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Evaluating statistical significance in a meta-analysis by using numerical integration.

ABSTRACT: Meta-analysis is a method for enhancing statistical power through the integration of information from multiple studies. Various methods for integrating p-values (i.e., statistical significance), including Fisher's method under an independence assumption, the permutation method, and the decorrelation method, have been broadly used in bioinformatics and computational biotechnology studies. However, these methods have limitations related to statistical assumption, computing efficiency, and accuracy of statistical significance estimation. In this study, we proposed a numerical integration method and examined its theoretical properties. Simulation studies were conducted to evaluate its Type I error, statistical power, computational efficiency, and estimation accuracy, and the results were compared with those of other methods. The results demonstrate that our proposed method performs well in terms of Type I error, statistical power, computing efficiency (regardless of sample size), and statistical significance estimation accuracy. P-value data from multiple large-scale genome-wide association studies (GWASs) and transcriptome-wise association studies (TWASs) were analyzed. The results demonstrate that our proposed method can be used to identify critical genomic regions associated with rheumatoid arthritis and asthma, increase statistical significance in individual GWASs and TWASs, and control for false-positives more effectively than can Fisher's method under an independence assumption. We created the software package Pbine, available at GitHub (https://github.com/Yinchun-Lin/Pbine).

SUBMITTER: Lin YC

PROVIDER: S-EPMC9283883 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Evaluating statistical significance in a <i>meta</i>-analysis by using numerical integration.

Lin Yin-Chun YC Liang Yu-Jen YJ Yang Hsin-Chou HC

Computational and structural biotechnology journal 20220704

Meta-analysis is a method for enhancing statistical power through the integration of information from multiple studies. Various methods for integrating p-values (i.e., statistical significance), including Fisher's method under an independence assumption, the permutation method, and the decorrelation method, have been broadly used in bioinformatics and computational biotechnology studies. However, these methods have limitations related to statistical assumption, computing efficiency, and accuracy ...[more]

PMID: 35860413

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Project description:BackgroundNowadays, multiple omics data are measured on the same samples in the belief that these different omics datasets represent various aspects of the underlying biological systems. Integrating these omics datasets will facilitate the understanding of the systems. For this purpose, various methods have been proposed, such as Partial Least Squares (PLS), decomposing two datasets into joint and residual subspaces. Since omics data are heterogeneous, the joint components in PLS will contain variation specific to each dataset. To account for this, Two-way Orthogonal Partial Least Squares (O2PLS) captures the heterogeneity by introducing orthogonal subspaces and better estimates the joint subspaces. However, the latent components spanning the joint subspaces in O2PLS are linear combinations of all variables, while it might be of interest to identify a small subset relevant to the research question. To obtain sparsity, we extend O2PLS to Group Sparse O2PLS (GO2PLS) that utilizes biological information on group structures among variables and performs group selection in the joint subspace.ResultsThe simulation study showed that introducing sparsity improved the feature selection performance. Furthermore, incorporating group structures increased robustness of the feature selection procedure. GO2PLS performed optimally in terms of accuracy of joint score estimation, joint loading estimation, and feature selection. We applied GO2PLS to datasets from two studies: TwinsUK (a population study) and CVON-DOSIS (a small case-control study). In the first, we incorporated biological information on the group structures of the methylation CpG sites when integrating the methylation dataset with the IgG glycomics data. The targeted genes of the selected methylation groups turned out to be relevant to the immune system, in which the IgG glycans play important roles. In the second, we selected regulatory regions and transcripts that explained the covariance between regulomics and transcriptomics data. The corresponding genes of the selected features appeared to be relevant to heart muscle disease.ConclusionsGO2PLS integrates two omics datasets to help understand the underlying system that involves both omics levels. It incorporates external group information and performs group selection, resulting in a small subset of features that best explain the relationship between two omics datasets for better interpretability.

Project description:BackgroundThe benefits of stroke unit care in terms of reducing death, dependency and institutional care were demonstrated in a 2009 Cochrane review carried out by the Stroke Unit Trialists' Collaboration.MethodsAs requested by the Belgian health authorities, a systematic review and meta-analysis of the effect of acute stroke units was performed. Clinical trials mentioned in the original Cochrane review were included. In addition, an electronic database search on Medline, Embase, the Cochrane Central Register of Controlled Trials, and Physiotherapy Evidence Database (PEDro) was conducted to identify trials published since 2006. Trials investigating acute stroke units compared to alternative care were eligible for inclusion. Study quality was appraised according to the criteria recommended by Scottish Intercollegiate Guidelines Network (SIGN) and the GRADE system. In the meta-analysis, dichotomous outcomes were estimated by calculating odds ratios (OR) and continuous outcomes were estimated by calculating standardized mean differences. The weight of a study was calculated based on inverse variance.ResultsEvidence from eight trials comparing acute stroke unit and conventional care (general medical ward) were retained for the main synthesis and analysis. The findings from this study were broadly in line with the original Cochrane review: acute stroke units can improve survival and independency, as well as reduce the chance of hospitalization and the length of inpatient stay. The improvement with stroke unit care on mortality was less conclusive and only reached borderline level of significance (OR 0.84, 95% CI 0.70 to 1.00, P = 0.05). This improvement became statistically non-significant (OR 0.87, 95% CI 0.74 to 1.03, P = 0.12) when data from two unpublished trials (Goteborg-Ostra and Svendborg) were added to the analysis. After further also adding two additional trials (Beijing, Stockholm) with very short observation periods (until discharge), the difference between acute stroke units and general medical wards on death remained statistically non-significant (OR 0.86, 95% CI 0.74 to 1.01, P = 0.06). Furthermore, based on figures reported by the clinical trials included in this study, a slightly higher proportion of patients became dependent after receiving care in stroke units than those treated in general medical wards - although the difference was not statistically significant. This result could have an impact on the future demand for healthcare services for individuals that survive a stroke but became dependent on their care-givers.ConclusionsThese findings demonstrate that a well-conducted meta-analysis can produce results that can be of value to policymakers but the choice of inclusion/exclusion criteria and outcomes in this context needs careful consideration. The financing of interventions such as stroke units that increase independency and reduce inpatient stays are worthwhile in a context of an ageing population with increasing care needs. One limitation of this study was the selection of trials published in only four languages: English, French, Dutch and German. This choice was pragmatic in the context of this study, where the objective was to support health authorities in their decision processes.

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Evaluating statistical significance in a meta-analysis by using numerical integration.

Publications

Evaluating statistical significance in a <i>meta</i>-analysis by using numerical integration.

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