Efficient, doubly robust estimation of the effect of dose switching for switchers in a randomized clinical trial

Abstract

Motivated by a clinical trial conducted by Janssen Pharmaceutica in which a flexible dosing regimen is compared to placebo, we evaluate how switchers in the treatment arm (i.e., patients who were switched to the higher dose) would have fared had they been kept on the low dose. This is done in order to understand whether flexible dosing is potentially beneficial for them. Simply comparing these patients' responses with those of patients who stayed on the low dose does not likely entail a satisfactory evaluation because the latter patients are usually in a better health condition. Because the available information in the considered trial is too limited to enable a reliable adjustment, we will instead transport data from a fixed dosing trial that has been conducted concurrently on the same target, albeit not in an identical patient population. In particular, we propose an estimator that relies on an outcome model, a model for switching, and a propensity score model for the association between study and patient characteristics. The proposed estimator is asymptotically unbiased if either the outcome or the propensity score model is correctly specified, and efficient (under the semiparametric model where the randomization probabilities are known and independent of baseline covariates) when all models are correctly specified. The proposed method for transporting information from an external study is more broadly applicable in studies where a classical confounding adjustment is not possible due to near positivity violation (e.g., studies where switching takes place in a (near) deterministic manner). Monte Carlo simulations and application to the motivating study demonstrate adequate performance.

Keywords: causal inference; double robustness; estimand; positivity violation; transportability.