Mixed-Effects Poisson Regression Models for Meta-Analysis of Follow-Up Studies with Constant or Varying Durations
- 26 January 2009
- journal article
- research article
- Published by Walter de Gruyter GmbH in The International Journal of Biostatistics
- Vol. 5 (1)
- https://doi.org/10.2202/1557-4679.1168
Abstract
We present a framework for meta-analysis of follow-up studies with constant or varying duration using the binary nature of the data directly. We use a generalized linear mixed model framework with the Poisson likelihood and the log link function. We fit models with fixed and random study effects using Stata for performing meta-analysis of follow-up studies with constant or varying duration. The methods that we present are capable of estimating all the effect measures that are widely used in such studies such as the Risk Ratio, the Risk Difference ( in case of studies with constant duration), as well as the Incidence Rate Ratio and the Incidence Rate Difference ( for studies of varying duration). The methodology presented here naturally extends previously published methods for meta-analysis of binary data in a generalized linear mixed model framework using the Poisson likelihood. Simulation results suggest that the method is uniformly more powerful compared to summary based methods, in particular when the event rate is low and the number of studies is small. The methods were applied in several already published meta-analyses with very encouraging results. The methods are also directly applicable to individual patients' data offering advanced options for modeling heterogeneity and confounders. Extensions of the models for more complex situations, such as competing risks models or recurrent events are also discussed. The methods can be implemented in standard statistical software and illustrative code in Stata is given in the appendix.Keywords
This publication has 19 references indexed in Scilit:
- Why add anything to nothing? The arcsine difference as a measure of treatment effect in meta‐analysis with zero cellsStatistics in Medicine, 2008
- Mixed‐effects Poisson regression analysis of adverse event reports: The relationship between antidepressants and suicideStatistics in Medicine, 2008
- Bias of estimates of the number needed to treatStatistics in Medicine, 2005
- A latent normal distribution model for analysing ordinal responses with applications in meta-analysisStatistics in Medicine, 2004
- What to add to nothing? Use and avoidance of continuity corrections in meta‐analysis of sparse dataStatistics in Medicine, 2004
- Quantifying heterogeneity in a meta-analysisStatistics in Medicine, 2002
- Bayesian random effects meta‐analysis of trials with binary outcomes: methods for the absolute risk difference and relative risk scalesStatistics in Medicine, 2002
- Issues in the selection of a summary statistic for meta‐analysis of clinical trials with binary outcomesStatistics in Medicine, 2002
- How should meta‐regression analyses be undertaken and interpreted?Statistics in Medicine, 2002
- Vaccines for preventing anthraxEmergencias, 1998