◽  
Statistical Power ◽  
Scientific Literature ◽  
Alternative Hypothesis ◽  
Meta Analysis ◽  
Likelihood Ratios ◽  
Type 1 Error

Psychology journals rarely publish non-significant results. At the same time, it is often very unlikely (or ‘too good to be true’) that a set of studies yields exclusively significant results. Here, we use likelihood ratios to explain when sets of studies that contain a mix of significant and non-significant results are likely to be true, or ‘too true to be bad’. As we show, mixed results are not only likely to be observed in lines of research, but when observed, mixed results often provide evidence for the alternative hypothesis, given reasonable levels of statistical power and an adequately controlled low Type 1 error rate. Researchers should feel comfortable submitting such lines of research with an internal meta-analysis for publication. A better understanding of probabilities, accompanied by more realistic expectations of what real lines of studies look like, might be an important step in mitigating publication bias in the scientific literature.

Requiring post-hoc power of 80% amounts to an unstated lowering of the type-1 error rate

2021 ◽  
Vol 82 ◽  
pp. 99
Author(s):  
Daniel Joseph Tancredi ◽  
Danielle J. Harvey ◽  
Suzette Smiley-Jewell ◽  
Danh V. Nguyen
Keyword(s):  
Error Rate ◽  
Type 1 Error ◽  
Post Hoc

53CONTROLLING TYPE 1 ERROR RATE IN CNV GENE-SET ENRICHMENT TESTS

2019 ◽  
Vol 29 ◽  
pp. S1097-S1098
Author(s):  
Andrew Pocklington ◽  
Elliott Rees ◽  
George Kirov ◽  
James Walters ◽  
Peter Holmans ◽  
...  
Keyword(s):  
Error Rate ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Type 1 Error

scholarly journals Too True to be Bad

2017 ◽  
Vol 8 (8) ◽  
pp. 875-881 ◽  
Author(s):  
Daniël Lakens ◽  
Alexander J. Etz
Keyword(s):  
Publication Bias ◽  
Error Rate ◽  
Statistical Power ◽  
Scientific Literature ◽  
Alternative Hypothesis ◽  
Meta Analysis ◽  
Likelihood Ratios ◽  
Type 1 Error

Psychology journals rarely publish nonsignificant results. At the same time, it is often very unlikely (or “too good to be true”) that a set of studies yields exclusively significant results. Here, we use likelihood ratios to explain when sets of studies that contain a mix of significant and nonsignificant results are likely to be true or “too true to be bad.” As we show, mixed results are not only likely to be observed in lines of research but also, when observed, often provide evidence for the alternative hypothesis, given reasonable levels of statistical power and an adequately controlled low Type 1 error rate. Researchers should feel comfortable submitting such lines of research with an internal meta-analysis for publication. A better understanding of probabilities, accompanied by more realistic expectations of what real sets of studies look like, might be an important step in mitigating publication bias in the scientific literature.

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