An empirical assessment of meta-analysis estimates from multi-level studies

A conventional meta-analysis may be performed using studies which are available at individual patient level (IPD) or aggregate level (AD). Presently however, meta-analysis that combine the two levels of studies is increasingly common. The implications of utilising different levels of data on the ove...

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Bibliographic Details
Main Authors: Misran, Nurul Afiqah, Nik Idris, Nik Ruzni
Format: Conference or Workshop Item
Language:English
English
Published: 2015
Subjects:
Online Access:http://irep.iium.edu.my/45232/
http://irep.iium.edu.my/45232/
http://irep.iium.edu.my/45232/1/IREP.pdf
http://irep.iium.edu.my/45232/3/conference_program-_kuala_lumpur_oct-ver06.pdf
Description
Summary:A conventional meta-analysis may be performed using studies which are available at individual patient level (IPD) or aggregate level (AD). Presently however, meta-analysis that combine the two levels of studies is increasingly common. The implications of utilising different levels of data on the overall estimates have not been fully explored. Objective: This study examined the efficacy of the estimates of overall treatment effect from AD, IPD and the mixed AD: IPD studies, and investigated how they differ from the true treatment effect. Additionally, this study investigated the influence of the ratio of AD: IPD on the precision of the overall treatment effects estimates. The bias, root mean-square-error (RMSE) and coverage probability were used to assess the efficiency of the overall estimates. Results: The results showed that the IPD meta-analysis produced better estimates in terms of RMSE compared to AD meta-analysis and the mixed AD:IPD meta-analysis. For the cases where both the AD and IPD studies were available, our findings showed that the combined AD : IPD data produced better estimates, in terms of precision, than utilising the AD alone. Conclusion: It is therefore recommended that available IPD should always be included in a conventional meta-analysis using summary level data as significant