Background: Individual patient data meta-analysis (IPD-MA) is increasingly used to analyse heterogeneity of treatment effects. Linearity assumptions are often made when examining subgroups based on continuous measurements. However, several more flexible methods exist.
Objectives: Our goal is to illustrate, critically review and compare state of the art methods on subgroup effects identification in IPD-MA, based on continuous measurements.
Methods: We reviewed META-STEPP, generalized additive mixed effects models, (multi-level) regression models involving fractional polynomials or splines and several tree-based approaches.
We applied the methods above to two empirical examples: prescription of antibiotics in children with otitis media and anti-platelet treatment in secondary stroke prevention.
Results: We will provide treatment effect plots to visualize subgroup effects within and across studies.
Conclusions: We will explain the advantages and limitations of the aforementioned methods.
Patient or healthcare consumer involvement: Our research will provide insight into recently developed statistical methods, to make better use of existing data for the benefit of individual patients. We aim to improve patient-centered health care.