scholarly journals Evaluating Alternative Correction Methods for Multiple Comparison in Functional Neuroimaging Research

2019 ◽  
Vol 9 (8) ◽  
pp. 198 ◽  
Author(s):  
Hyemin Han ◽  
Andrea L. Glenn ◽  
Kelsie J. Dawson

A significant challenge for fMRI research is statistically controlling for false positives without omitting true effects. Although a number of traditional methods for multiple comparison correction exist, several alternative tools have been developed that do not rely on strict parametric assumptions, but instead implement alternative methods to correct for multiple comparisons. In this study, we evaluated three of these methods, Statistical non-Parametric Mapping (SnPM), 3DClustSim, and Threshold Free Cluster Enhancement (TFCE), by examining which method produced the most consistent outcomes even when spatially-autocorrelated noise was added to the original images. We assessed the false alarm rate and hit rate of each method after noise was applied to the original images.

2019 ◽  
Author(s):  
Hyemin Han ◽  
Andrea Glenn ◽  
Kelsie J Dawson

A significant challenge for fMRI research is statistically controlling for false positives without omitting true effects. Although a number of traditional methods for multiple comparison correction exist, several alternative tools have been developed that do not rely on strict parametric assumptions, but instead implement alternative methods to correct for multiple comparisons. In this study, we evaluated three of these methods, Statistical non-Parametric Mapping (SnPM), 3DClustSim, and Threshold Free Cluster Enhancement (TFCE), by examining which method produced the most consistent outcomes even when spatially-autocorrelated noise was added to the original images. We assessed the false alarm rate and hit rate of each method after noise was applied to the original images.


2018 ◽  
Author(s):  
Xiaoying Pu ◽  
Matthew Kay

Tukey emphasized decades ago that taking exploratory findings as confirmatory is “destructively foolish”. We reframe recent conversations about the reliability of results from exploratory visual analytics—such as the multiple comparisons problem—in terms of Gelman and Loken’s garden of forking paths to lay out a design space for addressing the forking paths problem in visual analytics. This design space encompasses existing approaches to address the forking paths problem (multiple comparison correction) as well as solutions that have not been applied to exploratory visual analytics (regularization). We also discuss how perceptual bias correction techniques may be used to correct biases induced in analysts’ understanding of their data due to the forking paths problem, and outline how this problem can be cast as a threat to validity within Munzner’s Nested Model of visualization design. Finally, we suggest paper review guidelines to encourage reviewers to consider the forking paths problem when evaluating future designs of visual analytics tools.


2019 ◽  
Author(s):  
Hyemin Han

AbstractWe developed and tested Bayesian multiple comparison correction method for Bayesian voxelwise second-level fMRI analysis with R. The performance of the developed method was tested with simulation and real image datasets. First, we compared false alarm and hit rates, which were used as proxies for selectivity and sensitivity, respectively, between Bayesian and classical inference were conducted. For the comparison, we created simulated images, added noise to the created images, and analyzed the noise-added images while applying Bayesian and classical multiple comparison correction methods. Second, we analyzed five real image datasets to examine how our Bayesian method worked in realistic settings. When the performance assessment was conducted, Bayesian correction method demonstrated good sensitivity (hit rate ≥ 75%) and acceptable selectivity (false alarm rate < 10%) when N ≤ 8. Furthermore, Bayesian correction method showed better sensitivity compared with classical correction method while maintaining the aforementioned acceptable selectivity.


2021 ◽  
Author(s):  
Simon Dadson ◽  
Eleanor Blyth ◽  
Douglas Clark ◽  
Helen Davies ◽  
Richard Ellis ◽  
...  

&lt;p&gt;Timely predictions of fluvial flooding are important for national and regional planning and real-time flood response. Several new computational techniques have emerged in the past decade for making rapid fluvial flood inundation predictions at time and space scales relevant to early warning, although their efficient use is often constrained by the trade-off between model complexity, topographic fidelity and scale. Here we apply a simplified approach to large-area fluvial flood inundation modelling which combines a solution to the inertial form of the shallow water equations at 1 km horizontal resolution, with two alternative, simplified representations of sub-grid floodplain topography. One of these uses a fitted sub-grid probability distribution, the other a quantile-based representation of the floodplain. We evaluate the model&amp;#8217;s steady-state performance when used with flood depth estimates corresponding to the 0.01 Annual Exceedance Probability (AEP; &amp;#8216;100-year&amp;#8217;) flood and compare the results with published benchmark data for England. The quantile-based method accurately predicts flood inundation in 86% of locations, with a domain-wide hit rate of 95% and false alarm rate of 10%. These performance measures compare with a hit rate of 71%, and false alarm rate of 9% for the simpler, distribution-based method. We suggest that these approaches are suitable for rapid, wide-area flood forecasting and climate change impact assessment.&lt;/p&gt;


2006 ◽  
Vol 68 (4) ◽  
pp. 643-654 ◽  
Author(s):  
Michael F. Verde ◽  
Neil A. Macmillan ◽  
Caren M. Rotello

Stats ◽  
2020 ◽  
Vol 3 (1) ◽  
pp. 56-67
Author(s):  
Dewi Rahardja

In sequential tests, typically a (pairwise) multiple comparison procedure (MCP) is performed after an omnibus test (an overall equality test). In general, when an omnibus test (e.g., overall equality of multiple proportions test) is rejected, then we further conduct a (pairwise) multiple comparisons or MCPs to determine which (e.g., proportions) pairs the significant differences came from. In this article, via likelihood-based approaches, we acquire three confidence intervals (CIs) for comparing each pairwise proportion difference in the presence of over-reported binomial data. Our closed-form algorithm is easy to implement. As a result, for multiple-sample proportions differences, we can easily apply MCP adjustment methods (e.g., Bonferroni, Šidák, and Dunn) to address the multiplicity issue, unlike previous literatures. We illustrate our procedures to a real data example.


2015 ◽  
Vol 53 (10) ◽  
pp. 1011-1023 ◽  
Author(s):  
Joan Francesc Alonso ◽  
Sergio Romero ◽  
Miguel Ángel Mañanas ◽  
Mónica Rojas ◽  
Jordi Riba ◽  
...  

1986 ◽  
Vol 20 (3) ◽  
pp. 350-359 ◽  
Author(s):  
Wayne Hall ◽  
Kevin D. Bird

Methods are outlined for performing simultaneous multiple comparisons between groups when the dependent variable is one in which subjects are assigned to one of two or more categories. These methods provide tests which are analogous to Scheffe- and Bonferroni-adjusted tests of contrasts in the analysis of variance. Examples are provided of each of these procedures.


2021 ◽  
Author(s):  
Timothy F. Brady ◽  
Maria Martinovna Robinson ◽  
Jamal Rodgers Williams ◽  
John Wixted

There is a crisis of measurement in memory research, with major implications for theory and practice. This crisis arises because of a critical complication present when measuring memory using the recognition memory task that dominates the study of working memory and long-term memory (“did you see this item? yes/no” or “did this item change? yes/no”). Such tasks give two measures of performance, the “hit rate” (how often you say you previously saw an item you actually did previously see) and the “false alarm rate” (how often you say you saw something you never saw). Yet what researchers want is one single, integrated measure of memory performance. Integrating the hit and false alarm rate into a single measure, however, requires a complex problem of counterfactual reasoning that depends on the (unknowable) distribution of underlying memory signals: when faced with two people differing in both hit rate and false alarm rate, the question of who had the better memory is really “who would have had more hits if they each had the same number of false alarms”. As a result of this difficulty, different literatures in memory research (e.g., visual working memory, eyewitness identification, picture memory, etc) have settled on a variety of distinct metrics to combine hit rates and false alarm rates (e.g., A’, corrected hit rate, percent correct, d’, diagnosticity ratios, K values, etc.). These metrics make different, contradictory assumptions about the distribution of latent memory signals, and all of their assumptions are frequently incorrect. Despite a large literature on how to properly measure memory performance, spanning decades, real-life decisions are often made using these metrics, even when they subsequently turn out to be wrong when memory is studied with better measures. We suggest that in order for the psychology and neuroscience of memory to become a cumulative, theory-driven science, more attention must be given to measurement issues. We make a concrete suggestion: the default memory task should change from old/new (“did you see this item’?”) to forced-choice (“which of these two items did you see?”). In situations where old/new variants are preferred (e.g., eyewitness identification; theoretical investigations of the nature of memory decisions), receiver operating characteristic (ROC) analysis should always be performed.


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