A comprehensive investigation of statistical and machine learning approaches for predicting complex human diseases on genomic variants.

Quantifying an individual's risk for common diseases is an important goal of precision health. The polygenic risk score (PRS), which aggregates multiple risk alleles of candidate diseases, has emerged as a standard approach for identifying high-risk individuals. Although several studies have been performed to benchmark the PRS calculation tools and assess their potential to guide future clinical applications, some issues remain to be further investigated, such as lacking (i) various simulated data with different genetic effects; (ii) evaluation of machine learning models and (iii) evaluation on multiple ancestries studies. In this study, we systematically validated and compared 13 statistical methods, 5 machine learning models and 2 ensemble models using simulated data with additive and genetic interaction models, 22 common diseases with internal training sets, 4 common diseases with external summary statistics and 3 common diseases for trans-ancestry studies in UK Biobank. The statistical methods were better in simulated data from additive models and machine learning models have edges for data that include genetic interactions. Ensemble models are generally the best choice by integrating various statistical methods. LDpred2 outperformed the other standalone tools, whereas PRS-CS, lassosum and DBSLMM showed comparable performance. We also identified that disease heritability strongly affected the predictive performance of all methods. Both the number and effect sizes of risk SNPs are important; and sample size strongly influences the performance of all methods. For the trans-ancestry studies, we found that the performance of most methods became worse when training and testing sets were from different populations.

Is the assessment of asthma treatment efficacy sufficiently comprehensive?

The goal of asthma guideline therapy is to achieve disease control, by minimizing impairment and decreasing the risk of exacerbations and adverse effects of the disease and its treatment. The primary objective of most clinical trials of biologics for severe asthma is a reduction in exacerbation rate. Recently, studies with patients at the lower guideline steps have also selected exacerbation reduction as a primary objective. These trials in patients with milder disease frequently demonstrate statistically significantly fewer exacerbations, but their power calculations reflect larger sample size and smaller effect size. Exacerbations have a precise consensus definition, although a minimal clinically important difference has not been established. Reduction of exacerbations in severe asthma is commonly 10-fold greater than in mild disease. Further, reduction in exacerbations is not always associated with reduced impairment. If superior control is the objective, both domains should demonstrate consistent and parallel improvement. The disconnect may reflect the need for alternative tools for measurement of impairment or, possibly, different therapeutic mechanisms of action. Determining response to biologics or discussion of disease remission requires assessing symptoms that may occur daily rather than focusing on exacerbations that occur once or twice a year for patients at the highest steps of care according to the guidelines.

Unraveling Prevalence and Effects of Deleterious Mutations in Maize Elite Lines across Decades of Modern Breeding.

Future breeding is likely to involve the detection and removal of deleterious alleles, which are mutations that negatively affect crop fitness. However, little is known about the prevalence of such mutations and their effects on phenotypic traits in the context of modern crop breeding. To address this, we examined the number and frequency of deleterious mutations in 350 elite maize inbred lines developed over the past few decades in China and the United States. Our findings reveal an accumulation of weakly deleterious mutations and a decrease in strongly deleterious mutations, indicating the dominant effects of genetic drift and purifying selection for the two types of mutations, respectively. We also discovered that slightly deleterious mutations, when at lower frequencies, were more likely to be heterozygous in the developed hybrids. This is consistent with complementation as a potential explanation for heterosis. Subsequently, we found that deleterious mutations accounted for more of the variation in phenotypic traits than nondeleterious mutations with matched minor allele frequencies, especially for traits related to leaf angle and flowering time. Moreover, we detected fewer deleterious mutations in the promoter and gene body regions of differentially expressed genes across breeding eras than in nondifferentially expressed genes. Overall, our results provide a comprehensive assessment of the prevalence and impact of deleterious mutations in modern maize breeding and establish a useful baseline for future maize improvement efforts.

Detecting Episodic Evolution through Bayesian Inference of Molecular Clock Models.

Molecular evolutionary rate variation is a key aspect of the evolution of many organisms that can be modeled using molecular clock models. For example, fixed local clocks revealed the role of episodic evolution in the emergence of SARS-CoV-2 variants of concern. Like all statistical models, however, the reliability of such inferences is contingent on an assessment of statistical evidence. We present a novel Bayesian phylogenetic approach for detecting episodic evolution. It consists of computing Bayes factors, as the ratio of posterior and prior odds of evolutionary rate increases, effectively quantifying support for the effect size. We conducted an extensive simulation study to illustrate the power of this method and benchmarked it to formal model comparison of a range of molecular clock models using (log) marginal likelihood estimation, and to inference under a random local clock model. Quantifying support for the effect size has higher sensitivity than formal model testing and is straight-forward to compute, because it only needs samples from the posterior and prior distribution. However, formal model testing has the advantage of accommodating a wide range molecular clock models. We also assessed the ability of an automated approach, known as the random local clock, where branches under episodic evolution may be detected without their a priori definition. In an empirical analysis of a data set of SARS-CoV-2 genomes, we find "very strong" evidence for episodic evolution. Our results provide guidelines and practical methods for Bayesian detection of episodic evolution, as well as avenues for further research into this phenomenon.

A Practical Significance Bias in Laypeople's Evaluation of Scientific Findings.

People often rely on scientific findings to help them make decisions-however, failing to report effect magnitudes might lead to a potential bias in assuming findings are practically significant. Across two online studies (Prolific; N = 800), we measured U.S. adults' endorsements of expensive interventions described in media reports that led to effects that were small, large, or of unreported magnitude between groups. Participants who viewed interventions with unreported effect magnitudes were more likely to endorse interventions compared with those who viewed interventions with small effects and were just as likely to endorse interventions as those who viewed interventions with large effects, suggesting a practical significance bias. When effect magnitudes were reported, participants on average adjusted their evaluations accordingly. However, some individuals, such as those with low numeracy skills, were more likely than others to act on small effects, even when explicitly prompted to first consider the meaningfulness of the effect.

The Influence of Substance Properties on Arthropod Chemical Defenses: A Meta-Analysis.

Among defenses against predation, chemical defenses are possibly the most studied. However, when addressing the effectiveness of those chemical defenses, previous studies did not include properties of the chemical substances themselves. Lipophilicity, for instance, may facilitate crossing membranes, and boiling point may define the duration of the substances in the air. Moreover, other variables may also be relevant: the predator taxon; the prey model chosen to conduct experiments; whether the prey is presented grouped or not in experiments; and whether the chemical defense is a mixture of many substances or only one. To understand how those factors influence chemical defenses' effectiveness, we conducted a multilevel meta-analysis with 43 studies (127 effect sizes), accounting for different types of dependence. We used Akaike Information Criterion (AICc) to select the best model. The model with the lowest AICc value included only the boiling point, which defines how quickly a chemical substance volatilizes. This model indicated that the most effective chemical defenses had lower boiling point values, i.e., higher volatility. Moreover, we did not find chemicals with very low boiling points, suggesting there might be an optimum range of volatility. Other models, including the intercept-only model, were also recovered among the best models, therefore further studies are needed to confirm the relationship between volatility and chemical defenses' effectiveness. Our results highlight the value of incorporating physicochemical properties in the ecological and evolutionary study of chemical defense.

Scaling and interpreting treatment effects in clinical trials using restricted mean survival time.

BACKGROUND: Restricted mean survival time is the expected duration of survival up to a chosen time of restriction τ. For comparison studies, the difference in restricted mean survival times between two groups provides a summary measure of the treatment effect that is free of assumptions regarding the relative shape of the two survival curves, such as proportional hazards. However, it can be difficult to judge the magnitude of the effect from a comparison of restricted means due to the truncation of observation at time τ. METHODS: In this article, we describe additional ways of expressing the treatment effect based on restricted means that can be helpful in this regard. These include the ratio of restricted means, the ratio of life-years (or time) lost, and the average integrated difference between the survival curves, equal to the difference in restricted means divided by τ. These alternative metrics are straightforward to calculate and provide a means for scaling the effect size as an aid to interpretation. Examples from two randomized, multicenter clinical trials in prostate cancer, NRG/RTOG 0521 and NRG/RTOG 0534, with primary endpoints of overall survival and biochemical/radiological progression-free survival, respectively, are presented to illustrate the ideas. RESULTS: The four effect measures (restricted mean survival time difference, restricted mean survival time ratio, time lost ratio, and average survival rate difference) were 0.45 years, 1.05, 0.81, and 0.038 for RTOG 0521 and 1.36 years, 1.17, 0.56, and 0.12 for RTOG 0534 with τ = 12 and 11 years, respectively. Thus, for example, the 0.45-year difference in the first trial translates into a 19% reduction in time lost and a 3.8% average absolute difference between the survival curves over the 12-year horizon, a modest effect size, whereas the 1.36-year difference in the second trial corresponds to a 44% reduction in time lost and a 12% absolute survival difference, a rather large effect. CONCLUSIONS: In addition to the difference in restricted mean survival times, these alternative measures can be helpful in determining whether the magnitude of the treatment effect is clinically meaningful.

ABkPowerCalculator: An App to Compute Power for Balanced (AB)k Single Case Experimental Designs.

Single case experimental designs are an important research design in behavioral and medical research. Although there are design standards prescribed by the What Works Clearinghouse for single case experimental designs, these standards do not include statistically derived power computations. Recently we derived the equations for computing power for (AB)k designs. However, these computations and the software code in R may not be accessible to applied researchers who are most likely to want to compute power for their studies. Therefore, we have developed an (AB)k power calculator Shiny App (https://abkpowercalculator.shinyapps.io/ABkpowercalculator/) that researchers can use with no software training. These power computations assume that the researcher would be interested in fitting multilevel models with autocorrelations or conduct similar analyses. The purpose of this software contribution is to briefly explain how power is derived for balanced (AB)k designs and to elaborate on how to use the Shiny App. The app works well on not just computers but mobile phones without installing the R program. We believe this can be a valuable tool for practitioners and applied researchers who want to plan their single case studies with sufficient power to detect appropriate effect sizes.

On the effect heterogeneity of established disease susceptibility loci for Alzheimer's disease across different genetic ancestries.

INTRODUCTION: Genome-wide association studies have identified numerous disease susceptibility loci (DSLs) for Alzheimer's disease (AD). However, only a limited number of studies have investigated the dependence of the genetic effect size of established DSLs on genetic ancestry. METHODS: We utilized the whole genome sequencing data from the Alzheimer's Disease Sequencing Project (ADSP) including 35,569 participants. A total of 25,459 subjects in four distinct populations (African ancestry, non-Hispanic White, admixed Hispanic, and Asian) were analyzed. RESULTS: We found that nine DSLs showed significant heterogeneity across populations. Single nucleotide polymorphism (SNP) rs2075650 in translocase of outer mitochondrial membrane 40 (TOMM40) showed the largest heterogeneity (Cochran's Q = 0.00, I2 = 90.08), followed by other SNPs in apolipoprotein C1 (APOC1) and apolipoprotein E (APOE). Two additional loci, signal-induced proliferation-associated 1 like 2 (SIPA1L2) and solute carrier 24 member 4 (SLC24A4), showed significant heterogeneity across populations. DISCUSSION: We observed substantial heterogeneity for the APOE-harboring 19q13.32 region with TOMM40/APOE/APOC1 genes. The largest risk effect was seen among African Americans, while Asians showed a surprisingly small risk effect.

Statistical significance, clinical importance and effect sizes: Enhancing understanding of a study's results.

BACKGROUND: The proper interpretation of a study's results requires both excellent understanding of good methodological practices and deep knowledge of prior results, aided by the availability of effect sizes. METHODS: This review takes the form of an expository essay exploring the complex and nuanced relationships among statistical significance, clinical importance, and effect sizes. RESULTS: Careful attention to study design and methodology will increase the likelihood of obtaining statistical significance and may enhance the ability of investigators/readers to accurately interpret results. Measures of effect size show how well the variables used in a study account for/explain the variability in the data. Studies reporting strong effects may have greater practical value/utility than studies reporting weak effects. Effect sizes need to be interpreted in context. Verbal summary characterizations of effect sizes (e.g., "weak", "strong") are fundamentally flawed and can lead to inappropriate characterization of results. Common language effect size (CLES) indicators are a relatively new approach to effect sizes that may offer a more accessible interpretation of results that can benefit providers, patients, and the public at large. CONCLUSIONS: It is important to convey research findings in ways that are clear to both the research community and to the public. At a minimum, this requires inclusion of standard effect size data in research reports. Proper selection of measures and careful design of studies are foundational to the interpretation of a study's results. The ability to draw useful conclusions from a study is increased when investigators enhance the methodological quality of their work.

The resilience of transplanted seagrass traits encourages detection of restoration success.

Restoration of coastal ecosystems, particularly those dominated by seagrasses, has become a priority to recover the important ecosystem services they provide. However, assessing restoration outcomes as a success or failure remains still difficult, probably due to the unique features of seagrass species and the wide portfolio of practices used on transplanting actions. Here, several traits (maximum leaf length, number of leaves, leaf growth rate per shoot, and leaf elemental carbon and nitrogen contents) of transplanted seagrass Posidonia oceanica were compared to reference meadows in five sites of Western Mediterranean Sea in which restoration were completed in different times. Results have evidenced the resilience of transplanted P. oceanica shoots within a few years since restoration, as traits between treatments changed depending on the elapsed time since settlement. The highlighted stability of the restoration time effect suggests that the recovery of the plants is expected in four years after transplanting.

Examining the normality assumption of a design-comparable effect size in single-case designs.

What Works Clearinghouse (WWC, 2022) recommends a design-comparable effect size (D-CES; i.e., gAB) to gauge an intervention in single-case experimental design (SCED) studies, or to synthesize findings in meta-analysis. So far, no research has examined gAB's performance under non-normal distributions. This study expanded Pustejovsky et al. (2014) to investigate the impact of data distributions, number of cases (m), number of measurements (N), within-case reliability or intra-class correlation (ρ), ratio of variance components (λ), and autocorrelation (ϕ) on gAB in multiple-baseline (MB) design. The performance of gAB was assessed by relative bias (RB), relative bias of variance (RBV), MSE, and coverage rate of 95% CIs (CR). Findings revealed that gAB was unbiased even under non-normal distributions. gAB's variance was generally overestimated, and its 95% CI was over-covered, especially when distributions were normal or nearly normal combined with small m and N. Large imprecision of gAB occurred when m was small and ρ was large. According to the ANOVA results, data distributions contributed to approximately 49% of variance in RB and 25% of variance in both RBV and CR. m and ρ each contributed to 34% of variance in MSE. We recommend gAB for MB studies and meta-analysis with N ≥ 16 and when either (1) data distributions are normal or nearly normal, m = 6, and ρ = 0.6 or 0.8, or (2) data distributions are mildly or moderately non-normal, m ≥ 4, and ρ = 0.2, 0.4, or 0.6. The paper concludes with a discussion of gAB's applicability and design-comparability, and sound reporting practices of ES indices.

A meta-analysis on the relationship between media with violence and aggression in Iranian sports.

The present research aimed to conduct a systematic study on violence and aggression in the context of Iranian sports and perform a meta-analysis to investigate the association between the media and violence and aggression in sports. The research encompassed all relevant studies available in scientific databases within Iran (such as Magiran, Seyed, Civilica, Normagz, Humane resource study, and police publications), as well as dissertations from the information and scientific documents database. The selected timeframe for this analysis covered the years 2001 to 2018 in the Iranian context. Through this process, 209 studies related to the subject were identified, out of which 10 studies were included in the meta-analysis based on the research protocol investigating the relationship between media and violence and aggression in sports. Data analysis was performed using SPSS25 and CMA2 software. The results showed several variables played prominent roles in the researches on violence and aggression in sports, including media performance, referees' performance, stadium amenities, law enforcement and security factors, external and internal stadium environment, coach's behavior, social control, family influence, education, socio-economic factors, substance abuse, players' behavior, influence of friends, managerial aspects, and cultural and political factors. Inferential statistics indicated effect size for the relationship between media and violence and aggression, under the fixed model, was determined to be 0.259, and under the random model, it was 0.306, both of which were statistically significant. Consequently, based on the findings from the meta-analysis, a significant direct relationship between media and violence and aggression in sports was established.

Attribution of Cancer Origins to Endogenous, Exogenous, and Preventable Mutational Processes.

Mutational processes in tumors create distinctive patterns of mutations, composed of neutral "passenger" mutations and oncogenic drivers that have quantifiable effects on the proliferation and survival of cancer cell lineages. Increases in proliferation and survival are mediated by natural selection, which can be quantified by comparing the frequency at which we detect substitutions to the frequency at which we expect to detect substitutions assuming neutrality. Most of the variants detectable with whole-exome sequencing in tumors are neutral or nearly neutral in effect, and thus the processes generating the majority of mutations may not be the primary sources of the tumorigenic mutations. Across 24 cancer types, we identify the contributions of mutational processes to each oncogenic variant and quantify the degree to which each process contributes to tumorigenesis. We demonstrate that the origination of variants driving melanomas and lung cancers is predominantly attributable to the preventable, exogenous mutational processes associated with ultraviolet light and tobacco exposure, respectively, whereas the origination of selected variants in gliomas and prostate adenocarcinomas is largely attributable to endogenous processes associated with aging. Preventable mutations associated with pathogen exposure and apolipoprotein B mRNA-editing enzyme activity account for a large proportion of the cancer effect within head-and-neck, bladder, cervical, and breast cancers. These attributions complement epidemiological approaches-revealing the burden of cancer driven by single-nucleotide variants caused by either endogenous or exogenous, nonpreventable, or preventable processes, and crucially inform public health strategies.

CoCoA: conditional correlation models with association size.

Many scientific questions can be formulated as hypotheses about conditional correlations. For instance, in tests of cognitive and physical performance, the trade-off between speed and accuracy motivates study of the two variables together. A natural question is whether speed-accuracy coupling depends on other variables, such as sustained attention. Classical regression techniques, which posit models in terms of covariates and outcomes, are insufficient to investigate the effect of a third variable on the symmetric relationship between speed and accuracy. In response, we propose a conditional correlation model with association size, a likelihood-based statistical framework to estimate the conditional correlation between speed and accuracy as a function of additional variables. We propose novel measures of the association size, which are analogous to effect sizes on the correlation scale while adjusting for confound variables. In simulation studies, we compare likelihood-based estimators of conditional correlation to semiparametric estimators adapted from genomic studies and find that the former achieves lower bias and variance under both ideal settings and model assumption misspecification. Using neurocognitive data from the Philadelphia Neurodevelopmental Cohort, we demonstrate that greater sustained attention is associated with stronger speed-accuracy coupling in a complex reasoning task while controlling for age. By highlighting conditional correlations as the outcome of interest, our model provides complementary insights to traditional regression modeling and partitioned correlation analyses.

Quantitative analyses of cytokine profiles reveal hormone-mediated modulation of cytokine profiles in recurrent spontaneous miscarriage.

PURPOSE: Cytokines play important roles in pregnancy complications. Some hormones such as estrogen, progesterone, and dydrogesterone have been shown to alter cytokine profiles. Understanding how cytokine profiles are affected by these hormones is therefore an important step towards immunomodulatory therapies for pregnancy complications. We analyse previously published data on the effects of estrogen, progesterone, and dydrogesterone on cytokine balances in women having recurrent spontaneous miscarriages. MATERIALS AND METHODS: Levels of eight cytokines (IFN-γ, IL-2, IL-6, IL-10, IL-13, IL-17, IL-23, TNF-α) from n = 22 women presenting unexplained recurrent spontaneous miscarriages were studied. Cytokine values were recorded after in vitro exposure of peripheral blood cells to estrogen, progesterone, and dydrogesterone. We expand on earlier analysis of the dataset by employing different statistical techniques including effect sizes for individual cytokine values, a more powerful statistical test, and adjusting p-values for multiple comparisons. We employ multivariate analysis methods, including to determine the relative magnitude of the effects of the hormone therapies on cytokines. A new statistical method is introduced based on pairwise distances able to accommodate complex relations in cytokine profiles. RESULTS: We report several statistically significant differences in individual cytokine values between the control group and each hormone treated group, with estrogen affecting the fewest cytokines, and progesterone and dydrogesterone both affecting seven out of eight cytokines. Exposure to estrogen produces no large effects sizes however, while IFN-γ and IL-17 show large effect sizes for both progesterone and dydrogesterone, among other cytokines. Our new method for identifying which collections (i.e. subsets) of cytokines best distinguish contrasting groups identifies IFN-γ, IL-10 and IL-23 as especially noteworthy for both progesterone and dydrogesterone treatments. CONCLUSIONS: While some statistically significant differences in cytokine levels after exposure to estrogen are found, these have small effect sizes and are unlikely to be clinically relevant. Progesterone and dydrogesterone both induce statistically significant and large effect-size differences in cytokine levels, hence therapy with these two progestogens is more likely to be clinically relevant. Univariate and multivariate methods for identifying cytokine importances provide insight into which groups of cytokines are most affected and in what ways by therapies.

Step-by-step full factorial design to optimize a quantitative sandwich ELISA.

In this work, a quantitative sandwich ELISA was optimized, through a full factorial design of experiments (DOE) in successive steps of a preliminary protocol obtained by the method of one factor at a time (OFAT). The specificity of the optimized ELISA, the lower limit of quantification, the quantification range and the analytical sensitivity of the antigen quantification curve were evaluated, in comparison with the curve obtained from the preliminary protocol. The full factorial DOE was linked to a simple statistical processing, which facilitates the interpretation of the results in those laboratories where there is no trained statistician. The step-by-step optimization of the ELISA and the successive incorporation into the protocol of the best combination of factors and levels, allowed obtaining a specific immunoassay, with an analytical sensitivity 20 times greater and with a lower limit of antigen quantification that decreased from 156.25 at 9.766 ng/mL. As far as we know, there are no reports of optimization of an ELISA following the step-by-step scheme used in this work. The optimized ELISA will be used for the quantification of the TT-P0 protein, the active principle of a vaccine candidate against sea lice.

Two decades of digital interventions for anxiety disorders: a systematic review and meta-analysis of treatment effectiveness.

BACKGROUND: Digital interventions for anxiety disorders are a promising solution to address barriers to evidence-based treatment access. Precise and powerful estimates of digital intervention effectiveness for anxiety disorders are necessary for further adoption in practice. The present systematic review and meta-analysis examined the effectiveness of digital interventions across all anxiety disorders and specific to each disorder v. wait-list and care-as-usual controls. METHODS: A systematic search of bibliographic databases identified 15 030 abstracts from inception to 1 January 2020. Forty-seven randomized controlled trials (53 comparisons; 4958 participants) contributed to the meta-analysis. Subgroup analyses were conducted by an anxiety disorder, risk of bias, treatment support, recruitment, location and treatment adherence. RESULTS: A large, pooled effect size of g = 0.80 [95% Confidence Interval: 0.68-0.93] was found in favor of digital interventions. Moderate to large pooled effect sizes favoring digital interventions were found for generalized anxiety disorder (g = 0.62), mixed anxiety samples (g = 0.68), panic disorder with or without agoraphobia (g = 1.08) and social anxiety disorder (g = 0.76) subgroups. No subgroups were significantly different or related to the pooled effect size. Notably, the effects of guided interventions (g = 0.84) and unguided interventions (g = 0.64) were not significantly different. Supplemental analysis comparing digital and face-to-face interventions (9 comparisons; 683 participants) found no significant difference in effect [g = 0.14 favoring digital interventions; Confidence Interval: -0.01 to 0.30]. CONCLUSION: The precise and powerful estimates found further justify the application of digital interventions for anxiety disorders in place of wait-list or usual care.

Alternative metrics for characterizing longer-term clinical outcomes in difficult-to-treat depression: I. Association with change in quality of life.

BACKGROUND: In difficult-to-treat depression (DTD) the outcome metrics historically used to evaluate treatment effectiveness may be suboptimal. Metrics based on remission status and on single end-point (SEP) assessment may be problematic given infrequent symptom remission, temporal instability, and poor durability of benefit in DTD. METHODS: Self-report and clinician assessment of depression symptom severity were regularly obtained over a 2-year period in a chronic and highly treatment-resistant registry sample (N = 406) receiving treatment as usual, with or without vagus nerve stimulation. Twenty alternative metrics for characterizing symptomatic improvement were evaluated, contrasting SEP metrics with integrative (INT) metrics that aggregated information over time. Metrics were compared in effect size and discriminating power when contrasting groups that did (N = 153) and did not (N = 253) achieve a threshold level of improvement in end-point quality-of-life (QoL) scores, and in their association with continuous QoL scores. RESULTS: Metrics based on remission status had smaller effect size and poorer discrimination of the binary QoL outcome and weaker associations with the continuous end-point QoL scores than metrics based on partial response or response. The metrics with the strongest performance characteristics were the SEP measure of percentage change in symptom severity and the INT metric quantifying the proportion of the observation period in partial response or better. Both metrics contributed independent variance when predicting end-point QoL scores. CONCLUSIONS: Revision is needed in the metrics used to quantify symptomatic change in DTD with consideration of INT time-based measures as primary or secondary outcomes. Metrics based on remission status may not be useful.

Editorial Perspective: When is a 'small effect' actually large and impactful?

Reporting of effect sizes is standard practice in psychology and psychiatry research. However, interpretation of these effect sizes can be meaningless or misleading - in particular, the evaluation of specific effect sizes as 'small', 'medium' and 'large' can be inaccurate depending on the research context. A real-world example of this is research into the mental health of children and young people during the COVID-19 pandemic. Evidence suggests that clinicians and services are struggling with increased demand, yet population studies looking at the difference in mental health before and during the pandemic report effect sizes that are deemed 'small'. In this short review, we utilise simulations to demonstrate that a relatively small shift in mean scores on mental health measures can indicate a large shift in the number of cases of anxiety and depression when scaled up to an entire population. This shows that 'small' effect sizes can in some contexts be large and impactful.