Dynamic weighted ordinary least squares (dWOLS) was proposed as a simple analytic tool for estimating optimal adaptive treatment strategies. The approach aimed to combine the double robustness of G-estimation with the ease of implementation of Q-learning, however early methodology was limited to only the continuous outcome/binary treatment setting. In this talk, I will introduce generalized dWOLS, an extension that allowed for continuous-valued treatments to estimate optimal dosing strategies, and demonstrate the approach in estimating an optimal Warfarin dosing rule.

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Efficient, doubly robust estimation of the effect of dose switching for switchers in a randomised clinical trial
Kelly Van Lancker¹, An Vandebosch², Stijn Vansteelandt^1,3
1Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium; ²Janssen R&D, a division of Janssen Pharmaceutica NV, Beerse, Belgium; ³Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom

The interpretation of intention-to-treat analyses of randomised clinical trials is often hindered as a result of noncompliance and treatment switching. This has recently given rise to a vigorous research activity on the identification and estimation of so-called estimands.

Motivated by an ongoing 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 in order to understand whether flexible dosing is potentially beneficial for them. 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 and the available information is too limited to enable a reliable adjustment. In view of this, we will 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 will propose a doubly robust estimator, which relies on an outcome model and a propensity score model for the association between study and patient characteristics. The proposed estimator is easy to evaluate, asymptotically unbiased if either model is correctly specified and efficient (under the restricted semi-parametric model where the randomisation probabilities are known and independent of baseline covariates) when both models are correctly specified. Theoretical properties are also evaluated through Monte Carlo simulations and the method will be illustrated based on the motivating example.

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New causal criteria for decisions making under fairness constraints
Mats Stensrud
Ecole Polytechnique Fédérale de Lausanne, Switzerland

To justify that a decision is fair, causal reasoning is important: we usually evaluate how the decision was made (the causes of the decision) and what would happen if a different was made (the effects of the decision).

Several causal (counterfactual) definitions of fairness have recently been suggested, but these definitions suffer from any of the following caveats: they rely on ill-defined interventions, require identification conditions that are unreasonably strong, can be gamed by decision makers with malicious intentions, or fail to capture arguably reasonable notions of discrimination.

Motivated by the shortcomings of the existing definitions of fairness, we introduce two new causal criteria to prevent discrimination in practice. These criteria can be applied to settings with non-binary and time-varying decisions. We suggest strategies to evaluate whether these criteria hold in observed data and give conditions that allow identification of counterfactual outcomes under new, non-discriminatory decision rules. The interpretation of our criteria is discussed in several examples.