BPCPPDYSOE21 – wissenschaftliches Programm
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SOE: Fachverband Physik sozio-ökonomischer Systeme
SOE 2: Networks and Social Dynamics
SOE 2.1: Vortrag
Montag, 22. März 2021, 14:00–14:20, SOEa
Degree irregularity and rank probability bias in network-meta analysis — •Annabel L Davies1 and Tobias Galla1, 2 — 1The University of Manchester, Manchester, United Kingdom — 2Instituto de Fisica Interdisciplinar y Sistemas Complejos, IFISC (CSIC-UIB), Palma de Mallorca, Spain
Network meta-analysis (NMA) is a statistical technique for the comparison of treatment options. The nodes of the network graph are the competing treatments and the edges represent comparisons made between the treatments in the trials. Outcomes of Bayesian NMA include estimates of treatment effects, and the probabilities that each treatment is ranked best, second best and so on. How exactly network topology affects the accuracy and precision of these outcomes is not fully understood. We conduct a simulation study and find that disparity in the number of trials involving different treatments leads to a systematic bias in estimated rank probabilities. This bias is associated with an increased variation in the precision of treatment effect estimates. Using ideas from network theory, we define a measure of `degree irregularity' to quantify asymmetry in the number of studies involving each treatment. Our simulations indicate that more regular networks have more precise treatment effect estimates and smaller bias of rank probabilities. We also find that degree regularity is a better indicator for the accuracy and precision of parameter estimates in NMA than both the total number of studies in a network and the disparity in the number of trials per comparison. Reference: A. L. Davies, T. Galla, Research Synthesis Methods 2020, 1-17, https://doi.org/10.1002/jrsm.1454