High Statistical Power Research Articles

Population genomic and size distribution data suggest high genetic connectivity in the sandbar shark (Carcharhinus plumbeus) along a 2700 km coastline

Understanding patterns of connectivity in exploited elasmobranchs is vital to their successful management and ongoing sustainability. In this study we use 1480 nuclear SNP loci to investigate fine-scale genetic connectivity in sandbar sharks (Carcharhinus plumbeus) along the Western Australian coastline into the Timor Sea with high statistical power. The genetic dataset was combined with latitudinal and depth size distribution data to increase our understanding of population structuring of this species along the continuous Western Australian coastline. Genetic analyses based on traditional and Bayesian approaches found no evidence of restrictions to nuclear gene flow in sandbar sharks between Albany on the south coast and the Timor Sea to the north. The size distributions reveal a size-segregated population, with juveniles predominantly found in deeper water in the south of the state, and adults predominantly in shallower northern waters. The combination of these genomic and size datasets indicates that sandbar sharks form a single population within Western Australia. Levels of genetic diversity, e.g. observed and expected heterozygosity and allelic richness, in this population were high, implying that the size of the population is relatively large. The results have important management implications; despite forming a single biological stock, the species’ size segregation highlights the importance of ongoing management to minimize the likelihood of growth overfishing in the south of the state. Also, the stock extends into the Timor Sea where unmanaged Indonesian fishing may impact sustainability in Western Australian waters.

Sharing sensitive research data in the practice of personalised medicine

Fragmentation of health data and biomedical research data is a major obstacle for precision medicine based on data-driven decisions. The development of personalized medicine requires the efficient exploitation of health data resources that are extraordinary in size and complexity, but highly fragmented, as well as technologies that enable data sharing across institutions and even borders. Biobanks are both sample archives and data integration centers. The analysis of large biobank data warehouses in federated datasets promises to yield conclusions with higher statistical power. A prerequisite for data sharing is harmonization, i.e., the mapping of the unique clinical and molecular characteristics of samples into a unified data model and standard codes. These databases, which are aligned to a common schema, then make healthcare information available for privacy-preserving federated data sharing and learning. The re-evaluation of sensitive health data is inconceivable without the protection of privacy, the legal and conceptual framework for which is set out in the GDPR (General Data Protection Regulation) and the FAIR (findable, accessible, interoperable, reusable) principles. For biobanks in Europe, the BBMRI-ERIC (Biobanking and Biomolecular Research Infrastructure - European Research Infrastructure Consortium) research infrastructure develops common guidelines, which the Hungarian BBMRI Node joined in 2021. As the first step, a federation of biobanks can connect fragmented datasets, providing high-quality data sets motivated by multiple research goals. Extending the approach to real-word data could also allow for higher level evaluation of data generated in the real world of patient care, and thus take the evidence generated in clinical trials within a rigorous framework to a new level. In this publication, we present the potential of federated data sharing in the context of the Semmelweis University Biobanks joint project. Orv Hetil. 2023; 164(21): 811-819.

Does CUD measure interhemispheric transfer time? The allocation of attention influences the Poffenberger effect

A simple reaction time (SRT) difference between responses to visual stimuli presented ipsilaterally and contralaterally to the responding hand, known as the Poffenberger effect or the crossed–uncrossed difference (CUD), has been commonly interpreted as a measure of interhemispheric transfer time (IHTT). However, the validity of this interpretation and the measure's reliability have been debated. The present study aimed at obtaining reliable evidence of the influence of spatial attention on the CUD, which would provide an argument against the classical interpretation of CUD. To meet the high statistical power requirements, over 100 thousand SRTs in total were collected from 12 participants. The task had three stimulus presentation conditions differing in the degree of stimulus location uncertainty: blocked (no uncertainty), randomized (full uncertainty), and mixed (25% uncertainty). The results showed robust effects of location uncertainty, proving spatial attention's contribution to the CUD. Further, we observed a strong visual-field asymmetry reflecting the right hemisphere specialization in target detection and spatial reorienting. Lastly, despite exceptional reliability of the component SRT measures, the CUD reliability was still too low to justify using this measure as an index of individual differences.

Variable Selection for Mediators under a Bayesian Mediation Model

This study proposes a Bayesian variable selection approach to select mediators and quantify their respective posterior probabilities in exploratory mediation analysis. Monte Carlo simulation studies demonstrate that the proposed method has high statistical power in selecting mediating effects and low Type I error rate in excluding null effects. By estimating the probability of a given mediating effect via the posterior distribution, the proposed method quantifies the variable’s influence on a continuum scale. This is an attractive and unique gain that neither conventional p-value-based mediation methods nor the regularization-based LASSO method for exploratory mediation possess. We offer four decision rules to assist in selecting mediators and excluding null effects to minimize a common problem (i.e., elevated type I errors) in the exploratory context, as well as provide an empirical example to illustrate the proposed method’s application and interpretation. We end with a discussion of the work and directions for future work.

A maximum kernel-based association test to detect the pleiotropic genetic effects on multiple phenotypes.

Testing the association between multiple phenotypes with a set of genetic variants simultaneously, rather than analyzing one trait at a time, is receiving increasing attention for its high statistical power and easy explanation on pleiotropic effects. The kernel-based association test (KAT), being free of data dimensions and structures, has proven to be a good alternative method for genetic association analysis with multiple phenotypes. However, KAT suffers from substantial power loss when multiple phenotypes have moderate to strong correlations. To handle this issue, we propose a maximum KAT (MaxKAT) and suggest using the generalized extreme value distribution to calculate its statistical significance under the null hypothesis. We show that MaxKAT reduces computational intensity greatly while maintaining high accuracy. Extensive simulations demonstrate that MaxKAT can properly control type I error rates and obtain remarkably higher power than KAT under most of the considered scenarios. Application to a porcine dataset used in biomedical experiments of human disease further illustrates its practical utility. The R package MaxKAT that implements the proposed method is available on Github https://github.com/WangJJ-xrk/MaxKAT.

Piecewise Structural Equation Modeling of the collective implicature in child language

ABSTRACT We test children’s distributive and collective sentence interpretations and the variables that predict them. In our first experiment, we establish that adult English collective sentences with the or some in the subject are categorically collective in their interpretations. We further demonstrate that children’s collective and distributive interpretations are predicted by an independent measure of lexical growth, consistent with the lexical refraction hypothesis, and that their collective interpretations are predicted by their distributive interpretations, consistent with the pragmatic scale hypothesis. Furthermore, the distributive interpretations produce complete mediation between lexicon and collective interpretations in a mediation analysis. In our second experiment, we take independent measures of Spanish-speaking children’s knowledge of the Approximate Number System, inhibition, lexicon and phrasal syntax. We then fit a Piecewise Structural Equation Model with these variables, with high statistical power and showing high goodness-of-fit. We consider the contrast between this model of collective interpretations, which are putatively conversational implicatures, and quantity implicature models from previous studies.

Multiple logistic regression model to predict bile leak associated with subtotal cholecystectomy.

There are no prediction models for bile leakage associated with subtotal cholecystectomy (STC). Therefore, this study aimed to generate a multivariable prediction model for post-STC bile leakage and evaluate its overall performance. We analysed prospectively managed data of patients who underwent STC by a single consultant surgeon between 14 May 2013 and 21 December 2021. STC was schematised into four variants with five subvariants and classified broadly as closed-tract or open-tract STC. A contingency table was used to detect independent risk factors for bile leakage. A multiple logistic regression analysis was used to generate a model. Discrimination and calibration statistics were computed to assess the accuracy of the model. A total of 81 patients underwent the STC procedure. Twenty-eight patients (35%) developed bile leakage. Of these, 18 patients (64%) required secondary surgical intervention. Multivariable logistic regression revealed two independent predictors of post-STC bile leak: open-tract STC (odds ratio [OR], 7.07; 95% confidence interval [CI], 2.191-25.89; P = 0.0170) and acute cholecystitis (OR, 5.449; 95% CI, 1.584-23.48; P = 0.0121). The area under the receiver-operating characteristic curve was 82.11% (95% CI, 72.87-91.34; P < 0.0001). Tjur's pseudo-R2 was 0.3189 and the Hosmer-Lemeshow goodness-of-fit statistic was 4.916 (P = 0.7665). Open-tract STC and acute cholecystitis are the most reliable predictors of bile leakage associated with STC. Future prospective, multicentre studies with higher statistical power are needed to generate more specific and externally validated prediction models for post-STC bile leaks.

Causal mediation effects in single case experimental designs.

Single case experimental designs (SCEDs) are used to test treatment effects in a wide range of fields and consist of repeated measurements for a single case throughout one or more baseline phases and throughout one or more treatment phases. Recently, mediation analysis has been applied to SCEDs. Mediation analysis decomposes the total treatment-outcome effect into a direct and indirect effect, and therefore aims to unravel the causal processes underlying treatment-outcome effects. The most recent methodological advancement for mediation analysis is the development of causal mediation analysis methodology which clarifies the necessary causal assumptions for mediation analysis. The goal of this article is to derive the causal mediation effects and corresponding standard errors based on piecewise linear regression models for the mediator and outcome and to evaluate the performance of these regression estimators and standard errors. Whereas previous studies estimated the direct and indirect effects as either the change in level or change in trend, we showed that the causal direct and indirect effects incorporate both the change in level and change in trend. Based on our simulation study we showed that for the causal indirect effects, Monte Carlo confidence intervals provided accurate (i.e., p = .05) Type I error rates and higher statistical power than normal theory confidence intervals. For the causal direct effects and total effect, normal theory confidence intervals provided accurate Type I error rates and higher statistical power than the Monte Carlo confidence intervals. Limitations and future directions are discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Bayesian lesion-deficit inference with Bayes factor mapping: Key advantages, limitations, and a toolbox

Statistical lesion-symptom mapping is largely dominated by frequentist approaches with null hypothesis significance testing. They are popular for mapping functional brain anatomy but are accompanied by some challenges and limitations. The typical analysis design and the structure of clinical lesion data are linked to the multiple comparison problem, an association problem, limitations to statistical power, and a lack of insights into evidence for the null hypothesis. Bayesian lesion deficit inference (BLDI) could be an improvement as it collects evidence for the null hypothesis, i.e. the absence of effects, and does not accumulate α-errors with repeated testing. We implemented BLDI by Bayes factor mapping with Bayesian t-tests and general linear models and evaluated its performance in comparison to frequentist lesion-symptom mapping with a permutation-based family-wise error correction. We mapped the voxel-wise neural correlates of simulated deficits in an in-silico-study with 300 stroke patients, and the voxel-wise and disconnection-wise neural correlates of phonemic verbal fluency and constructive ability in 137 stroke patients. Both the performance of frequentist and Bayesian lesion-deficit inference varied largely across analyses. In general, BLDI could find areas with evidence for the null hypothesis and was statistically more liberal in providing evidence for the alternative hypothesis, i.e. the identification of lesion-deficit associations. BLDI performed better in situations in which the frequentist method is typically strongly limited, for example with on average small lesions and in situations with low power, where BLDI also provided unprecedented transparency in terms of the informative value of the data. On the other hand, BLDI suffered more from the association problem, which led to a pronounced overshoot of lesion-deficit associations in analyses with high statistical power. We further implemented a new approach to lesion size control, adaptive lesion size control, that, in many situations, was able to counter the limitations imposed by the association problem, and increased true evidence both for the null and the alternative hypothesis. In summary, our results suggest that BLDI is a valuable addition to the method portfolio of lesion-deficit inference with some specific and exclusive advantages: it deals better with smaller lesions and low statistical power (i.e. small samples and effect sizes) and identifies regions with absent lesion-deficit associations. However, it is not superior to established frequentist approaches in all respects and therefore not to be seen as a general replacement. To make Bayesian lesion-deficit inference widely accessible, we published an R toolkit for the analysis of voxel-wise and disconnection-wise data.

Genetic Validation of Psoriasis Phenotyping in UK Biobank Supports the Utility of Self-Reported Data and Composite Definitions for Large Genetic and Epidemiological Studies

Genetic Validation of Psoriasis Phenotyping in UK Biobank Supports the Utility of Self-Reported Data and Composite Definitions for Large Genetic and Epidemiological Studies

Geometry-derived statistical significance: A probabilistic framework for detecting true positive findings in MRI data.

The false discovery rate (FDR) procedure does not incorporate the geometry of the random field and requires high statistical power at each voxel, a requirement not satisfied by the limited number of participants in imaging studies. Topological FDR, threshold free cluster enhancement (TFCE), and probabilistic TFCE improve statistical power by incorporating local geometry. However, topological FDR requires specifying a cluster defining threshold and TFCE requires specifying transformation weights. Geometry-derived statistical significance (GDSS) procedure overcomes these limitations by combining voxelwise p-values for the test statistic with the probabilities computed from the local geometry for the random field, thereby providing substantially greater statistical power than the procedures currently used to control for multiple comparisons. We use synthetic data and real-world data to compare its performance against the performance of these other, previously developed procedures. GDSS provided substantially greater statistical power relative to the comparator procedures, which was less variable to the number of participants. GDSS was more conservative than TFCE: that is, it rejected null hypotheses at voxels with much higher effect sizes than TFCE. Our experiments also showed that the Cohen's D effect size decreases as the number of participants increases. Therefore, sample size calculations from small studies may underestimate the participants required in larger studies. Our findings also suggest effect size maps should be presented along with p-value maps for correct interpretation of findings. GDSS compared with the other procedures provides considerably greater statistical power for detecting true positives while limiting false positives, especially in small sized (<40 participants) imaging cohorts.

THE EFFECT DIRECTION SHOULD BE TAKEN INTO ACCOUNT WHEN ASSESSING SMALL-STUDY EFFECTS.

Studies with statistically significant results are frequently more likely to be published than those with non-significant results. This phenomenon leads to publication bias or small-study effects and can seriously affect the validity of the conclusion from systematic reviews and meta-analyses. Small-study effects typically appear in a specific direction, depending on whether the outcome of interest is beneficial or harmful, but this direction is rarely taken into account in conventional methods. We propose to use directional tests to assess potential small-study effects. The tests are built on a one-sided testing framework based on the existing Egger's regression test. We performed simulation studies to compare the proposed one-sided regression tests, conventional two-sided regression tests, as well as two other competitive methods (Begg's rank test and the trim-and-fill method). Their performance was measured by type I error rates and statistical power. Three real-world meta-analyses on measurements of infrabony periodontal defects were also used to examine the various methods' performance. Based on simulation studies, the one-sided tests could have considerably higher statistical power than competing methods, particularly their two-sided counterparts. Their type I error rates were generally controlled well. In the case study of the three real-world meta-analyses, by accounting for the favored direction of effects, the one-sided tests could rule out potential false-positive conclusions about small-study effects. They also are more powerful in assessing small-study effects than the conventional two-sided tests when true small-study effects likely exist. We recommend researchers incorporate the potential favored direction of effects into the assessment of small-study effects.

Morphological Abnormalities in Early-Onset Schizophrenia Revealed by Structural Magnetic Resonance Imaging

Schizophrenia is a pathological condition characterized by delusions, hallucinations, and a lack of motivation. In this study, we performed a morphological analysis of regional biomarkers in early-onset schizophrenia, including cortical thicknesses, surface areas, surface curvature, and volumes extracted from T1-weighted structural magnetic resonance imaging (MRI) and compared these findings with a large cohort of neurotypical controls. Results demonstrate statistically significant abnormal presentation of the curvature of select brain regions in early-onset schizophrenia with large effect sizes, inclusive of the pars orbitalis, pars triangularis, posterior cingulate cortex, frontal pole, orbital gyrus, lateral orbitofrontal gyrus, inferior occipital gyrus, as well as in medial occipito-temporal, lingual, and insular sulci. We also observed reduced regional volumes, surface areas, and variability of cortical thicknesses in early-onset schizophrenia relative to neurotypical controls in the lingual, transverse temporal, cuneus, and parahippocampal cortices that did not reach our stringent standard for statistical significance and should be confirmed in future studies with higher statistical power. These results imply that abnormal neurodevelopment associated with early-onset schizophrenia can be characterized with structural MRI and may reflect abnormal and possibly accelerated pruning of the cortex in schizophrenia.

Effectiveness of telehealth interventions on psychological outcomes and quality of life in community adults during the COVID-19 pandemic: A systematic review and meta-analysis.

Physical distancing and contact restrictions have been implemented in most countries and territories due to the rapid spread of SARS-CoV-2. This has caused physical, emotional, and psychological distress for adults living in the community. Diversified telehealth interventions have been widely applied in health care and have proven to be cost-effective and well accepted by patients and health professionals. Currently, the effectiveness of telehealth interventions on psychological outcomes and quality of life among community adults during the COVID-19 pandemic remains unclear. A literature search was conducted using PubMed, PsycINFO, CINAHL, EMBASE, MEDLINE, and the Cochrane Library from 2019 to October 2022. Twenty-five randomized controlled trials with 3228 subjects were finally included in this review. Two independent reviewers performed the screening, extraction of key data points, and appraisal of the methodological quality. There were positive effects of telehealth interventions on anxiety, stress, loneliness, and well-being among community adults. Participants who were women or older adults were more likely to recover from negative emotions, increase well-being, and improve quality of life. The real-time and interactive interventions and remote cognitive-behavioural therapy (CBT) may be better choices during the COVID-19 pandemic. Based on the findings of this review, health professionals have more options and alternatives for delivering telehealth interventions in the future. Rigorously designed randomized controlled trials (RCTs) with higher statistical power and long-term follow-up should be conducted in the future to strengthen the currently weak evidence.

Parametric mapping of QTL for resistance to European canker in apple in ‘Aroma’ × ‘Discovery’

Resistance to European canker (Neonectria ditissima) in apple is currently one of the most important breeding targets for commercial production in Sweden. Previous research has identified significant genetic variation in susceptibility to the disease, with the local Swedish cultivar ‘Aroma’ considered as one of the most resistant cultivars. Identification of genetic regions underlying the resistance of this cultivar would be a valuable tool for future breeding. Thus, we performed Bayesian quantitative trait loci (QTL) mapping for resistance to European canker in a full-sib family of ‘Aroma’ × ‘Discovery’. Mapping was performed with the area under the disease progression curves (AUDPCs) from all seven (AUDPC_All7) and the first four assessments (AUDPC_First4), and three parameters of a sigmoid growth model for lesion length. As a scale for the effect of the different parameters, historic phenotypic data from screenings of a genetically diverse germplasm was compiled and re-analyzed. The parametrization of the data on lesion growth increased the number of QTL that could be identified with high statistical power, and provided some insight into their roles during different stages of disease development in the current experimental setup. Five QTL regions with strong or decisive evidence were identified on linkage groups 1, 8, 15, and 16. The QTL regions could be assigned to either of the parameters lesion length at the first assessment (‘LL_A1’), the maximal lesion growth rate (lesion length doubling time, ‘t_gen’), and the lesion length at girdling (‘LL_G’). Three of these QTL were traced along the pedigrees of some known relatives of the FS family, and discussed in relation to future crosses for breeding and genetic research.

A nonparametric alternative to the Cochran-Armitage trend test in genetic case-control association studies: The Jonckheere-Terpstra trend test.

Identifications of novel genetic signals conferring susceptibility to human complex diseases is pivotal to the disease diagnosis, prevention, and treatment. Genetic association study is a powerful tool to discover candidate genetic signals that contribute to diseases, through statistical tests for correlation between the disease status and genetic variations in study samples. In such studies with a case-control design, a standard practice is to perform the Cochran-Armitage (CA) trend test under an additive genetic model, which suffers from power loss when the model assumption is wrong. The Jonckheere-Terpstra (JT) trend test is an alternative method to evaluate association in a nonparametric way. This study compares the power of the JT trend test and the CA trend test in various scenarios, including different sample sizes (200-2000), minor allele frequencies (0.05-0.4), and underlying modes of inheritance (dominant genetic model to recessive genetic model). By simulation and real data analysis, it is shown that in general the JT trend test has higher, similar, and lower power than the CA trend test when the underlying mode of inheritance is dominant, additive, and recessive, respectively; when the sample size is small and the minor allele frequency is low, the JT trend test outperforms the CA trend test across the spectrum of genetic models. In sum, the JT trend test is a valuable alternative to the CA trend test under certain circumstances with higher statistical power, which could lead to better detection of genetic signals to human diseases and finer dissection of their genetic architecture.

Facilitating Next-Generation Pre-Exposure Prophylaxis Clinical Trials Using HIV Recent Infection Assays: A Consensus Statement from the Forum HIV Prevention Trial Design Project.

Standard-of-care HIV pre-exposure prophylaxis (PrEP) is highly efficacious, but uptake of and persistence on a daily oral pill is low in many settings. Evaluation of alternate PrEP products will require innovation to avoid the unpractically large sample sizes in noninferiority trials. We propose estimating HIV incidence in people not on PrEP as an external counterfactual to which on-PrEP incidence in trial subjects can be compared. HIV recent infection testing algorithms (RITAs), such as the limiting antigen avidity assay plus viral load used on specimens from untreated HIV positive people identified during screening, is one possible approach. Its feasibility is partly dependent on the sample size needed to ensure adequate power, which is impacted by RITA performance, the number of recent infections identified, the expected efficacy of the intervention, and other factors. Screening sample sizes to support detection of an 80% reduction in incidence for 3 key populations are more modest, and comparable to the number of participants in recent phase III PrEP trials. Sample sizes would be significantly larger in populations with lower incidence, where the false recency rate is higher or if PrEP efficacy is expected to be lower. Our proposed counterfactual approach appears to be feasible, offers high statistical power, and is nearly contemporaneous with the on-PrEP population. It will be important to monitor the performance of this approach during new product development for HIV prevention. If successful, it could be a model for preventive HIV vaccines and prevention of other infectious diseases.

Between Lynn White and Big Gods – religious sources of pro-environmental attitudes – cross-cultural test of Lynn White's Hypothesis

This study examines the connection between religious beliefs and pro-environmental attitudes using data from three waves of the World Values Survey (WVS) conducted in 1989, 1999, and 2009. The findings reveal a paradoxical relationship between religion and environmental attitudes. While respondents who identified with a specific religious denomination tended to have lower pro-environmental attitudes than those without a religious affiliation, individuals who placed high personal importance on their religiosity and participated in religious practices were more likely to have pro-environmental attitudes. This relationship was moderated by the religious denomination. However, the effects were minimal, explaining only 0.4% of the variance, and were only detected due to the analysis's high statistical power.

The Effect of Health Education on Knowledge, Attitude and Practice of Folic Acid Supplementation among Periconceptional Women at the Health Clinic in Kuala Lumpur

Background: Neural tube defects (NTDs) are congenital abnormalities of the central nervous system that can be prevented by consuming 400 µg of folic acid daily during conception until the twelfth week of pregnancy. Malaysia is experiencing an increase in NTD cases. Addressing women's knowledge, attitudes, and practices regarding folic acid- and folate-rich foods is crucial to reduce the incidence of NTDs. Objectives: This study aimed to evaluate the impact of a health education program on improving knowledge, attitudes, and practices related to folic acid supplementation among periconceptional women in a community clinic. Methods: A pre- and post-quasi-experimental study was conducted among 114 periconceptional women in two groups. The intervention group received structured Folic Acid supplementation education, while the control group received routine antenatal care. Results: Regarding knowledge, the intervention significantly improved the scores of the intervention group, increasing from 13.40 to 15.39. This improvement was statistically significant, with a mean difference of 1.98 (p &lt; 0.001), large effect size of 0.16, and strong statistical power of 0.89. Meanwhile for attitude, the intervention group experienced a more significant increase in scores, from 25.72 to 27.46. Statistically significant effects were observed (p = 0.031), along with a large effect size 0.26 and relatively high statistical power 0.99. In terms of practice, the intervention group showed a significant improvement, with scores increasing from 27.68 33.16. The findings were significant, with a mean difference of 5.47 (p &lt; 0.001), extreme effect size of 0.74, and strong statistical power of 1.00. Conclusion: A comprehensive health education program for pregnant women can significantly improve their understanding, attitude, and adherence to folic acid supplementation. In addition, it is essential to promote the consumption of folic acid-fortified foods.

Moderation role of government acts, laws and policies between economic factors and risk management: A case study of Saudi Arabia contractors

In construction projects, contractors have prioritized risks due to abandonment of operations and events, interruptions, time, and cost overruns. Construction hazards are linked to the ambiguity and unpredictability of the timely delivery of a project, with standard quality and within an allowable budget. The bid process is heavily reliant on economic considerations which include the exchange market, rate of interest and cost inflation for equipment and workforce. Project failure takes place if economic considerations have not complied for effective management of risks in construction. The research framework is founded on Organization Control Theory and focused on the PLS-SEM approach which addresses the effect of economic factors with moderating government regulatory procedures on the management of risks in construction within 303 large (higher than 250 workers) Saudi Arabian contractors. In the PLS-SEM approach, complicated models are effectively analyzed with higher statistical power. The findings show that economic factors and government regulatory procedures have a favorable impact on the management of risks in the Saudi Arabian development industry. Additionally, moderating government regulatory procedures has a favorable correlation to the management of risks in the Saudi Arabian construction sector. By addressing economic considerations, this study enables practitioners, experts and stakeholders involved in construction industries to conduct effective management of risks in the construction sector.