A new approach for handling missing correlation values for meta-analytic structural equation modeling: Corboundary R package.
Ontology highlight
ABSTRACT:
SUBMITTER: Ahn S
PROVIDER: S-EPMC8356472 | biostudies-literature | 2020 Mar
REPOSITORIES: biostudies-literature
Publications
A new approach for handling missing correlation values for meta-analytic structural equation modeling: Corboundary R package.
Ahn Soyeon S Abbamonte John M JM
Campbell systematic reviews 20200131 1
With increased use of multivariate meta-analysis in numerous disciplines, where structural relationships among multiple variables are examined, researchers often encounter a particular challenge due to missing information. The current research concerns missing correlations (<i>r</i>s) in the correlation matrix of <i>m</i> variables (R <sub><i>m</i> × <i>m</i></sub> ) and establish more informative and empirical prior distributions for missing <i>r</i>s in R <sub><i>m</i> × <i>m</i></sub> . In pa ...[more]
Similar Datasets
Project description:We meta-analyzed the relationship between team planning and performance moderated by task, team, context, and methodological factors. For testing our hypothesized model, we used a meta-analytic structural equation modeling approach. Based on K = 33 independent samples (N = 1,885 teams), a mixed-effects model indicated a non-zero moderate positive effect size (ρ = .31, 95% CI [.20, .42]). Methodological quality, generally rated as adequate, was unrelated to effect size. Sensitivity analyses suggest that effect sizes were robust to exclusion of any individual study and publication bias. The statistical power of the studies was generally low and significantly moderated the relationship, with a large positive relationship for studies with high-powered (k = 42, ρ = .40, 95% CI [.27, .54]) and a smaller, significant relationship for low-powered studies (k = 54, ρ = .16, 95% CI [.01, .30]). The effect size was robust and generally not qualified by a large number of moderators, but was more pronounced for less interdependent tasks, less specialized team members, and assessment of quality rather than quantity of planning. Latent class analysis revealed no qualitatively different subgroups within populations. We recommend large-scale collaboration to overcome several methodological weaknesses of the current literature, which is severely underpowered, potentially biased by self-reporting data, and lacks long-term follow-ups.
| S-EPMC9838875 | biostudies-literature
Project description:This study was undertaken to define the number of missing values permissible to render valid total scores for each Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part. To handle missing values, imputation strategies serve as guidelines to reject an incomplete rating or create a surrogate score. We tested a rigorous, scale-specific, data-based approach to handling missing values for the MDS-UPDRS. From two large MDS-UPDRS datasets, we sequentially deleted item scores, either consistently (same items) or randomly (different items) across all subjects. Lin's Concordance Correlation Coefficient (CCC) compared scores calculated without missing values with prorated scores based on sequentially increasing missing values. The maximal number of missing values retaining a CCC greater than 0.95 determined the threshold for rendering a valid prorated score. A second confirmatory sample was selected from the MDS-UPDRS international translation program. To provide valid part scores applicable across all Hoehn and Yahr (H&Y) stages when the same items are consistently missing, one missing item from Part I, one from Part II, three from Part III, but none from Part IV can be allowed. To provide valid part scores applicable across all H&Y stages when random item entries are missing, one missing item from Part I, two from Part II, seven from Part III, but none from Part IV can be allowed. All cutoff values were confirmed in the validation sample. These analyses are useful for constructing valid surrogate part scores for MDS-UPDRS when missing items fall within the identified threshold and give scientific justification for rejecting partially completed ratings that fall below the threshold.
| S-EPMC5072275 | biostudies-literature
Project description:When analyzing large datasets from high-throughput technologies, researchers often encounter missing quantitative measurements, which are particularly frequent in metabolomics datasets. Metabolomics, the comprehensive profiling of metabolite abundances, are typically measured using mass spectrometry technologies that often introduce missingness via multiple mechanisms: (1) the metabolite signal may be smaller than the instrument limit of detection; (2) the conditions under which the data are collected and processed may lead to missing values; (3) missing values can be introduced randomly. Missingness resulting from mechanism (1) would be classified as Missing Not At Random (MNAR), that from mechanism (2) would be Missing At Random (MAR), and that from mechanism (3) would be classified as Missing Completely At Random (MCAR). Two common approaches for handling missing data are the following: (1) omit missing data from the analysis; (2) impute the missing values. Both approaches may introduce bias and reduce statistical power in downstream analyses such as testing metabolite associations with clinical variables. Further, standard imputation methods in metabolomics often ignore the mechanisms causing missingness and inaccurately estimate missing values within a data set. We propose a mechanism-aware imputation algorithm that leverages a two-step approach in imputing missing values. First, we use a random forest classifier to classify the missing mechanism for each missing value in the data set. Second, we impute each missing value using imputation algorithms that are specific to the predicted missingness mechanism (i.e., MAR/MCAR or MNAR). Using complete data, we conducted simulations, where we imposed different missingness patterns within the data and tested the performance of combinations of imputation algorithms. Our proposed algorithm provided imputations closer to the original data than those using only one imputation algorithm for all the missing values. Consequently, our two-step approach was able to reduce bias for improved downstream analyses.
| S-EPMC9109373 | biostudies-literature
Project description:The metaSEM package provides functions to conduct univariate, multivariate, and three-level meta-analyses using a structural equation modeling (SEM) approach via the OpenMx package in the R statistical platform. It also implements the two-stage SEM approach to conducting fixed- and random-effects meta-analytic SEM on correlation or covariance matrices. This paper briefly outlines the theories and their implementations. It provides a summary on how meta-analyses can be formulated as structural equation models. The paper closes with a conclusion on several relevant topics to this SEM-based meta-analysis. Several examples are used to illustrate the procedures in the supplementary material.
| S-EPMC4283449 | biostudies-literature
Project description:BackgroundWhile the correlation between physical activity (PA) and mental health is well known, the mediating mechanism of resilience between the two variables remains unclear.ObjectivesTo systematically evaluate the relationship between PA and mental health, and explore the mediating role of resilience between them.MethodsA systematic search of electronic databases (PubMed, Web of Science, ProQuest, Ebsco, China Knowledge, and China Biomedical Database) was conducted to identify relevant studies, and meta-analytic procedures were used to assess the strength of the relationships between PA and mental health. Furthermore, a meta-analytic structural equation model (MASEM) was used to assess the mediating effects of resilience, ensuring the reliability of our findings.ResultsThe findings of 15 studies (17,043 subjects) were subjected to meta-analysis and route analysis. The findings of the meta-analysis revealed a statistically significant positive correlation of 0.288 (95% CI, 0.166-0.402) between PA and positive indicators of mental health, as well as a statistically significant negative correlation (95% CI, -0.342 to -0.171) with negative indicators of mental health. Furthermore, the results of MASEM path analysis indicated that PA may indirectly impact both positive and negative indicators of mental health through the mediating factor of resilience. The indirect effect values were 0.108 (95% CI, 0.080-0.141) and -0.074 (95% CI, -0.100 to -0.051), respectively, accounting for 40.15% of the total effect value and 28.91%.ConclusionPhysical activity is positively correlated with positive indicators of mental health and negatively correlated with negative indicators of mental health. Moreover, PA can positively influence mental health through the mediating role of resilience.
| S-EPMC11473373 | biostudies-literature
Project description:Improving the accuracy of phenotyping through the use of advanced psychometric tools will increase the power to find significant associations with genetic variants and expand the range of possible hypotheses that can be tested on a genome-wide scale. Multivariate methods, such as structural equation modeling (SEM), are valuable in the phenotypic analysis of psychiatric and substance use phenotypes, but these methods have not been integrated into standard genome-wide association analyses because fitting a SEM at each single nucleotide polymorphism (SNP) along the genome was hitherto considered to be too computationally demanding. By developing a method that can efficiently fit SEMs, it is possible to expand the set of models that can be tested. This is particularly necessary in psychiatric and behavioral genetics, where the statistical methods are often handicapped by phenotypes with large components of stochastic variance. Due to the enormous amount of data that genome-wide scans produce, the statistical methods used to analyze the data are relatively elementary and do not directly correspond with the rich theoretical development, and lack the potential to test more complex hypotheses about the measurement of, and interaction between, comorbid traits. In this paper, we present a method to test the association of a SNP with multiple phenotypes or a latent construct on a genome-wide basis using a diagonally weighted least squares (DWLS) estimator for four common SEMs: a one-factor model, a one-factor residuals model, a two-factor model, and a latent growth model. We demonstrate that the DWLS parameters and p-values strongly correspond with the more traditional full information maximum likelihood parameters and p-values. We also present the timing of simulations and power analyses and a comparison with and existing multivariate GWAS software package.
| S-EPMC5423544 | biostudies-literature
Project description:IntroductionGlobal evidence suggests that natural disasters are on the rise, and that earthquake harm disproportionally impacts developing countries. To mitigate these harms, disaster preparedness is an important behavioral strategy in earthquake risk mitigation. However, there is limited evidence about the levels of preparedness in developing countries, especially Iran. Therefore, this study aimed to investigate earthquake preparedness among the general public, using the Theory of Planned Behavior (TPB) as a theoretical framework.MethodThis methodological study was conducted in Iran in 2022, involving 302 participants selected through stratified random sampling. Data was collected using a valid and reliable questionnaire. Structural equation modeling (SEM) was employed to analyze TPB constructs, including attitudes, subjective norms, perceived behavioral control, and intentions, in relation to earthquake preparedness behaviors. The statistical analyses encompassed descriptive statistics, t-tests, ANOVA, correlation coefficients, confirmatory factor analysis, and structural equation modeling.ResultsResults show that attitudes toward disaster preparedness, subjective norms, and perceived behavioral control significantly (< 0.05) influenced residents' disaster preparedness intentions. Additionally, there was good model fit of the TPB in explaining earthquake preparedness behavior. For confirmatory factor analysis (CFA) of TPB model the fit indices were CMin/df = 1.35 (< 3), RMSEA = 0.03 (< 0.05); CFI = 0.96 (> 0.95); TLI = 0.95 (= 0.95), and for SEM model they were CMin/df = 1.36 (< 3), RMSEA = 0.03 (< 0.05); CFI = 0.96 (> 0.95); TLI = 0.96 (> 0.95).ConclusionThe overall fit indexes were acceptable indicating the TPB model is a valid and reliable measurement model for screening population-level readiness for earthquake preparedness, developing and/or implementing health promotion interventions. The findings also suggest that disaster education programs should emphasize the importance of attitudes, subjective norms, and perceived behavioral control in promoting disaster preparedness.
| S-EPMC11792548 | biostudies-literature
Project description:Missing data are unavoidable in environmental epidemiologic surveys. The aim of this study was to compare methods for handling large amounts of missing values: omission of missing values, single and multiple imputations (through linear regression or partial least squares regression), and a fully Bayesian approach. These methods were applied to the PARIS birth cohort, where indoor domestic pollutant measurements were performed in a random sample of babies' dwellings. A simulation study was conducted to assess performances of different approaches with a high proportion of missing values (from 50% to 95%). Different simulation scenarios were carried out, controlling the true value of the association (odds ratio of 1.0, 1.2, and 1.4), and varying the health outcome prevalence. When a large amount of data is missing, omitting these missing data reduced statistical power and inflated standard errors, which affected the significance of the association. Single imputation underestimated the variability, and considerably increased risk of type I error. All approaches were conservative, except the Bayesian joint model. In the case of a common health outcome, the fully Bayesian approach is the most efficient approach (low root mean square error, reasonable type I error, and high statistical power). Nevertheless for a less prevalent event, the type I error is increased and the statistical power is reduced. The estimated posterior distribution of the OR is useful to refine the conclusion. Among the methods handling missing values, no approach is absolutely the best but when usual approaches (e.g. single imputation) are not sufficient, joint modelling approach of missing process and health association is more efficient when large amounts of data are missing.
| S-EPMC4165576 | biostudies-literature
Project description:This dataset is used to clarify the nexus between effective parenting practices, low self-control, and anti-social behaviors in Gottfredson and Hirschi's General Theory of Crime (GTC). The analysis included 72 articles reporting 255 effect sizes (N = 94,604). We used the method of Meta-Analytic Structural Equation Modeling (MASEM) to test the assumptions of GTC. In this regard, we employed Two-Stage Meta-Analytic Structural Equation Modeling (TSSEM) and One Stage Meta-Analytic Structural Equation Modeling (OSMASEM) to perform MASEM and its moderators. The findings of the MASEM revealed that low self-control is a positive and in magnitude modest determinant of anti-social behaviors. The effective parenting practice is negative, of small size, and also a statistically significant determinant of low self-control. We observed that effective parenting practice is statistically significant and, in magnitude, shows small size negative direct and indirect effects on anti-social behaviors. That is, low self-control partially mediated the relationship between effective parenting practices and anti-social behaviors. Consistent with the construct of aggregated effective parenting practices, we found uniform patterns for models performed across the elements of effective parenting practices (i.e., emotionally supportive practices, monitoring, recognition, and effective discipline) with low self-control and anti-social behaviors. The findings of moderator analyses showed that the association between low self-control and anti-social behaviors tended to be stronger when the individualistic score of countries improved.
| S-EPMC7452709 | biostudies-literature
Project description:Firearm homicides disproportionately affect Black communities. Redlining - discriminatory lending practices of the early 20th century - are associated with current increased rates of firearm violence. Poverty and concentrated disadvantage are also associated with firearm violence. The interaction of these factors with racist redlining housing practices remains unclear. We used generalized structural equation modeling to characterize the mediators through which redlining practices of the 1930s led to present rates of firearm violence in Boston using a negative binomial model. Principle component analysis was used to create four distinct mediating variables representing census block socioeconomic and built environment information, while reducing dimensionality. We calculated the direct effect between harmful (Red and Yellow) vs beneficial (Green) designations and firearm incident rate, indirect effect between redlining designation and firearm incident rate through each mediating variable, and the total effect. The percentage mediation of each mediator was subsequently calculated. Red and Yellow areas of Boston were associated with an 11•1 (95% CI 5•5,22•4) and 11•4 (5•7,22•8) increased incident rate of shooting when compared to Green. In the pathway between Red designation and firearm incident rate, poverty and poor educational attainment mediated 20% of the interaction, share of rented housing mediated 8%, and Black share of the population 3%. In the pathway between Yellow designation and firearm incident rate, poverty and poor educational attainment mediated 16% of the association, and Black share of the population mediated 13%. Redlining practices of the 1930s potentially contribute to increased rates of firearm violence through changes to neighborhood environments, namely through preclusion from homeownership, poverty, poor educational attainment, and concentration (i.e. segregation) of Black communities. These downstream mediating factors serve as points for policy interventions to address urban firearm violence. Michael Poulson and Miriam Neufeld were supported by T32 Training Grants (HP10028, GM86308).
| S-EPMC8654098 | biostudies-literature