From means to meaning in the study of sex/gender differences and similarities.

The incorporation of sex and gender (S/G) related factors is commonly acknowledged as a necessary step to advance towards more personalized diagnoses and treatments for somatic, psychiatric, and neurological diseases. Until now, most attempts to integrate S/G-related factors have been reduced to identifying average differences between females and males in behavioral/ biological variables. The present commentary questions this traditional approach by highlighting three main sets of limitations: 1) Issues stemming from the use of classic parametric methods to compare means; 2) challenges related to the ability of means to accurately represent the data within groups and differences between groups; 3) mean comparisons impose a results' binarization and a binary theoretical framework that precludes advancing towards precision medicine. Alternative methods free of these limitations are also discussed. We hope these arguments will contribute to reflecting on how research on S/G factors is conducted and could be improved.

Univariate and multivariate sex differences and similarities in gray matter volume within essential language-processing areas.

BACKGROUND: Sex differences in language-related abilities have been reported. It is generally assumed that these differences stem from a different organization of language in the brains of females and males. However, research in this area has been relatively scarce, methodologically heterogeneous and has yielded conflicting results. METHODS: Univariate and multivariate sex differences and similarities in gray matter volume (GMVOL) within 18 essential language-processing brain areas were assessed in a sex-balanced sample (N = 588) of right-handed young adults. Univariate analyses involved location, spread, and shape comparisons of the females' and males' distributions and were conducted with several robust statistical methods able to quantify the size of sex differences and similarities in a complementary way. Multivariate sex differences and similarities were estimated by the same methods in the continuous scores provided by two distinct multivariate procedures (logistic regression and a multivariate analog of the Wilcoxon-Mann-Whitney test). Additional analyses were addressed to compare the outcomes of these two multivariate analytical strategies and described their structure (that is, the relative contribution of each brain area to the multivariate effects). RESULTS: When not adjusted for total intracranial volume (TIV) variation, "large" univariate sex differences (males > females) were found in all 18 brain areas considered. In contrast, "small" differences (females > males) in just two of these brain areas were found when controlling for TIV. The two multivariate methods tested provided very similar results. Multivariate sex differences surpassed univariate differences, yielding "large" differences indicative of larger volumes in males when calculated from raw GMVOL estimates. Conversely, when calculated from TIV-adjusted GMVOL, multivariate differences were "medium" and indicative of larger volumes in females. Despite their distinct size and direction, multivariate sex differences in raw and TIV-adjusted GMVOL shared a similar structure and allowed us to identify the components of the SENT_CORE network which more likely contribute to the observed effects. CONCLUSIONS: Our results confirm and extend previous findings about univariate sex differences in language-processing areas, offering unprecedented evidence at the multivariate level. We also observed that the size and direction of these differences vary quite substantially depending on whether they are estimated from raw or TIV-adjusted GMVOL measurements.

While it is generally assumed that there is a distinct organization of language in the brains of females and males, studies investigating potential sex-based differences in language-related neural circuits have been characterized by their methodological heterogeneity and yielded inconclusive results. In this study, we explored how the brains of men and women differ in a well-defined network of brain areas essential for basic language functions. We found that there are indeed differences in the size of certain brain regions involved in language, with men and women showing varying patterns of these differences. Interestingly, the way these differences were observed depended on whether they are assessed at the whole network or at individual brain regions. Also, when considering the size of these brain regions in relation to overall cranial volume, the differences changed. So, this study highlights that understanding these brain differences requires considering different factors, like existing sex differences in cranial size, and looking at local effects but also their interactions and relationships in the broader context of functional brain networks.

Sedentary Behavior and Incident Dementia Among Older Adults.

Importance: Sedentary behavior is associated with cardiometabolic disease and mortality, but its association with dementia is unclear. Objective: To investigate whether accelerometer-assessed sedentary behavior is associated with incident dementia. Design, Setting, and Participants: A retrospective study of prospectively collected data from the UK Biobank including 49 841 adults aged 60 years or older without a diagnosis of dementia at the time of wearing the wrist accelerometer and living in England, Scotland, or Wales. Follow-up began at the time of wearing the accelerometer (February 2013 to December 2015) and continued until September 2021 in England, July 2021 in Scotland, and February 2018 in Wales. Exposures: Mean daily sedentary behavior time (included in the primary analysis) and mean daily sedentary bout length, maximum daily sedentary bout length, and mean number of daily sedentary bouts (included in the secondary analyses) were derived from a machine learning-based analysis of 1 week of wrist-worn accelerometer data. Main Outcome and Measures: Incident all-cause dementia diagnosis from inpatient hospital records and death registry data. Cox proportional hazard models with linear and cubic spline terms were used to assess associations. Results: A total of 49 841 older adults (mean age, 67.19 [SD, 4.29] years; 54.7% were female) were followed up for a mean of 6.72 years (SD, 0.95 years). During this time, 414 individuals were diagnosed with incident all-cause dementia. In the fully adjusted models, there was a significant nonlinear association between time spent in sedentary behavior and incident dementia. Relative to a median of 9.27 hours/d for sedentary behavior, the hazard ratios (HRs) for dementia were 1.08 (95% CI, 1.04-1.12, P < .001) for 10 hours/d, 1.63 (95% CI, 1.35-1.97, P < .001) for 12 hours/d, and 3.21 (95% CI, 2.05-5.04, P < .001) for 15 hours/d. The adjusted incidence rate of dementia per 1000 person-years was 7.49 (95% CI, 7.48-7.49) for 9.27 hours/d of sedentary behavior, 8.06 (95% CI, 7.76-8.36) for 10 hours/d, 12.00 (95% CI, 10.00-14.36) for 12 hours/d, and 22.74 (95% CI, 14.92-34.11) for 15 hours/d. Mean daily sedentary bout length (HR, 1.53 [95% CI, 1.03-2.27], P = .04 and 0.65 [95% CI, 0.04-1.57] more dementia cases per 1000 person-years for a 1-hour increase from the mean of 0.48 hours) and maximum daily sedentary bout length (HR, 1.15 [95% CI, 1.02-1.31], P = .02 and 0.19 [95% CI, 0.02-0.38] more dementia cases per 1000 person-years for a 1-hour increase from the mean of 1.95 hours) were significantly associated with higher risk of incident dementia. The number of sedentary bouts per day was not associated with higher risk of incident dementia (HR, 1.00 [95% CI, 0.99-1.01], P = .89). In the sensitivity analyses, after adjustment for time spent in sedentary behavior, the mean daily sedentary bout length and the maximum daily sedentary bout length were no longer significantly associated with incident dementia. Conclusions and Relevance: Among older adults, more time spent in sedentary behaviors was significantly associated with higher incidence of all-cause dementia. Future research is needed to determine whether the association between sedentary behavior and risk of dementia is causal.

An Updated Guide to Robust Statistical Methods in Neuroscience.

There is a vast array of new and improved methods for comparing groups and studying associations that offer the potential for substantially increasing power, providing improved control over the probability of false positives, and yielding a deeper and more nuanced understanding of data. These new techniques effectively deal with four insights into when and why conventional methods can be unsatisfactory. But for the non-statistician, this vast array of techniques for comparing groups and studying associations can seem daunting. This article briefly reviews when and why conventional methods can have relatively low power and yield misleading results. The main goal is to suggest guidelines regarding the use of modern techniques that improve upon classic approaches such as Pearson's correlation, ordinary linear regression, ANOVA, and ANCOVA. This updated version includes recent advances dealing with effect sizes, including situations where there is a covariate. The R code, figures, and accompanying notebooks have been updated as well. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.

Leisure-time sedentary behaviors are differentially associated with all-cause dementia regardless of engagement in physical activity.

Sedentary behavior (SB) is associated with cardiometabolic disease and mortality, but its association with dementia is currently unclear. This study investigates whether SB is associated with incident dementia regardless of engagement in physical activity (PA). A total of 146,651 participants from the UK Biobank who were 60 years or older and did not have a diagnosis of dementia (mean [SD] age: 64.59 [2.84] years) were included. Self-reported leisure-time SBs were divided into two domains: time spent watching television (TV) or time spent using a computer. A total of 3,507 individuals were diagnosed with all-cause dementia over a mean follow-up of 11.87 (±1.17) years. In models adjusted for a wide range of covariates, including time spent in PA, time spent watching TV was associated with increased risk of incident dementia (HR [95% CI] = 1.24 [1.15 to 1.32]) and time spent using a computer was associated with decreased risk of incident dementia (HR [95% CI] = 0.85 [0.81 to 0.90]). In joint associations with PA, TV time and computer time remained significantly associated with dementia risk at all PA levels. Reducing time spent in cognitively passive SB (i.e., TV time) and increasing time spent in cognitively active SB (i.e., computer time) may be effective behavioral modification targets for reducing risk of dementia regardless of engagement in PA.

Astigmatism analysis and reporting of surgically induced astigmatism and prediction error.

PURPOSE: To provide a method for determining the vector that, when added to the preoperative astigmatism, results in no prediction error (PE) and to specify statistical methods for evaluating astigmatism and determining the 95% confidence convex polygon. SETTING: Baylor College of Medicine, Houston, Texas, and University of Southern California, Los Angeles, California. DESIGN: Retrospective consecutive case series. METHODS: An analysis of 3 clinical trials involving toric intraocular lenses was performed. 3 formulas were evaluated (generic vergence formula with zero surgically induced astigmatism, the Barrett toric formula, and the Holladay toric formula). Scalar and vector analyses were performed on each dataset with each formula and the results compared. Since the PE was not a Gaussian distribution, a 95% convex polygon was used to determine the spread of the data. RESULTS: The mean values for the vector absolute astigmatism PEs were not different for the 3 formulas and 3 datasets. The Barrett and Holladay toric calculators were statistically superior to the zero formula for 3 intervals (0.75, 1.0, and 1.25) in the high astigmatism dataset. CONCLUSIONS: Residual astigmatism and vector absolute astigmatism PE mean values and SDs are useful but require extremely large datasets to demonstrate a statistical difference, whereas examining percentages in 0.25 diopters (D) steps from 0.25 to 2.0 D reveals differences with far fewer cases using the McNemar test for a P value. Double-angle plots are especially useful to visualize astigmatic vector PEs, and a 95% confidence convex polygon should be used when distributions are not Gaussian.

Association of Physical Activity with Incidence of Dementia Is Attenuated by Air Pollution.

INTRODUCTION: Physical activity (PA) is recognized as one of the key lifestyle behaviors that reduces risk of developing dementia late in life. However, PA also leads to increased respiration, and in areas with high levels of air pollution, PA may increase exposure to pollutants linked with higher risk of developing dementia. Here, we investigate whether air pollution attenuates the association between PA and dementia risk. METHODS: This prospective cohort study included 35,562 adults 60 yrs and older from the UK Biobank. Average acceleration magnitude (ACCave) from wrist-worn accelerometers was used to assess PA levels. Air pollution levels (NO, NO2, PM10, PM2.5, PM2.5-10, and PM2.5 absorbance) were estimated with land use regression methods. Incident all-cause dementia was derived from inpatient hospital records and death registry data. RESULTS: In adjusted models, ACCave was associated with reduced risk of developing dementia (HR = 0.71, 95% confidence interval [CI] = 0.60-0.83), whereas air pollution variables were not associated with dementia risk. There were significant interactions between ACCave and PM2.5 (HRinteraction = 1.33, 95% CI = 1.13-1.57) and PM2.5 absorbance (HRinteraction = 1.24, 95% CI = 1.07-1.45) on incident dementia. At the lowest tertiles of pollution, ACCave was associated with reduced risk of incident dementia (HRPM 2.5 = 0.66, 95% CI = 0.49-0.91; HRPM 2.5 absorbance = 0.60, 95% CI = 0.44-0.81). At the highest tertiles of these pollutants, there was no significant association of ACCave with incident dementia (HRPM 2.5 = 0.88, 95% CI = 0.68-1.14; HRPM 2.5 absorbance = 0.79, 95% CI = 0.60-1.04). CONCLUSIONS: PA is associated with reduced risk of developing all-cause dementia. However, exposure to even moderate levels of air pollution attenuates the benefits of PA on risk of dementia.

Two-way ANOVA: Inferences about interactions based on robust measures of effect size.

Consider a two-way ANOVA design. Generally, interactions are characterized by the difference between two measures of effect size. Typically the measure of effect size is based on the difference between measures of location, with the difference between means being the most common choice. This paper deals with extending extant results to two robust, heteroscedastic measures of effect size. The first is a robust, heteroscedastic analogue of Cohen's d. The second characterizes effect size in terms of the quantiles of the null distribution. Simulation results indicate that a percentile bootstrap method yields reasonably accurate confidence intervals. Data from an actual study are used to illustrate how these measures of effect size can add perspective when comparing groups.

Review and recommendations for univariate statistical analysis of spherical equivalent prediction error for IOL power calculations.

PURPOSE: To provide a reference for study design comparing intraocular lens (IOL) power calculation formulas, to show that the standard deviation (SD) of the prediction error (PE) is the single most accurate measure of outcomes, and to provide the most recent statistical methods to determine P values for type 1 errors. SETTING: Baylor College of Medicine, Houston, Texas, and University of Southern California, Los Angeles, California, USA. DESIGN: Retrospective consecutive case series. METHODS: Two datasets comprised of 5200 and 13 301 single eyes were used. The SDs of the PEs for 11 IOL power calculation formulas were calculated for each dataset. The probability density functions of signed and absolute PE were determined. RESULTS: None of the probability distributions for any formula in either dataset was normal (Gaussian). All the original signed PE distributions were not normal, but symmetric and leptokurtotic (heavy tailed) and had higher peaks than a normal distribution. The absolute distributions were asymmetric and skewed to the right. The heteroscedastic method was much better at controlling the probability of a type I error than older methods. CONCLUSIONS: (1) The criteria for patient and data inclusion were outlined; (2) the appropriate sample size was recommended; (3) the requirement that the formulas be optimized to bring the mean error to zero was reinforced; (4) why the SD is the single best parameter to characterize the performance of an IOL power calculation formula was demonstrated; and (5) and using the heteroscedastic statistical method was the preferred method of analysis was shown.

Inferences about which of J dependent groups has the largest robust measure of location.

Recently, a multiple comparisons procedure was derived with the goal of determining whether it is reasonable to make a decision about which of J independent groups has the largest robust measure of location. This was done by testing hypotheses aimed at comparing the group with the largest estimate to the remaining J - 1 groups. It was demonstrated that for the goal of controlling the familywise error rate, meaning the probability of one or more Type I errors, well-known improvements on the Bonferroni method can perform poorly. A technique for dealing with this issue was suggested and found to perform well in simulations. However, when dealing with dependent groups, the method is unsatisfactory. This note suggests an alternative method that is designed for dependent groups.

Altered Cortical Brain Structure and Increased Risk for Disease Seen Decades After Perinatal Exposure to Maternal Smoking: A Study of 9000 Adults in the UK Biobank.

Secondhand smoke exposure is a major public health risk that is especially harmful to the developing brain, but it is unclear if early exposure affects brain structure during middle age and older adulthood. Here we analyzed brain MRI data from the UK Biobank in a population-based sample of individuals (ages 44-80) who were exposed (n = 2510) or unexposed (n = 6079) to smoking around birth. We used robust statistical models, including quantile regressions, to test the effect of perinatal smoke exposure (PSE) on cortical surface area (SA), thickness, and subcortical volumes. We hypothesized that PSE would be associated with cortical disruption in primary sensory areas compared to unexposed (PSE-) adults. After adjusting for multiple comparisons, SA was significantly lower in the pericalcarine (PCAL), inferior parietal (IPL), and regions of the temporal and frontal cortex of PSE+ adults; these abnormalities were associated with increased risk for several diseases, including circulatory and endocrine conditions. Sensitivity analyses conducted in a hold-out group of healthy participants (exposed, n = 109, unexposed, n = 315) replicated the effect of PSE on SA in the PCAL and IPL. Collectively our results show a negative, long term effect of PSE on sensory cortices that may increase risk for disease later in life.

Generation of Linear Impulse During the Takeoff of the Long Jump.

This study investigates the effect of initial leg angle on horizontal jump performance. Eleven highly skilled male and female long jumpers (national and Olympic level) performed a series of horizontal jumps for distance. Within-jumper differences in initial leg angle, normalized horizontal and net vertical impulses, contact time, and average reaction force during the impact interval, post-impact interval, and in total were measured using highspeed video (240 or 300 Hz) and a force plate (1200 Hz). Pearson correlations, Winsorized correlations, and the HC4 method were used to determine significant correlations between variables (α = 0.05). Within-jumper analysis indicated that when jumpers initiate the takeoff phase with a larger leg angle they are able to generate significantly greater negative horizontal and positive net vertical impulse (n = 7). Increased impulse generation was the result of increased contact time (n = 5 of 7) and / or increased average reaction force (n = 4) during the impact interval (n = 3) and / or post-impact interval (n = 4), depending on the individual. Initial leg configuration at contact and individual specific impulse generation strategies are important to consider when determining how an athlete with initial momentum can increase impulse generation to jump for distance.

Robust regression: Testing global hypotheses about the slopes when there is multicollinearity or heteroscedasticity.

A well-known concern regarding the usual linear regression model is multicollinearity. As the strength of the association among the independent variables increases, the squared standard error of regression estimators tends to increase, which can seriously impact power. This paper examines heteroscedastic methods for dealing with this issue when testing the hypothesis that all of the slope parameters are equal to zero via a robust ridge estimator that guards against outliers among the dependent variable. Included are results related to leverage points, meaning outliers among the independent variables. In various situations, the proposed method increases power substantially.

A Guide to Robust Statistical Methods in Neuroscience.

There is a vast array of new and improved methods for comparing groups and studying associations that offer the potential for substantially increasing power, providing improved control over the probability of a Type I error, and yielding a deeper and more nuanced understanding of data. These new techniques effectively deal with four insights into when and why conventional methods can be unsatisfactory. But for the non-statistician, the vast array of new and improved techniques for comparing groups and studying associations can seem daunting, simply because there are so many new methods that are now available. This unit briefly reviews when and why conventional methods can have relatively low power and yield misleading results. The main goal is to suggest some general guidelines regarding when, how, and why certain modern techniques might be used. © 2018 by John Wiley & Sons, Inc.

Robust statistical methods: A primer for clinical psychology and experimental psychopathology researchers.

This paper reviews and offers tutorials on robust statistical methods relevant to clinical and experimental psychopathology researchers. We review the assumptions of one of the most commonly applied models in this journal (the general linear model, GLM) and the effects of violating them. We then present evidence that psychological data are more likely than not to violate these assumptions. Next, we overview some methods for correcting for violations of model assumptions. The final part of the paper presents 8 tutorials of robust statistical methods using R that cover a range of variants of the GLM (t-tests, ANOVA, multiple regression, multilevel models, latent growth models). We conclude with recommendations that set the expectations for what methods researchers submitting to the journal should apply and what they should report.

Beyond differences in means: robust graphical methods to compare two groups in neuroscience.

If many changes are necessary to improve the quality of neuroscience research, one relatively simple step could have great pay-offs: to promote the adoption of detailed graphical methods, combined with robust inferential statistics. Here, we illustrate how such methods can lead to a much more detailed understanding of group differences than bar graphs and t-tests on means. To complement the neuroscientist's toolbox, we present two powerful tools that can help us understand how groups of observations differ: the shift function and the difference asymmetry function. These tools can be combined with detailed visualisations to provide complementary perspectives about the data. We provide implementations in R and MATLAB of the graphical tools, and all the examples in the article can be reproduced using R scripts.