Background: Network meta-analyses using individual participant data (IPD-NMAs) have been increasingly used to compare the effects of multiple interventions. Although there have been many studies on statistical methods for IPD-NMAs, it is unclear whether there are statistical defects in published IPD-NMAs and whether the reporting of statistical analyses has improved. This study aimed to investigate statistical methods used and assess the reporting and methodological quality of IPD-NMAs.
Methods: We searched four bibliographic databases to identify published IPD-NMAs. The methodological quality was assessed using AMSTAR-2 and reporting quality assessed based on PRISMA-IPD and PRISMA-NMA. We performed stratified analyses and correlation analyses to explore the factors that might affect quality.
Results: We identified 21 IPD-NMAs. Only 23.8% of the included IPD-NMAs reported statistical techniques used for missing participant data, 42.9% assessed the consistency, and none assessed the transitivity. None of the included IPD-NMAs reported sources of funding for trials included, only 9.5% stated pre-registration of protocols, and 28.6% assessed the risk of bias in individual studies. For reporting quality, compliance rates were lower than 50.0% for more than half of the items. Less than 15.0% of the IPD-NMAs reported data integrity, presented the network geometry, or clarified risk of bias across studies. IPD-NMAs with statistical or epidemiological authors often better assessed the inconsistency (P = 0.017). IPD-NMAs with a priori protocol were associated with higher reporting quality in terms of search (P = 0.046), data collection process (P = 0.031), and syntheses of results (P = 0.006).
Conclusions: The reporting of statistical methods and compliance rates of methodological and reporting items of IPD-NMAs were suboptimal. Authors of future IPD-NMAs should address the identified flaws and strictly adhere to methodological and reporting guidelines.
Keywords: Individual participant data; Methodological quality; Network meta-analysis; Reporting quality; Statistical analysis.