effect sizes
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2022 ◽  
Vol 40 (3) ◽  
pp. 1-47
Author(s):  
Ameer Albahem ◽  
Damiano Spina ◽  
Falk Scholer ◽  
Lawrence Cavedon

In many search scenarios, such as exploratory, comparative, or survey-oriented search, users interact with dynamic search systems to satisfy multi-aspect information needs. These systems utilize different dynamic approaches that exploit various user feedback granularity types. Although studies have provided insights about the role of many components of these systems, they used black-box and isolated experimental setups. Therefore, the effects of these components or their interactions are still not well understood. We address this by following a methodology based on Analysis of Variance (ANOVA). We built a Grid Of Points that consists of systems based on different ways to instantiate three components: initial rankers, dynamic rerankers, and user feedback granularity. Using evaluation scores based on the TREC Dynamic Domain collections, we built several ANOVA models to estimate the effects. We found that (i) although all components significantly affect search effectiveness, the initial ranker has the largest effective size, (ii) the effect sizes of these components vary based on the length of the search session and the used effectiveness metric, and (iii) initial rankers and dynamic rerankers have more prominent effects than user feedback granularity. To improve effectiveness, we recommend improving the quality of initial rankers and dynamic rerankers. This does not require eliciting detailed user feedback, which might be expensive or invasive.


2022 ◽  
Author(s):  
Joanna von Berg ◽  
Michelle ten Dam ◽  
Sander W. van der Laan ◽  
Jeroen de Ridder

Pleiotropic SNPs are associated with multiple traits. Such SNPs can help pinpoint biological processes with an effect on multiple traits or point to a shared etiology between traits. We present PolarMorphism, a new method for the identification of pleiotropic SNPs from GWAS summary statistics. PolarMorphism can be readily applied to more than two traits or whole trait domains. PolarMorphism makes use of the fact that trait-specific SNP effect sizes can be seen as Cartesian coordinates and can thus be converted to polar coordinates r (distance from the origin) and theta (angle with the Cartesian x-axis). r describes the overall effect of a SNP, while theta describes the extent to which a SNP is shared. r and theta are used to determine the significance of SNP sharedness, resulting in a p-value per SNP that can be used for further analysis. We apply PolarMorphism to a large collection of publicly available GWAS summary statistics enabling the construction of a pleiotropy network that shows the extent to which traits share SNPs. This network shows how PolarMorphism can be used to gain insight into relationships between traits and trait domains. Furthermore, pathway analysis of the newly discovered pleiotropic SNPs demonstrates that analysis of more than two traits simultaneously yields more biologically relevant results than the combined results of pairwise analysis of the same traits. Finally, we show that PolarMorphism is more efficient and more powerful than previously published methods.


eLife ◽  
2022 ◽  
Vol 11 ◽  
Author(s):  
Daniel W Belsky ◽  
Avshalom Caspi ◽  
David L Corcoran ◽  
Karen Sugden ◽  
Richie Poulton ◽  
...  

Background: Measures to quantify changes in the pace of biological aging in response to intervention are needed to evaluate geroprotective interventions for humans. Previously we showed that quantification of the pace of biological aging from a DNA-methylation blood test was possible (Belsky et al. 2020). Here we report a next-generation DNA-methylation biomarker of Pace of Aging, DunedinPACE (for Pace of Aging Calculated from the Epigenome).Methods: We used data from the Dunedin Study 1972-3 birth cohort tracking within-individual decline in 19 indicators of organ-system integrity across four time points spanning two decades to model Pace of Aging. We distilled this two-decade Pace of Aging into a single-time-point DNA-methylation blood-test using elastic-net regression and a DNA-methylation dataset restricted to exclude probes with low test-retest reliability. We evaluated the resulting measure, named DunedinPACE, in five additional datasets.Results: DunedinPACE showed high test-retest reliability, was associated with morbidity, disability, and mortality, and indicated faster aging in young adults with childhood adversity. DunedinPACE effect-sizes were similar to GrimAge Clock effect-sizes. In analysis of incident morbidity, disability, and mortality, DunedinPACE and added incremental prediction beyond GrimAge.Conclusions: DunedinPACE is a novel blood biomarker of the pace of aging for gerontology and geroscience.Funding: This research was supported by US-National Institute on Aging grants AG032282, AG061378, AG066887, and UK Medical Research Council grant MR/P005918/1.


2022 ◽  
Author(s):  
Marco Del Giudice ◽  
Steven Gangestad

Harrison et al. (2021) set out to test the greater male variability hypothesis with respect to personality in non-human animals. Based on the non-significant results of their meta-analysis, they concluded that there is no evidence to support the hypothesis, and that biological explanations for greater male variability in human psychological traits should be called into question. Here, we show that these conclusions are unwarranted. Specifically: (a) in mammals, birds, and reptiles/amphibians, the magnitude of the sex differences in variability found in the meta-analysis is entirely in line with previous findings from both humans and non-human animals; (b) the generalized lack of statistical significance does not imply that effect sizes were too small to be considered meaningful, as the study was severely underpowered to detect effect sizes in the plausible range; (c) the results of the meta-analysis can be expected to underestimate the true magnitude of sex differences in the variability of personality, because the behavioral measures employed in most of the original studies contain large amounts of measurement error; and (d) variability effect sizes based on personality scores, latencies, and proportions suffer from lac of statistical validity, adding even more noise to the meta-analysis. In total, Harrison et al.’s study does nothing to disprove the greater male variability hypothesis in mammals, let alone in humans. To the extent that they are valid, the data remain compatible with a wide range of plausible scenarios.


2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Shintaro Sukegawa ◽  
Tamamo Matsuyama ◽  
Futa Tanaka ◽  
Takeshi Hara ◽  
Kazumasa Yoshii ◽  
...  

AbstractPell and Gregory, and Winter’s classifications are frequently implemented to classify the mandibular third molars and are crucial for safe tooth extraction. This study aimed to evaluate the classification accuracy of convolutional neural network (CNN) deep learning models using cropped panoramic radiographs based on these classifications. We compared the diagnostic accuracy of single-task and multi-task learning after labeling 1330 images of mandibular third molars from digital radiographs taken at the Department of Oral and Maxillofacial Surgery at a general hospital (2014–2021). The mandibular third molar classifications were analyzed using a VGG 16 model of a CNN. We statistically evaluated performance metrics [accuracy, precision, recall, F1 score, and area under the curve (AUC)] for each prediction. We found that single-task learning was superior to multi-task learning (all p < 0.05) for all metrics, with large effect sizes and low p-values. Recall and F1 scores for position classification showed medium effect sizes in single and multi-task learning. To our knowledge, this is the first deep learning study to examine single-task and multi-task learning for the classification of mandibular third molars. Our results demonstrated the efficacy of implementing Pell and Gregory, and Winter’s classifications for specific respective tasks.


2022 ◽  
Author(s):  
Katherine L. Winters ◽  
Javier Jasso ◽  
James E Pustejovsky ◽  
Courtney Byrd

Purpose: Speech-language pathologists (SLPs) typically examine narrative performance when completing a comprehensive language assessment. However, there is significant variability in the methodologies used to evaluate narration. The primary aims of this systematic review and meta-analysis were to a) investigate how narrative assessment type (e.g., macrostructure, microstructure, internal state language) differentiates typically developing (TD) children from children with developmental language disorder (DLD), or, TD–DLD group differences, b) identify specific narrative assessment measures (e.g., number of different words) that result in greater TD–DLD differences, and, c) evaluate participant and sample characteristics (e.g., DLD inclusionary criteria) that may uniquely influence performance differences. Method: Three electronic databases (PsychInfo, ERIC, and PubMed) and ASHAWire were searched on July 30, 2019 to locate studies that reported oral narrative language measures for both DLD and TD groups between ages 4 and 12 years; studies focusing on written narration or other developmental disorders only were excluded. Thirty-seven primary studies were identified via a three-step study selection procedure. We extracted data related to the sample participants, the narrative task(s) and assessment measures, and research design. Standardized mean differences using a bias-corrected Hedges’ g were the calculated effect sizes (N = 382). Research questions were analyzed using mixed-effects meta-regression with robust variance estimation to account for effect size dependencies. Results: Searches identified eligible studies published between 1987 and 2019. An overall meta-analysis using 382 effect sizes obtained across 37 studies showed that children with DLD had decreased narrative performance relative to TD peers, with summary estimates ranging from -0.850, 95% CI [-1.016, -0.685] to -0.794, 95% CI [-0.963, -0.624], depending on the correlation assumed. Across all models, effect size estimates showed significant heterogeneity both between and within studies, even after accounting for effect size-, sample-, and study-level predictors. Grammatical accuracy (microstructure) and story grammar (macrostructure) yielded the most consistent evidence of significant TD–DLD group differences across statistical models.Conclusions: Present findings suggest some narrative assessment measures may yield significantly different performance between children with and without DLD. However, researchers need to be consistent in their inclusionary criteria, their description of sample characteristics, and in their reporting of the correlations of measures, in order to determine which assessment measures are more likely to yield group differences.


2022 ◽  
Vol 8 (1) ◽  
Author(s):  
John W. D. Lea ◽  
Jamie M. O’Driscoll ◽  
Sabina Hulbert ◽  
James Scales ◽  
Jonathan D. Wiles

Abstract Background The validity of ratings of perceived exertion (RPE) during aerobic training is well established; however, its validity during resistance exercise is less clear. This meta-analysis used the known relationships between RPE and exercise intensity (EI), heart rate (HR), blood lactate (BLa), blood pressure (BP) and electromyography (EMG) to determine the convergent validity of RPE as a measure of resistance exercise intensity and physiological exertion, during different forms of resistance exercise. Additionally, this study aims to assess the effect of several moderator variables on the strength of the validity coefficients, so that clearer guidance can be given on the use of RPE during resistance exercise. Methods An online search of 4 databases and websites (PubMed, Web of Science SPORTDiscus and ResearchGate) was conducted up to 28 February 2020. Additionally, the reference lists of the included articles were inspected manually for further unidentified studies. The inclusion criteria were healthy participants of any age, a rating scale used to measure RPE, resistance exercise of any type, one cohort receiving no other intervention, and must present data from one of the following outcome measures: EI, HR, BP, EMG or BLa. Weighted mean effect sizes (r) were calculated using a random-effects model. Heterogeneity was assessed using the τ2 and I2 statistics. Moderator analysis was conducted using random-effects meta-regression. Results One-hundred and eighteen studies were included in the qualitative synthesis, with 75 studies (99 unique cohorts) included in the meta-analysis. The overall weighted mean validity coefficient was large (0.88; 95% CI 0.84–0.91) and between studies heterogeneity was very large (τ2 = 0.526, I2 = 96.1%). Studies using greater workload ranges, isometric muscle actions, and those that manipulated workload or repetition time, showed the highest validity coefficients. Conversely, sex, age, training status, RPE scale used, and outcome measure no significant effect. Conclusions RPE provides a valid measure of exercise intensity and physiological exertion during resistance exercise, with effect sizes comparable to or greater than those shown during aerobic exercise. Therefore, RPE may provide an easily accessible means of prescribing and monitoring resistance exercise training. Trial Registration The systematic review protocol was registered on the PROSPERO database (CRD42018102640).


2022 ◽  
Author(s):  
Timo Gnambs ◽  
Ulrich Schroeders

Meta-analyses of treatment effects in randomized control trials are often faced with the problem of missing information required to calculate effect sizes and their sampling variances. Particularly, correlations between pre- and posttest scores are frequently not available. As an ad-hoc solution, researchers impute a constant value for the missing correlation. As an alternative, we propose adopting a multivariate meta-regression approach that models independent group effect sizes and accounts for the dependency structure using robust variance estimation or three-level modeling. A comprehensive simulation study mimicking realistic conditions of meta-analyses in clinical and educational psychology suggested that the prevalent imputation approach works well for estimating the pooled effect but severely distorts the between-study heterogeneity. In contrast, the robust meta-regression approach resulted in largely unbiased fixed and random effects. Based on these results recommendations for meta-analytic practice and future meta-analytic developments are provided.


Assessment ◽  
2022 ◽  
pp. 107319112110675
Author(s):  
Maria Aparcero ◽  
Emilie H. Picard ◽  
Alicia Nijdam-Jones ◽  
Barry Rosenfeld

Several meta-analyses of the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) and Minnesota Multiphasic Personality Inventory-2 Restructured Form (MMPI-2-RF) have examined these instruments’ ability to detect symptom exaggeration or feigning. However, limited research has directly compared whether the scales across these two instruments are equally effective. This study used a moderated meta-analysis to compare 109 MMPI-2 and 41 MMPI-2-RF feigning studies, 83 (56.46%) of which were not included in previous meta-analyses. Although there were differences between the two test versions, with most MMPI-2 validity scales generating larger effect sizes than the corresponding MMPI-2-RF scales, these differences were not significant after controlling for study design and type of symptoms being feigned. Additional analyses showed that the F and Fp-r scales generated the largest effect sizes in identifying feigned psychiatric symptoms, while the FBS and RBS were better at detecting exaggerated medical symptoms. The findings indicate that the MMPI-2 validity scales and their MMPI-2-RF counterparts were similarly effective in differentiating genuine responders from those exaggerating or feigning psychiatric and medical symptoms. These results provide reassurance for the use of both the MMPI-2 and MMPI-2-RF in settings where symptom exaggeration or feigning is likely. Findings are discussed in the context of the recently released MMPI-3.


2022 ◽  
Author(s):  
Mikkel Helding Vembye ◽  
James E Pustejovsky ◽  
Terri Pigott

Meta-analytic models for dependent effect sizes have grown increasingly sophisticated over the last few decades, which has created challenges for a priori power calculations. We introduce power approximations for tests of average effect sizes based upon the most common models for handling dependent effect sizes. In a Monte Carlo simulation, we show that the new power formulas can accurately approximate the true power of common meta-analytic models for dependent effect sizes. Lastly, we investigate the Type I error rate and power for several common models, finding that tests using robust variance estimation provide better Type I error calibration than tests with model-based variance estimation. We consider implications for practice with respect to selecting a working model and an inferential approach.


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