Comparing Methods of Data Synthesis

Background: Cost-effectiveness models should always be amendable to updating once new data on important model parameters become available. However, several methods of synthesizing data exist and the choice of method may affect the cost-effectiveness estimates.

Objectives: To investigate the impact of the different methods of metaanalysis on final estimates of cost effectiveness from a probabilistic Markov model for chronic obstructive pulmonary disease (COPD).

Methods: We compared four different methods to synthesize data for the parameters of a cost-effectiveness model for COPD: frequentist and Bayesian fixed-effects (FE) and random-effects (RE) meta-analyses. These methods were applied to obtain new transition probabilities between stable disease states and new event probabilities.

Results: The four methods resulted in different estimates of probabilities and their standard errors (SE). The effects of using different synthesis techniques were most prominent in the estimation of the SEs. We found up to 9-fold differences in SEs of the exacerbation probabilities and up to almost 3-fold differences in SEs of the transition probabilities. We found that the frequentist FE model produced the lowest SEs, whereas the Bayesian RE model produced the highest for all parameters. The estimates of differences between treatments in total costs, QALYs and cost-effectiveness acceptability curves (CEAC) also varied depending on the synthesis method. The CEAC was 15% lower with a Bayesian RE model than with any of the other models.

Conclusions: Health economic modellers should be aware that the choice of synthesis technique can affect resulting model parameters considerably, which can in turn affect estimates of cost effectiveness and the uncertainty around them.