Identifying subgroups based on continuous measurements in individual patient data meta-analysis




Poster session 3


Tuesday 18 September 2018 - 12:30 to 14:00

All authors in correct order:

Belias M1, Rovers M2, Reitsma JB3, Debray TPA3, IntHout J1
1 Radboud Institute for Health Sciences, Department for Health Evidence, Radboud University Medical Center Nijmegen, The Netherlands
2 Department for Health Evidence and Department of Operating Rooms, Radboud University Medical Center Nijmegen; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, The Netherlands
3 Julius Center for Health Sciences and Primary Care and Dutch Cochrane Center, University Medical Center Utrecht, The Netherlands
Presenting author and contact person

Presenting author:

Michail Belias

Contact person:

Abstract text
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.

Relevance to patients and consumers: 

We develop and evaluate methods to tailor medical interventions to individual patients.