Details of talk
|Title||Issues in Using the Random Effects Approach in Meta-analysis of Sparse Data|
|Presenter||Elasma Milanzi (The University of Melbourne)|
|Session||Biostatistics and Bioinformatics|
The current interest in meta-analysis of count data in which some studies have zero events (sparse data) has led to re-assessment of commonly used meta-analysis methods to establish their validity in such scenarios. The general consensus is that methods which exclude studies with zero events should be avoided. In the family of parametric methods, random effects models come out highly recommended. The random effects approach accounts for the variation in the effect measure between studies by associating the effect measure with a random effects structure (random slope). It is has been suggested that if a parameter is associated with a random effects structure and the distribution of the random effects is misspecified, the estimated parameter can be biased. However, previous studies have concentrated on misspecification of random effects in context where the parameter of interest is not associated with the random effect hence the frequently conveyed message is that misspecification of the random effects distribution is not a big deal. Considering the importance of the random effects approach in meta-analysis of sparse data, we used a simulation study to investigate the impact of misspecification of the distribution of the random effects is on the parameter of interest, the effect measure. Our results indicated that relative bias in the estimated effect could be as high as 30\% and the 95\% confidence interval coverage as low as 0\%. These results send a clear message that misspecification of the distribution of random effects is a big deal when dealing with sparse data. We therefore illustrated a sensitivity analysis and recommend that it should always accompany a random effects approach in meta-analysis of sparse data.