VP90 Which Matching Adjusted Indirect Comparison Method Is Best?. (2019)

• Record Type: Journal Article
• Authors: Alsop, Jonathan; Pont, Lawrence; Scott, Martin
• Is Part Of: International journal of technology assessment in health care. Volume 35(2019)Supplement 1
• Page Start: 94
• Page End: 95
• Publication Date: 2019
• Subjects: Medical technology -- Periodicals; Technology assessment -- Periodicals; 610.28
• DOI: 10.1017/S0266462319003350
• Languages: English
• ISSNs: 0266-4623

Abstract

Abstract : Introduction: Matching adjusted indirect comparison (MAIC) methods are extremely useful when conducting ITCs, as they reduce baseline imbalances between studies, particularly upon patient characteristics that are confounded with treatment. The standard approach when conducting MAIC is that proposed by Signorovitch et al. (2010). However, there are newer, and potentially better, methods available. Methods: Three different MAIC methods (Signorovitch, Entropy Balancing, Polynomial Weighting) were compared using multiple phase 3 RCTs conducted in Diabetic Retinal Edema. The matching ability of each method was assessed, alongside its ability to avoid large weights (i.e. avoiding high leverage), and maximise effective same size (ESS). Each method's overall ease of use and impact upon estimates of treatment effectiveness were also evaluated. Results: All methods were able to precisely match the aggregate level data. However, the Entropy Balancing and Polynomial Weighting both outperformed the Signorovitch method in terms of having the lowest maximum weights. The Polynomial Weighting provided the highest ESS. The Entropy Balancing method was arguably the most challenging to implement, whilst the Signorovitch method the least. The Polynomial Weighting method appears to provide the greatest flexibility to the user. Conclusions: Whilst the Signorovitch method has become almost synonymous with MAIC, the Entropy Balancing and Polynomial Weighting methods offer potentially superior performance. In the absence of head-to-head trial data, these new MAIC approaches should provide less biased and more precise estimates of comparative effectiveness – ultimately leading to better decision making by regulators and payers.