Review and comparison of methodologies for indirect comparison of clinical trial results: an illustration with ranibizumab and aflibercept

Aim: To demonstrate the potential of fourth-order polynomials within a non-linear optimisation framework for matching-adjusted indirect comparison (MAIC). Materials & methods: Simulated individual patient data were reweighted via fourth-order polynomials (polyMAIC) to match aggregate-level data across multiple baseline characteristics. The polyMAIC approach employed pre-specified matching tolerances and maximum allowable weights. Matching performance against aggregate-level targets was assessed, and also compared against the current industry-standard MAIC approach (Signorovitch). Results: The polyMAIC method matched aggregate-level targets within pre-specified tolerances. Effective sample sizes were either similar to or somewhat higher than those obtained from the Signorovitch method. Performance gains from polyMAIC tended to increase as matching complexity increased. Conclusion: PolyMAIC incorporates greater flexibility than the industry-standard MAIC approach and demonstrates matching potential.

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Review and comparison of methodologies for indirect comparison of clinical trial results: an illustration with ranibizumab and aflibercept

Abstract: Weight optimization and regression methods are useful options to adjust for more than one baseline characteristic.

Cited by 4 publications

References 20 publications

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Matching-adjusted indirect comparison via a polynomial-based non-linear optimization method

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