Outlier detection methods for meta-analyses of site-specific effect estimates from a multi-site network
| dc.contributor.author | Halder, Henry R. | |
| dc.contributor.examiningcommittee | Rabbani, Rasheda (George & Fay Yee Centre for Healthcare Innovation) | |
| dc.contributor.examiningcommittee | Gerstein, Aleeza (Microbiology) | |
| dc.contributor.examiningcommittee | Schneider-Lindner, Verena (Heidelberg University) | |
| dc.contributor.supervisor | Lix, Lisa M. | |
| dc.date.accessioned | 2023-08-28T19:36:23Z | |
| dc.date.available | 2023-08-28T19:36:23Z | |
| dc.date.issued | 2023-08-21 | |
| dc.date.submitted | 2023-08-23T04:05:41Z | en_US |
| dc.degree.discipline | Community Health Sciences | en_US |
| dc.degree.level | Master of Science (M.Sc.) | |
| dc.description.abstract | Introduction: Data privacy legislation in Canada prohibits patient-level administrative health data from crossing jurisdictional boundaries. Accordingly, multi-site research networks often conduct distributed analyses and pool site-specific effect estimates (EEs) using meta-analysis models. Rare outcomes and heterogeneity in site-specific EEs can produce potential outliers that may bias pooled EEs. Limited research has compared outlier detection methods and the impact of potential outliers on meta-analysis results. Purpose and Objectives: The research purpose was to examine outlier detection methods for meta-analyses of site-specific EEs from a multi-site network. The objectives were to: 1) compare outlier detection methods for random-effects meta-analysis (REM) models, and 2) apply these methods to site-specific EEs from systematically selected real-world meta-analyses. Methods: We compared studentized residual estimates (StdR), relative change in pooled EE variance (RCPEV), relative change in estimated between-site variance (RCEBV), and model-based mean-shift method (MMS) using computer simulation. EEs were simulated assuming a normal distribution. Accuracy, misclassification error (ME), and F-1 score were assessed using random-effects analysis of variance models. We systematically selected meta-analyses conducted by investigators from the Canadian Network for Observational Drug Effect Studies (CNODES), applied outlier detection methods, and assessed the impact of potential outliers on REM results. Results: StdR had the highest accuracy (median: 89.9%) and lowest ME (median: 10.2%). RCPEV was the most consistent in all metrics. For StdR, the number of sites explained 95.1% and 93.0% of the variation in accuracy and ME values. For RCEBV and MMS, between-site variance described the most variation in accuracy and ME values. StdR and RCPEV were most sensitive to detect potential outliers in re-analyses of 39 published CNODES meta-analyses. Heterogeneity in site-specific EEs was reduced to zero in two-thirds of the meta-analyses when potential outliers were removed, and the precision of pooled EEs increased. Conclusions: StdR and RCPEV outperformed RCEBV and MMS in outlier detection. The number of sites and between-site variance explained the most variation in performance metrics for all methods. Excluding potential outliers from published meta-analyses, substantially reduced heterogeneity in site-specific EEs and increased the precision of pooled EEs. | |
| dc.description.note | October 2023 | |
| dc.identifier.uri | http://hdl.handle.net/1993/37503 | |
| dc.language.iso | eng | |
| dc.rights | open access | en_US |
| dc.subject | Meta-analysis models | |
| dc.subject | Outlier detection methods | |
| dc.title | Outlier detection methods for meta-analyses of site-specific effect estimates from a multi-site network | |
| dc.type | master thesis | en_US |
| local.subject.manitoba | no |