The use of continuous data versus binary data in MTC models: A case study in rheumatoid arthritis

Susanne Schmitz,Roisin Adams,Cathal Walsh

doi:10.1186/1471-2288-12-167

Abstract

BackgroundEstimates of relative efficacy between alternative treatments are crucial for decision making in health care. When sufficient head to head evidence is not available Bayesian mixed treatment comparison models provide a powerful methodology to obtain such estimates. While models can be fit to a broad range of efficacy measures, this paper illustrates the advantages of using continuous outcome measures compared to binary outcome measures.MethodsUsing a case study in rheumatoid arthritis a Bayesian mixed treatment comparison model is fit to estimate the relative efficacy of five anti-TNF agents currently licensed in Europe. The model is fit for the continuous HAQ improvement outcome measure and a binary version thereof as well as for the binary ACR response measure and the underlying continuous effect. Results are compared regarding their power to detect differences between treatments.ResultsSixteen randomized controlled trials were included for the analysis. For both analyses, based on the HAQ improvement as well as based on the ACR response, differences between treatments detected by the binary outcome measures are subsets of the differences detected by the underlying continuous effects.ConclusionsThe information lost when transforming continuous data into a binary response measure translates into a loss of power to detect differences between treatments in mixed treatment comparison models. Binary outcome measures are therefore less sensitive to change than continuous measures. Furthermore the choice of cut-off point to construct the binary measure also impacts the relative efficacy estimates.

Highlights

Estimates of relative efficacy between alternative treatments are crucial for decision making in health care
Using a case study in rheumatoid arthritis this paper presents a mixed treatment comparison (MTC) model on a range of efficacy measures illustrating the increased power of continuous outcome measures in such models compared to using binary measures
The MTC models described previously are fit to the data; the continuous model for the improvement in Health Assessment Questionnaire (HAQ) score and the binary model for the American College of Rheumatology (ACR) 20, ACR 50 and ACR 70 outcome measures

Summary

Introduction

Estimates of relative efficacy between alternative treatments are crucial for decision making in health care. When sufficient head to head evidence is not available Bayesian mixed treatment comparison models provide a powerful methodology to obtain such estimates. Meta-analysis has developed to be a widely used tool to combine trials evaluating the same intervention This allows more powerful conclusions when a single study group is too small, and it fits naturally in the Bayesian framework where the inclusion of all available evidence into an analysis is anticipated [1]. While direct evidence is preferable it is often not available due to ethical or financial reasons and MTC models provide a powerful tool to estimate relative efficacy among treatments which are not directly. Using a case study in rheumatoid arthritis this paper presents a MTC model on a range of efficacy measures illustrating the increased power of continuous outcome measures in such models compared to using binary measures. For the presentation of the analysis we follow the checklist suggested by Spiegelhalter et al [3]

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