scholarly journals Standardized Brain MRI Acquisition Protocols Improve Statistical Power in Multicenter Quantitative Morphometry Studies

2019 ◽  
Vol 30 (1) ◽  
pp. 126-133
Author(s):  
Allan George ◽  
Ruben Kuzniecky ◽  
Henry Rusinek ◽  
Heath R. Pardoe ◽  
Keyword(s):  
Statistical Power ◽  
Brain Mri ◽  
Quantitative Morphometry

scholarly journals The Effect of Fluoxetine on Progression in Progressive Multiple Sclerosis: A Double-Blind, Randomized, Placebo-Controlled Trial

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Jop Mostert ◽  
Thea Heersema ◽  
Manju Mahajan ◽  
Jeroen Van Der Grond ◽  
Mark A. Van Buchem ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Placebo Group ◽  
Treatment Effect ◽  
Statistical Power ◽  
Controlled Trial ◽  
Brain Mri ◽  
Progressive Multiple Sclerosis ◽  
Grey Matter Volume ◽  
Double Blind ◽  
Matter Volume

Preclinical studies suggest that fluoxetine may have neuroprotective properties. In this pilot study forty-two patients with secondary or primary progressive MS were randomized to receive fluoxetine 20 mg twice daily or placebo for 2 years. Every 3 months the Expanded Disability Status Scale (EDSS), 9-hole peg test (9-HPT) and ambulation index (AI) were assessed. Brain MRI scans, Multiple Sclerosis Functional Composite, Fatigue Impact Scale, Guy’s neurological disability Scale and SF-36 were performed at baseline, year 1 and year 2. Seven out of 20 (35%) patients in the fluoxetine group and 7 out of 22 (32%) patients in the placebo group had sustained progression on the EDSS, 9-HPT, or AI at 2 years. No differences were identified between the 2 treatment groups with respect to secondary clinical outcomes and T2 lesion load, grey matter volume and white matter volume. An unanticipated low rate of disability progression in the placebo group decreased the statistical power. At least 200 patients would have been needed to detect a 50% treatment effect. This trial shows that fluoxetine was generally well tolerated, but no assumptions can be made about a possible treatment effect. An adequately powered controlled trial of fluoxetine in progressive MS is still warranted. This trial is registered with Current Controlled Trials ISRCTN38456328.

scholarly journals A new virtue of phantom MRI data: explaining variance in human participant data

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 1131
Author(s):  
Christopher P. Cheng ◽  
Yaroslav O. Halchenko
Keyword(s):  
Seasonal Variation ◽  
Statistical Power ◽  
Signal To Noise Ratio ◽  
Statistical Significance ◽  
Brain Mri ◽  
Complex Data ◽  
Weak Support ◽  
Phantom Data ◽  
Mri Studies ◽  
Many Sources

Background: Magnetic resonance imaging (MRI) is an important yet complex data acquisition technology for studying the brain. MRI signals can be affected by many factors and many sources of variance are often simply attributed to “noise”. Unexplained variance in MRI data hinders the statistical power of MRI studies and affects their reproducibility. We hypothesized that it would be possible to use phantom data as a proxy of scanner characteristics with a simplistic model of seasonal variation to explain some variance in human MRI data. Methods: We used MRI data from human participants collected in several studies, as well as phantom data collected weekly for scanner quality assurance (QA) purposes. From phantom data we identified the variables most likely to explain variance in acquired data and assessed their statistical significance by using them to model signal-to-noise ratio (SNR), a fundamental MRI QA metric. We then included phantom data SNR in the models of morphometric measures obtained from human anatomical MRI data from the same scanner. Results: Phantom SNR and seasonal variation, after multiple comparisons correction, were statistically significant predictors of the volume of gray brain matter. However, a sweep over 16 other brain matter areas and types revealed no statistically significant predictors among phantom SNR or seasonal variables after multiple comparison correction. Conclusions: Seasonal variation and phantom SNR may be important factors to account for in MRI studies. Our results show weak support that seasonal variations are primarily caused by biological human factors instead of scanner performance variation. The phantom QA metric and scanning parameters are useful for more than just QA. Using QA metrics, scanning parameters, and seasonal variation data can help account for some variance in MRI studies, thus making them more powerful and reproducible.

scholarly journals An illustration of reproducibility in neuroscience research in the absence of selective reporting

2019 ◽  
Author(s):  
Xiang-Zhen Kong ◽  
Clyde Francks ◽  
Keyword(s):  
Publication Bias ◽  
Cortical Thickness ◽  
Statistical Power ◽  
Biological Effects ◽  
Brain Mri ◽  
Collaborative Study ◽  
Selective Reporting ◽  
Neuroscience Research ◽  
The World ◽  
Empirical Illustration

AbstractThe problem of poor reproducibility of scientific findings has received much attention over recent years, in a variety of fields including psychology and neuroscience. The problem has been partly attributed to publication bias and unwanted practices such as p-hacking. Low statistical power in individual studies is also understood to be an important factor. In a recent multi-site collaborative study, we mapped brain anatomical left-right asymmetries for regional measures of surface area and cortical thickness, in 99 MRI datasets from around the world, for a total of over 17,000 participants. In the present study, we re-visited these hemispheric effects from the perspective of reproducibility. Within each dataset, we considered that an effect had been reproduced when it matched the meta-analytic effect from the 98 other datasets, in terms of effect direction and uncorrected significance at p<0.05. In this sense, the results within each dataset were viewed as coming from separate studies in an ‘ideal publishing environment’, i.e. free from selective reporting and p hacking. We found an average reproducibility rate per dataset, over all effects, of 63.2% (SD = 22.9%, min = 22.2%, max = 97.0%). As expected, reproducibility was higher for larger effects and in larger datasets. There is clearly substantial room to improve reproducibility in brain MRI research through increasing statistical power. These findings constitute an empirical illustration of reproducibility in the absence of publication bias or p hacking, when assessing realistic biological effects in heterogeneous neuroscience data, and given typically-used sample sizes.

scholarly journals The Genetic Architecture of Amygdala Nuclei

2021 ◽  
Author(s):  
Mary S Mufford ◽  
Dennis van der Meer ◽  
Tobias Kaufmann ◽  
Oleksandr Frei ◽  
Raj Ramesar ◽  
...  
Keyword(s):  
Statistical Power ◽  
Genetic Architecture ◽  
Brain Mri ◽  
Genetic Correlations ◽  
Complex Trait ◽  
Central Nucleus ◽  
Association Analyses ◽  
Genome Wide ◽  
Amygdala Volume ◽  
Shared Genetic

Background: Whereas a number of genetic variants influencing total amygdala volume have been identified in previous research, genetic architecture of its distinct nuclei have yet to be thoroughly explored. We aimed to investigate whether increased phenotypic specificity through segmentation of the nuclei aids genetic discoverability and sheds light on the extent of shared genetic architecture and biological pathways between the nuclei and disorders associated with the amygdala. Methods: T1-weighted brain MRI scans (n=36,352, mean age= 64.26 years, 52% female) of trans-ancestry individuals from the UK Biobank were segmented into nine amygdala nuclei with FreeSurfer v6.1, and genome-wide association analyses were performed on the full sample and a European-only subset (n=31,690). We estimated heritability using Genome-wide Complex Trait Analysis, derived estimates of polygenicity, discoverability and power using MiXer, and identified genetic correlations and shared loci with psychiatric disorders using Linkage Disequilibrium Score Regression and conjunctional FDR, followed by functional annotation using FUMA. Results: The SNP-based heritability of the nuclei ranged between 0.17-0.33, and the central nucleus had the greatest statistical power for discovery. Across the whole amygdala and the nuclei volumes, 38 novel significant (p < 5x10-9) loci were identified, with most loci mapped to the central nucleus. The mapped genes and associated pathways revealed both unique and shared effects across the nuclei, and immune-related pathways were particularly enriched across several nuclei. Conclusions: These findings indicate that the amygdala nuclei volumes have significant genetic heritability, increased power for discovery compared to whole amygdala volume, may have unique and shared genetic architectures, and a significant immune component to their aetiology.

Power-Enhanced Funnel Plots for Meta-Analysis

2020 ◽  
Vol 228 (1) ◽  
pp. 43-49 ◽  
Author(s):  
Michael Kossmeier ◽  
Ulrich S. Tran ◽  
Martin Voracek
Keyword(s):  
Statistical Power ◽  
Meta Analysis ◽  
Relevant Information ◽  
Selective Reporting ◽  
Graphical Display ◽  
Funnel Plot ◽  
Evidential Value ◽  
Graphical Displays ◽  
Funnel Plots ◽  
Meta Analyses

Abstract. Currently, dedicated graphical displays to depict study-level statistical power in the context of meta-analysis are unavailable. Here, we introduce the sunset (power-enhanced) funnel plot to visualize this relevant information for assessing the credibility, or evidential value, of a set of studies. The sunset funnel plot highlights the statistical power of primary studies to detect an underlying true effect of interest in the well-known funnel display with color-coded power regions and a second power axis. This graphical display allows meta-analysts to incorporate power considerations into classic funnel plot assessments of small-study effects. Nominally significant, but low-powered, studies might be seen as less credible and as more likely being affected by selective reporting. We exemplify the application of the sunset funnel plot with two published meta-analyses from medicine and psychology. Software to create this variation of the funnel plot is provided via a tailored R function. In conclusion, the sunset (power-enhanced) funnel plot is a novel and useful graphical display to critically examine and to present study-level power in the context of meta-analysis.

How to Detect Publication Bias in Psychological Research

2019 ◽  
Vol 227 (4) ◽  
pp. 261-279 ◽  
Author(s):  
Frank Renkewitz ◽  
Melanie Keiner
Keyword(s):  
Publication Bias ◽  
Effect Size ◽  
Statistical Power ◽  
Type I Error ◽  
Psychological Research ◽  
Type I ◽  
True Effect Size ◽  
Questionable Research Practices ◽  
True Effect ◽  
Meta Analyses

Abstract. Publication biases and questionable research practices are assumed to be two of the main causes of low replication rates. Both of these problems lead to severely inflated effect size estimates in meta-analyses. Methodologists have proposed a number of statistical tools to detect such bias in meta-analytic results. We present an evaluation of the performance of six of these tools. To assess the Type I error rate and the statistical power of these methods, we simulated a large variety of literatures that differed with regard to true effect size, heterogeneity, number of available primary studies, and sample sizes of these primary studies; furthermore, simulated studies were subjected to different degrees of publication bias. Our results show that across all simulated conditions, no method consistently outperformed the others. Additionally, all methods performed poorly when true effect sizes were heterogeneous or primary studies had a small chance of being published, irrespective of their results. This suggests that in many actual meta-analyses in psychology, bias will remain undiscovered no matter which detection method is used.

Replication of the Superstition and Performance Study by

2014 ◽  
Vol 45 (3) ◽  
pp. 239-245 ◽  
Author(s):  
Robert J. Calin-Jageman ◽  
Tracy L. Caldwell
Keyword(s):  
Task Difficulty ◽  
Statistical Power ◽  
Meta Analysis ◽  
A Priori ◽  
Significant Heterogeneity ◽  
Performance Study ◽  
Improve Performance ◽  
Research Designs ◽  
Series Of Experiments ◽  
And Performance

A recent series of experiments suggests that fostering superstitions can substantially improve performance on a variety of motor and cognitive tasks ( Damisch, Stoberock, & Mussweiler, 2010 ). We conducted two high-powered and precise replications of one of these experiments, examining if telling participants they had a lucky golf ball could improve their performance on a 10-shot golf task relative to controls. We found that the effect of superstition on performance is elusive: Participants told they had a lucky ball performed almost identically to controls. Our failure to replicate the target study was not due to lack of impact, lack of statistical power, differences in task difficulty, nor differences in participant belief in luck. A meta-analysis indicates significant heterogeneity in the effect of superstition on performance. This could be due to an unknown moderator, but no effect was observed among the studies with the strongest research designs (e.g., high power, a priori sampling plan).

Power of Modified Brown-Forsythe and Mixed-Model Approaches in Split-Plot Designs

Methodology ◽  
2017 ◽  
Vol 13 (1) ◽  
pp. 9-22 ◽  
Author(s):  
Pablo Livacic-Rojas ◽  
Guillermo Vallejo ◽  
Paula Fernández ◽  
Ellián Tuero-Herrero
Keyword(s):  
Repeated Measures ◽  
Statistical Power ◽  
Mixed Model ◽  
Covariance Structure ◽  
Simulation Method ◽  
Future Research ◽  
Repeated Measures Design ◽  
Fixed And Random Effects ◽  
Split Plot ◽  
High Level

Abstract. Low precision of the inferences of data analyzed with univariate or multivariate models of the Analysis of Variance (ANOVA) in repeated-measures design is associated to the absence of normality distribution of data, nonspherical covariance structures and free variation of the variance and covariance, the lack of knowledge of the error structure underlying the data, and the wrong choice of covariance structure from different selectors. In this study, levels of statistical power presented the Modified Brown Forsythe (MBF) and two procedures with the Mixed-Model Approaches (the Akaike’s Criterion, the Correctly Identified Model [CIM]) are compared. The data were analyzed using Monte Carlo simulation method with the statistical package SAS 9.2, a split-plot design, and considering six manipulated variables. The results show that the procedures exhibit high statistical power levels for within and interactional effects, and moderate and low levels for the between-groups effects under the different conditions analyzed. For the latter, only the Modified Brown Forsythe shows high level of power mainly for groups with 30 cases and Unstructured (UN) and Autoregressive Heterogeneity (ARH) matrices. For this reason, we recommend using this procedure since it exhibits higher levels of power for all effects and does not require a matrix type that underlies the structure of the data. Future research needs to be done in order to compare the power with corrected selectors using single-level and multilevel designs for fixed and random effects.

The Limits of Ego Depletion

2019 ◽  
Vol 50 (5-6) ◽  
pp. 292-304 ◽  
Author(s):  
Mario Wenzel ◽  
Marina Lind ◽  
Zarah Rowland ◽  
Daniela Zahn ◽  
Thomas Kubiak
Keyword(s):  
Research Agenda ◽  
Statistical Power ◽  
Interindividual Variability ◽  
Ego Depletion ◽  
Crossover Design ◽  
Effect Sizes ◽  
Future Research ◽  
Future Research Agenda ◽  
Depletion Effect ◽  
Within Subjects

Abstract. Evidence on the existence of the ego depletion phenomena as well as the size of the effects and potential moderators and mediators are ambiguous. Building on a crossover design that enables superior statistical power within a single study, we investigated the robustness of the ego depletion effect between and within subjects and moderating and mediating influences of the ego depletion manipulation checks. Our results, based on a sample of 187 participants, demonstrated that (a) the between- and within-subject ego depletion effects only had negligible effect sizes and that there was (b) large interindividual variability that (c) could not be explained by differences in ego depletion manipulation checks. We discuss the implications of these results and outline a future research agenda.

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