scholarly journals Statistical agnostic mapping: a framework in neuroimaging based on concentration inequalities

2019 ◽  
Author(s):  
J.M. Gorriz ◽  
◽  
◽  

ABSTRACTIn the 70s a novel branch of statistics emerged focusing its effort in selecting a function in the pattern recognition problem, which fulfils a definite relationship between the quality of the approximation and its complexity. These data-driven approaches are mainly devoted to problems of estimating dependencies with limited sample sizes and comprise all the empirical out-of sample generalization approaches, e.g. cross validation (CV) approaches. Although the latter are not designed for testing competing hypothesis or comparing different models in neuroimaging, there are a number of theoretical developments within this theory which could be employed to derive a Statistical Agnostic (non-parametric) Mapping (SAM) at voxel or multi-voxel level. Moreover, SAMs could relieve i) the problem of instability in limited sample sizes when estimating the actual risk via the CV approaches, e.g. large error bars, and provide ii) an alternative way of Family-wise-error (FWE) corrected p-value maps in inferential statistics for hypothesis testing. In this sense, we propose a novel framework in neuroimaging based on concentration inequalities, which results in (i) a rigorous development for model validation with a small sample/dimension ratio, and (ii) a less-conservative procedure than FWE p-value correction, to determine the brain significance maps from the inferences made using small upper bounds of the actual risk.

Author(s):  
Kathryn Rayson ◽  
Louise Waddington ◽  
Dougal Julian Hare

Abstract Background: Cognitive behavioural therapy (CBT) is in high demand due to its strong evidence base and cost effectiveness. To ensure CBT is delivered as intended in research, training and practice, fidelity assessment is needed. Fidelity is commonly measured by assessors rating treatment sessions, using CBT competence scales (CCSs). Aims: The current review assessed the quality of the literature examining the measurement properties of CCSs and makes recommendations for future research, training and practice. Method: Medline, PsychINFO, Scopus and Web of Science databases were systematically searched to identify relevant peer-reviewed, English language studies from 1980 onwards. Relevant studies were those that were primarily examining the measurement properties of CCSs used to assess adult 1:1 CBT treatment sessions. The quality of studies was assessed using a novel tool created for this study, following which a narrative synthesis is presented. Results: Ten studies met inclusion criteria, most of which were assessed as being ‘fair’ methodological quality, primarily due to small sample sizes. Construct validity and responsiveness definitions were applied inconsistently in the studies, leading to confusion over what was being measured. Conclusions: Although CCSs are widely used, we need to pay careful attention to the quality of research exploring their measurement properties. Consistent definitions of measurement properties, consensus about adequate sample sizes and improved reporting of individual properties are required to ensure the quality of future research.


Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 2885-2885
Author(s):  
Jenny N Poynter ◽  
Michaela Richardson ◽  
Erica Langer ◽  
Anthony Hooten ◽  
Michelle A. Roesler ◽  
...  

Abstract Background Polymorphisms in mitochondrial DNA can be used to group individuals into haplogroups that reflect human global migration. These mitochondrial variants are associated with differences in mitochondrial function and have been associated with multiple diseases, including cancer. In this analysis, we evaluated the association between mtDNA haplogroup and risk of myelodysplastic syndromes (MDS). Methods Cases were identified by rapid case ascertainment through the population-based Minnesota Cancer Surveillance System (MCSS). Participants were recruited to the MDS study if they were diagnosed with MDS between April 1, 2010 and October 31, 2014. Eligibility criteria included residence in Minnesota, age at diagnosis between 20 and 85 years, and ability to understand English or Spanish. Centralized pathology and cytogenetics review were conducted to confirm diagnosis and classify by subtypes. Controls were identified through the Minnesota State driver's license/identification card list. Genomic DNA from cases and controls was collected using Oragene DNA collection kits (DNA Genotek, Ontario, Canada) and extracted via Autopure LS Instrument according to manufacturer's instructions (Qiagen). We genotyped 15 mtSNPs that capture common European mitochondrial haplogroup variation (Mitchell et al Hum Genet 2014; Raby et al J Allergy Clin Immunol 2007) on the Sequenom iPLEX Gold MassArray platform (Sequenom, Inc., San Diego, CA) in the University of Minnesota Genomics Core. Because haplogroup frequencies vary by race and ethnicity, we restricted analyses to non-Hispanic white cases and controls. All statistical analyses were conducted using SAS v.9.3 (SAS Institute, Cary, NC). Odds ratios (OR) and 95% confidence intervals (CI) were calculated. We also evaluated associations by MDS subtype and IPSS-R risk category. Results We were able to classify 215 cases with confirmed MDS and 522 controls into one of the 11 common European haplogroups. The distribution of haplogroups in our control sample was similar to the distribution reported in a previous sample of non-Hispanic white individuals from the United States (Mitchell et al Hum Genet 2014), with the highest number in the H haplogroup (42%). Due to small sample sizes in some subgroups, we combined mt haplogroups into larger bins based on the haplogroup evolutionary tree, including HV (H+V), JT (J+T), IWX (I+W+X), UK (U+K), and Z (van Oven & Kayser Hum Mut 2009) for comparisons of cases and controls. Using haplogroup HV as the reference group, we found a statistically significant association between haplogroup JT and MDS (OR=0.57, 95% CI 0.36, 0.90, p=0.02). No other significant associations were observed in a comparison of cases and controls (Figure). In the analysis stratified by MDS subtype, the association with haplogroup JT reached statistical significance only in MDS cases with the RCMD subtype (OR=0.42, 95% CI 0.18, 0.97), although the association was similar in magnitude for RARS and the p-value for heterogeneity was non-significant (0.76). Similarly, the associations between haplogroup JT and MDS were similar in the analysis stratified by IPSS-R risk category (p-value for heterogeneity = 0.71). Conclusions In this population-based study of MDS, we observed an association between mtDNA haplogroup JT and risk of MDS. Previous studies using cybrid cells have reported functional differences by mtDNA haplogroup and provide biological plausibility for the observed association, including higher capacity to cope with oxidative stress in haplogroup T (Meuller et al PLoS One 2012) and lower levels of ATP and reactive oxygen species production in haplogroup J (Kenney et al PLoS One 2013). Further studies of the relationship between mtDNA variation and MDS are warranted in larger sample sizes. Figure 1. Association between mtDNA haplogroup and MDS Figure 1. Association between mtDNA haplogroup and MDS Disclosures No relevant conflicts of interest to declare.


2016 ◽  
Author(s):  
Stevie C. Y. Yap ◽  
Jessica Wortman ◽  
Ivana Anusic ◽  
S. Glenn Baker ◽  
Laura Danielle Scherer ◽  
...  

Life satisfaction judgments are thought to represent an overall evaluationof the quality of a person’s life as a whole. Thus, they should reflectrelatively important and stable characteristics of that person’s life.Previous highly cited research has suggested that transient factors, suchas the mood that a person experiences at the time that well-being judgmentsare made, can influence these judgments. However, most existing studiesused small sample sizes, and few replications have been attempted. Ninedirect and conceptual replications of past studies testing the effects ofmood on life satisfaction judgments were conducted using sample sizes thatwere considerably larger than previous studies (Ns = 202, 200, 269, 118,320, 401, 285, 129, 122). Most of the nine studies resulted innonsignificant effects on life satisfaction and happiness judgments, andthose that were significant were substantially smaller than effects foundin previous research.______________________________________


Author(s):  
Jens Nußberger ◽  
Frederic Boesel ◽  
Stefan Lenz ◽  
Harald Binder ◽  
Moritz Hess

AbstractDeep generative models can be trained to represent the joint distribution of data, such as measurements of single nucleotide polymorphisms (SNPs) from several individuals. Subsequently, synthetic observations are obtained by drawing from this distribution. This has been shown to be useful for several tasks, such as removal of noise, imputation, for better understanding underlying patterns, or even exchanging data under privacy constraints. Yet, it is still unclear how well these approaches work with limited sample size. We investigate such settings specifically for binary data, e.g., as relevant when considering SNP measurements, and evaluate three frequently employed generative modeling approaches, variational autoencoders (VAEs), deep Boltzmann machines (DBMs) and generative adversarial networks (GANs). This includes conditional approaches, such as when considering gene expression conditional on SNPs. Recovery of pair-wise odds ratios is considered as a primary performance criterion. For simulated as well as real SNP data, we observe that DBMs generally can recover structure for up to 100 variables with as little as 500 observations, with a tendency of over-estimating odds ratios when not carefully tuned. VAEs generally get the direction and relative strength of pairwise relations right, yet with considerable under-estimation of odds ratios. GANs provide stable results only with larger sample sizes and strong pair-wise relations in the data. Taken together, DBMs and VAEs (in contrast to GANs) appear to be well suited for binary omics data, even at rather small sample sizes. This opens the way for many potential applications where synthetic observations from omics data might be useful.


2005 ◽  
Vol 28 (3) ◽  
pp. 283-294 ◽  
Author(s):  
Jin-Shei Lai ◽  
Jeanne Teresi ◽  
Richard Gershon

An item with differential item functioning (DIF) displays different statistical properties, conditional on a matching variable. The presence of DIF in measures can invalidate the conclusions of medical outcome studies. Numerous approaches have been developed to examine DIF in many areas, including education and health-related quality of life. There is little consensus in the research community regarding selection of one best method, and most methods require large sample sizes. This article describes some approaches to examine DIF with small samples (e.g., less than 200).


10.2196/15981 ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. e15981 ◽  
Author(s):  
Jack Parker ◽  
Lauren Powell ◽  
Susan Mawson

Background With advances in technology, the adoption of wearable devices has become a viable adjunct in poststroke rehabilitation. Upper limb (UL) impairment affects up to 77% of stroke survivors impacting on their ability to carry out everyday activities. However, despite an increase in research exploring these devices for UL rehabilitation, little is known of their effectiveness. Objective This review aimed to assess the effectiveness of UL wearable technology for improving activity and participation in adult stroke survivors. Methods Randomized controlled trials (RCTs) and randomized comparable trials of UL wearable technology for poststroke rehabilitation were included. Primary outcome measures were validated measures of activity and participation as defined by the International Classification of Functioning, Disability, and Health. Databases searched were MEDLINE, Web of Science (Core collection), CINAHL, and the Cochrane Library. The Cochrane Risk of Bias Tool was used to assess the methodological quality of the RCTs and the Downs and Black Instrument for the quality of non RCTs. Results In the review, we included 11 studies with collectively 354 participants at baseline and 323 participants at final follow-up including control groups and participants poststroke. Participants’ stroke type and severity varied. Only 1 study found significant between-group differences for systems functioning and activity (P≤.02). The 11 included studies in this review had small sample sizes ranging from 5 to 99 participants at an average (mean) age of 57 years. Conclusions This review has highlighted a number of reasons for insignificant findings in this area including low sample sizes and the appropriateness of the methodology for complex interventions. However, technology has the potential to measure outcomes, provide feedback, and engage users outside of clinical sessions. This could provide a platform for motivating stroke survivors to carry out more rehabilitation in the absence of a therapist, which could maximize recovery. Trial Registration PROSPERO CRD42017057715; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=57715


2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Elizabeth Collins ◽  
Roger Watt

Statistical power is key to planning studies if understood and used correctly. Power is the probability of obtaining a statistically significant p-value, given a set alpha, sample size, and population effect size. The literature suggests that psychology studies are underpowered due to small sample sizes, and that researchers do not hold accurate intuitions about sensible sample sizes and associated levels of power. In this study, we surveyed 214 psychological researchers, and asked them about their experiences of using a priori power analysis, effect size estimation methods, post hoc power, and their understanding of what the term “power” actually means. Power analysis use was high, although participants reported difficulties with complex research designs, and effect size estimation. Participants also typically could not accurately define power. If psychological researchers are expected to compute a priori power analyses to plan their research, clearer educational material and guidelines should be made available.


2009 ◽  
Vol 59 (6) ◽  
Author(s):  
Júlia Volaufová

AbstractSeemingly, testing for fixed effects in linear models with variance-covariance components has been solved for decades. However, even in simple situations such as in fixed one-way model with heteroscedastic variances (a multiple means case of the Behrens-Fisher problem) the questions of statistical properties of various approximations of test statistics are still alive. Here we present a brief overview of several approaches suggested in the literature as well as those available in statistical software, accompanied by a simulation study in which the accuracy of p-values is studied. Our interest is limited here to the Welch’s test, the Satterthwaite-Fai-Cornelius test, the Kenward-Roger test, the simple ANOVA F-test, and the parametric bootstrap test. We conclude that for small sample sizes, regardless the number of compared means and the heterogeneity of variance, the ANOVA F-test p-value performs the best. For higher sample sizes (at least 5 per group), the parametric bootstrap performs well, and the Kenward-Roger test also performs well.


2018 ◽  
Vol 11 (1) ◽  
pp. 3-23 ◽  
Author(s):  
Hannes Lepschy ◽  
Hagen Wäsche ◽  
Alexander Woll

Background:Despite the popularity of football, the analysis of success factors in football remains a challenge. While reviews on performance indicators in football are available, none focuses solely on the identification of success factors and addresses the large and growing body of recent research up until 2016.Objective:To find out what determines success in football and to organize the body of literature, a systematic literature review analyzing existing studies with regard to success factors in football was undertaken.Methods:The studies included in this review had to deal with performance indicators related to success in football. The studies were published in 2016 or before. The initial search revealed 19,161 articles. Finally, sixty-eight articles were included in this review. The studies were clustered with regard to comparative analyses, predictive analyses and analyses of home advantage.Results:In total, 76 different variables were investigated in the reviewed papers. It appeared that the most significant variables are efficiency (number of goals divided by the number of shots), shots on goal, ball possession, pass accuracy/successful passes as well as the quality of opponent and match location. Moreover, new statistical methods were used to reveal interactions among these variables such as discriminant analysis, factor analysis and regression analysis. The studies showed methodological deficits such as clear operational definitions of investigated variables and small sample sizes.Conclusion:The review allows a comprehensive identification of critical success factors in football and sheds light on utilized methodological approaches. Future research should consider precise operational definitions of the investigated variables, adequate sample sizes and the involvement of situational variables as well as their interaction.


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