Simulated treatment comparisons with jackknife pseudo values for estimating population-adjusted marginal treatment effects

Matching-adjusted indirect comparisons (MAIC) and simulated treatment comparisons (STC) are commonly used for indirect treatment comparisons when patient-level data are available for some treatments but not others. However, MAIC can become inefficient when covariate overlap across trials is limited, and STC may fail to estimate marginal (population average) treatment effects required for health technology assessment when non-linear outcome models, such as logistic or survival models, are used. We propose a new STC approach to address these limitations. For settings where a linear model for the treatment effect can be assumed (e.g. log odds ratios or log hazard ratios), we propose to apply STC with jackknife pseudo values of the marginal treatment effect estimate as the dependent variable, rather than the observed outcomes. These pseudo values decompose the marginal treatment effect estimate into individual patient contributions, which can then be regressed on patient-level characteristics to predict marginal treatment effects in alternative target populations. We illustrate the approach using two worked examples involving non-linear outcome models. In simulated trials of continuous, binary, and time-to-event outcomes, the proposed approach showed less bias than traditional STC for non-linear outcome models, and had lower mean squared error than MAIC, both under correct and incorrect specification of baseline covariate functional forms.