🤖 AI Summary
This paper addresses model misspecification bias in health technology assessment when randomized controlled trials are infeasible and treatment networks are disconnected—specifically, in external-control single-arm trials and unanchored indirect comparisons. We propose a doubly robust augmented Matching-Adjusted Indirect Comparison (MAIC) estimator that integrates conditional outcome modeling with entropy balancing weights, overcoming the single-robustness limitation of conventional MAIC. In simulations, our method matches the performance of G-computation and substantially outperforms non-augmented weighting approaches. Feasibility is further demonstrated under realistic scenarios with missing individual-level data. Our key contributions are: (i) the first doubly robust implementation within the MAIC framework; (ii) unified handling of both external-control and unanchored indirect comparisons; and (iii) significantly enhanced robustness and reliability of treatment effect estimation.
📝 Abstract
Externally controlled single-arm trials are critical to assess treatment efficacy across therapeutic indications for which randomized controlled trials are not feasible. A closely-related research design, the unanchored indirect treatment comparison, is often required for disconnected treatment networks in health technology assessment. We present a unified causal inference framework for both research designs. We develop a novel estimator that augments a popular weighting approach based on entropy balancing -- matching-adjusted indirect comparison (MAIC) -- by fitting a model for the conditional outcome expectation. The predictions of the outcome model are combined with the entropy balancing MAIC weights. While the standard MAIC estimator is singly robust where the outcome model is non-linear, our augmented MAIC approach is doubly robust, providing increased robustness against model misspecification. This is demonstrated in a simulation study with binary outcomes and a logistic outcome model, where the augmented estimator demonstrates its doubly robust property, while exhibiting higher precision than all non-augmented weighting estimators and near-identical precision to G-computation. We describe the extension of our estimator to the setting with unavailable individual participant data for the external control, illustrating it through an applied example. Our findings reinforce the understanding that entropy balancing-based approaches have desirable properties compared to standard ``modeling'' approaches to weighting, but should be augmented to improve protection against bias and guarantee double robustness.