Network meta‐analysis of rare events using penalized likelihood regression

• Published in: Statistics in medicine Vol. 41; no. 26; pp. 5203 - 5219
• Main Authors: Evrenoglou, Theodoros, White, Ian R., Afach, Sivem, Mavridis, Dimitris, Chaimani, Anna
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 20.11.2022; Wiley Subscription Services, Inc; Wiley-Blackwell
• Subjects: Bias; bias reduction; Computer Simulation; Dermatology; Human health and pathology; Humans; Life Sciences; Likelihood Functions; maximum likelihood estimates; Meta-analysis; multiple treatment meta‐analysis; Network Meta-Analysis; rare endpoints; Research Design; multiple treatment meta-analysis; bias reduction; maximum likelihood estimates; rare endpoints
• ISSN: 0277-6715; 1097-0258
• DOI: 10.1002/sim.9562

Network meta‐analysis (NMA) of rare events has attracted little attention in the literature. Until recently, networks of interventions with rare events were analyzed using the inverse‐variance NMA approach. However, when events are rare the normal approximations made by this model can be poor and effect estimates are potentially biased. Other methods for the synthesis of such data are the recent extension of the Mantel‐Haenszel approach to NMA or the use of the noncentral hypergeometric distribution. In this article, we suggest a new common‐effect NMA approach that can be applied even in networks of interventions with extremely low or even zero number of events without requiring study exclusion or arbitrary imputations. Our method is based on the implementation of the penalized likelihood function proposed by Firth for bias reduction of the maximum likelihood estimate to the logistic expression of the NMA model. A limitation of our method is that heterogeneity cannot be taken into account as an additive parameter as in most meta‐analytical models. However, we account for heterogeneity by incorporating a multiplicative overdispersion term using a two‐stage approach. We show through simulation that our method performs consistently well across all tested scenarios and most often results in smaller bias than other available methods. We also illustrate the use of our method through two clinical examples. We conclude that our “penalized likelihood NMA” approach is promising for the analysis of binary outcomes with rare events especially for networks with very few studies per comparison and very low control group risks.