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Network meta‐analysis of longitudinal data using fractional polynomials
Author(s) -
Jansen J. P.,
Vieira M. C.,
Cope S.
Publication year - 2015
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6492
Subject(s) - meta analysis , measure (data warehouse) , randomized controlled trial , random effects model , time point , computer science , mathematics , network analysis , statistics , medicine , data mining , philosophy , physics , quantum mechanics , aesthetics
Network meta‐analysis of randomized controlled trials (RCTs) are often based on one treatment effect measure per study. However, many studies report data at multiple time points. Furthermore, not all studies measure the outcomes at the same time points. As an alternative to a network meta‐analysis based on a synthesis of the results at one time point, a network meta‐analysis method is presented that allows for the simultaneous analysis of outcomes at multiple time points. The development of outcomes over time of interventions compared in an RCT is modeled with fractional polynomials, and the differences between the parameters of these polynomials within a trial are synthesized across studies with a Bayesian network meta‐analysis. The proposed models are illustrated with an analysis of RCTs evaluating interventions for osteoarthritis of the knee. Fixed and random effects second order fractional polynomials were applied to the case study. Network meta‐analysis with models that represent the treatment effects in terms of several parameters using fractional polynomials can be considered a useful addition to models for network meta‐analysis of repeated measures previously proposed. When RCTs report treatment effects at multiple follow‐up times, these models can be used to synthesize the results even if reporting times differ across the studies. Copyright © 2015 John Wiley & Sons, Ltd.