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A comparative study of the impacts of unbalanced sample sizes on the four synthesized methods of meta-analytic structural equation modeling.

ABSTRACT: In the first stage of meta-analytic structural equation modeling (MASEM), researchers synthesized studies using univariate meta-analysis (UM) and multivariate meta-analysis (MM) approaches. The MM approaches are known to be of better performance than the UM approaches in the meta-analysis with equal sized studies. However in real situations, where the studies might be of different sizes, the empirical performance of these approaches is yet to be studied in the first and second stages of MASEM. The present study aimed to evaluate the performance of the UM and MM methods, having unequal sample sizes in different primary studies. Testing the homogeneity of correlation matrices and the empirical power, estimating the pooled correlation matrix and also, estimating parameters of a path model were investigated using these approaches by simulation.The results of the first stage showed that Type I error rate was well under control at 0.05 level when the average sample sizes were 200 or more, irrespective of the types of the methods or the sample sizes used. Moreover, the relative percentage biases of the pooled correlation matrices were also lower than 2.5% for all methods. There was a dramatic decrease in the empirical power for all synthesis methods when the inequality of the sample sizes was increased. In fitting the path model at the second stage, MM methods provided better estimation of the parameters.This study showed the different performance of the four methods in the statistical power, especially when the sample sizes of primary studies were highly unequal. Moreover, in fitting the path model, the MM approaches provided better estimation of the parameters.

SUBMITTER: Alamolhoda M

PROVIDER: S-EPMC5585956 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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A comparative study of the impacts of unbalanced sample sizes on the four synthesized methods of meta-analytic structural equation modeling.

Alamolhoda Marzieh M Ayatollahi Seyyed Mohammad Taghi SMT Bagheri Zahra Z

BMC research notes 20170906 1

<h4>Background</h4>In the first stage of meta-analytic structural equation modeling (MASEM), researchers synthesized studies using univariate meta-analysis (UM) and multivariate meta-analysis (MM) approaches. The MM approaches are known to be of better performance than the UM approaches in the meta-analysis with equal sized studies. However in real situations, where the studies might be of different sizes, the empirical performance of these approaches is yet to be studied in the first and second ...[more]

PMID: 28877742

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Project description:The standardization of collection and processing methods for rumen samples is crucial to reduce the level of errors that may affect the analysis and interpretation of the data. The aim of this study was to compare two processing methods and their impacts on the microbial community composition analysis, from material that was either immediately frozen or samples that were stored as cell pellets after removing the supernatant prior to freezing. Eight rumen-fistulated Brahman steers received chloroform as an antimethanogenic compound for 21 days. Rumen fluid samples (60 mL per animal) were collected using a probe covered with two layers of cheesecloth at 3 h post feeding at day 0 prior-treatment (control period) and day 21 of treatment. One sub-set of samples were placed in dry ice and stored at -80°C (Method 1) for subsequent DNA extraction, while a second subset of samples was centrifuged, the supernatant removed and the microbial pellet and rumen contents placed in dry ice and stored at -80°C (Method 2) prior to DNA extractions. Phylogenetic based methods (Illumina Miseq) targeting the 16S rRNA gene were used to characterize the bacterial and archaeal communities from both collection methods for the control and treatment periods. The results from this study showed that the chloroform treatment was significantly different for all beta diversity measures regardless of the processing method used. Significant differences in the relative abundances of some bacteria and archaea, such as Elusimicrobia, Fibrobacteres, Lentisphaerae, Spirochaetes, and Verrucomicrobia and Methanomassiliicoccaceae, were observed at higher levels in the Method 2. These microbial populations are known to have fragile cell wall structures and are susceptible to cell lysis. Regardless of the processing method used, both identified the key microbial groups and can be used to compare the relative shifts in the rumen microbiome between treatments. However, immediately freezing samples might alter the abundance of material from species that are more readily lysed and will not be suitable for studies that aim to assign absolute abundance values to these species within the rumen.

Project description:BackgroundMediation analysis can be used to evaluate the effect of an exposure on an outcome acting through an intermediate variable or mediator. For studies with small sample sizes, permutation testing may be useful in evaluating the indirect effect (i.e., the effect of exposure on the outcome through the mediator) while maintaining the appropriate type I error rate. For mediation analysis in studies with small sample sizes, existing permutation testing methods permute the residuals under the full or alternative model, but have not been evaluated under situations where covariates are included. In this article, we consider and evaluate two additional permutation approaches for testing the indirect effect in mediation analysis based on permutating the residuals under the reduced or null model which allows for the inclusion of covariates.MethodsSimulation studies were used to empirically evaluate the behavior of these two additional approaches: (1) the permutation test of the Indirect Effect under Reduced Models (IERM) and (2) the Permutation Supremum test under Reduced Models (PSRM). The performance of these methods was compared to the standard permutation approach for mediation analysis, the permutation test of the Indirect Effect under Full Models (IEFM). We evaluated the type 1 error rates and power of these methods in the presence of covariates since mediation analysis assumes no unmeasured confounders of the exposure-mediator-outcome relationships.ResultsThe proposed PSRM approach maintained type I error rates below nominal levels under all conditions, while the proposed IERM approach exhibited grossly inflated type I rates in many conditions and the standard IEFM exhibited inflated type I error rates under a small number of conditions. Power did not differ substantially between the proposed PSRM approach and the standard IEFM approach.ConclusionsThe proposed PSRM approach is recommended over the existing IEFM approach for mediation analysis in studies with small sample sizes.

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A comparative study of the impacts of unbalanced sample sizes on the four synthesized methods of meta-analytic structural equation modeling.

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A comparative study of the impacts of unbalanced sample sizes on the four synthesized methods of meta-analytic structural equation modeling.

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