  Anchored covariate-adjusted indirect comparisons inform reimbursement
decisions where there are no head-to-head trials between the treatments of
interest, there is a common comparator arm shared by the studies, and there are
patient-level data limitations. Matching-adjusted indirect comparison (MAIC) is
the most widely used covariate-adjusted indirect comparison method. MAIC has
poor precision and is inefficient when the effective sample size after
weighting is small. A modular extension to MAIC, termed two-stage
matching-adjusted indirect comparison (2SMAIC), is proposed. This uses two
parametric models. One estimates the treatment assignment mechanism in the
study with individual patient data (IPD), the other estimates the trial
assignment mechanism. The resulting weights seek to balance covariates between
treatment arms and across studies. A simulation study provides
proof-of-principle in an indirect comparison performed across two randomized
trials and explores the use of weight truncation in combination with MAIC for
the first time. Despite enforcing randomization and knowing the true treatment
assignment mechanism in the IPD trial, 2SMAIC yields improved precision and
efficiency with respect to MAIC in all scenarios, while maintaining similarly
low levels of bias. The two-stage approach is effective when sample sizes in
the IPD trial are low, as it controls for chance imbalances in prognostic
baseline covariates between study arms. It is not as effective when overlap
between the trials' target populations is poor and the extremity of the weights
is high. In these scenarios, truncation leads to substantial precision and
efficiency gains but induces considerable bias. The combination of a two-stage
approach with truncation produces the highest precision and efficiency
improvements.
