Using the counterfactual framework to estimate non-intention-to-treat estimands in randomised controlled trials: A...

Randomised controlled trials (RCTs) commonly estimate intention-to-treat (ITT) estimands. However, when nonadherence to assigned treatment occurs, ITT estimands reflect the effect of being offered treatment, rather than adhering to it and thus are less useful for clinical decision-making.

Summarise current literature on non-ITT estimands used in RCTs to estimate the effect of adhering to treatment and counterfactual framework estimators that are employed to obtain these non-ITT estimands.

We conducted a methodological scoping review and searched MEDLINE and EMBASE from inception to March 2024, with forward and backward citation searching. Eligible records discussed counterfactual framework estimators to obtain non-ITT estimands in RCTs or simulation studies based on empirical RCTs.

From 746 records screened, our search identified 56 eligible records. 47 (84 %) described specific estimators for addressing nonadherence and 9 (16 %) described frameworks for overall methodological approach. In the 47 estimator records, 51 non-ITT estimands were reported, including poorly defined estimands (n = 21, 41 %), complier average causal effects (n = 17, 33 %), switching-adjusted estimands (n = 7, 14 %), and per-protocol estimands (n = 6, 12 %). There were 83 estimator applications, including inverse probability weighting (n = 22, 27 %), instrumental variables (IVs) for time-varying treatments (n = 15, 18 %), and standard IVs (n = 14, 17 %). Other estimators included doubly-robust estimators using machine learning.

Non-ITT estimands in RCTs tended to be poorly defined and lacked relevance for clinical decision-making. Further research on using estimators from the counterfactual framework to estimate well-defined estimands, particularly per-protocol estimands, is needed to support greater uptake in practice and policy decision making.