  Disagreement remains on what the target estimand should be for
population-adjusted indirect treatment comparisons. This debate is of central
importance for policy-makers and applied practitioners in health technology
assessment. Misunderstandings are based on properties inherent to estimators,
not estimands, and on generalizing conclusions based on linear regression to
non-linear models. Estimators of marginal estimands need not be unadjusted and
may be covariate-adjusted. The population-level interpretation of conditional
estimates follows from collapsibility and does not necessarily hold for the
underlying conditional estimands. For non-collapsible effect measures, neither
conditional estimates nor estimands have a population-level interpretation.
Estimators of marginal effects tend to be more precise and efficient than
estimators of conditional effects where the measure of effect is
non-collapsible. In any case, such comparisons are inconsequential for
estimators targeting distinct estimands. Statistical efficiency should not
drive the choice of the estimand. On the other hand, the estimand, selected on
the basis of relevance to decision-making, should drive the choice of the most
efficient estimator. Health technology assessment agencies make reimbursement
decisions at the population level. Therefore, marginal estimands are required.
Current pairwise population adjustment methods such as matching-adjusted
indirect comparison are restricted to target marginal estimands that are
specific to the comparator study sample. These may not be relevant for
decision-making. Multilevel network meta-regression (ML-NMR) can potentially
target marginal estimands in any population of interest. Such population could
be characterized by decision-makers using increasingly available "real-world"
data sources. Therefore, ML-NMR presents new directions and abundant
opportunities for evidence synthesis.
