arXiv subjects

Jack M. Wolf

Publications and source records attributed to Jack M. Wolf.

3 recordsLinked to original sources

arXiv2026

Combining covariate adjustment with information from secondary endpoints to improve precision in randomized trials

Background/Aims: Adjustment for prognostic baseline covariates can improve precision in randomized trials. Previous work has shown that jointly modeling primary and secondary endpoints can yield additional precision by borrowing information across endpoints. We investigated whether these approaches can be combined to achieve efficiency gains beyond those obtained through covariate adjustment alone. Methods: We extended a previously proposed one-factor structural equation modeling framework for borrowing information from secondary endpoints to incorporate baseline covariates while retaining the average treatment effect on the primary endpoint as the estimand. To mitigate sensitivity to model misspecification, we combined this estimator with a conventional covariate-adjusted estimator using cross-validated model averaging. We evaluated operating characteristics in simulations and applied the methods to a randomized trial of very low versus normal nicotine content cigarettes. Results: When the structural equation model was correctly specified, endpoint borrowing improved efficiency beyond conventional covariate adjustment across all simulated settings. Model misspecification could induce bias and undercoverage. Model averaging reduced bias and improved coverage relative to the structural equation model estimator, although coverage remained imperfect under severe misspecification. In the trial application, the model-averaged estimate was 21% more precise than the unadjusted estimate and 13% more precise than covariate adjustment alone. Conclusion: Secondary endpoints can contribute meaningful information about the average treatment effect on a primary endpoint even after baseline covariates have been incorporated. These gains require stronger assumptions than conventional covariate adjustment; model averaging provides a practical compromise between efficiency and robustness.

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arXiv2026

Nonparametric Estimation of Optimal Stochastic Just-In-Time Adaptive Interventions for Distal Outcomes

Mobile and wearable technologies enable the delivery of just-in-time adaptive interventions (JITAIs) -- interventions that adapt treatment delivery to an individual's rapidly changing internal state and context in real-time, real-world settings. Estimating optimal JITAIs, however, remains challenging because these studies often involve dozens of decision points per individual, and existing methods can produce unstable and irregular estimators with substantial bias and slow convergence rates. Advanced reinforcement learning approaches may be difficult to interpret and often target proximal, discounted outcomes rather than the distal end-of-study outcomes that define long-term success in many behavioral and clinical studies. To address these challenges, we develop a nonparametrically efficient estimator of the regimen-response curve for distal outcomes under a class of stochastic policies and introduce a data-adaptive tilting procedure to stabilize estimation in settings with many decision points. We show that the estimated regimen-response curve converges weakly to a Gaussian process, enabling simultaneous confidence bands, and we derive asymptotic theory for the optimizer of the curve, thereby enabling inference for the learned optimal stochastic policy. These developments provide a unified framework for estimation, inference, and optimization of stochastic JITAIs for distal outcomes.

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arXiv2025

Jointly modeling multiple endpoints for efficient treatment effect estimation in randomized controlled trials

Randomized controlled trials are the gold standard for evaluating the efficacy of an intervention. However, there is often a trade-off between selecting the most scientifically relevant primary endpoint versus a less relevant, but more powerful, endpoint. For example, in the context of tobacco regulatory science many trials evaluate cigarettes per day as the primary endpoint instead of abstinence from smoking due to limited power. Additionally, it is often of interest to consider subgroup analyses to answer additional questions; such analyses are rarely adequately powered. In practice, trials often collect multiple endpoints. Heuristically, if multiple endpoints demonstrate a similar treatment effect we would be more confident in the results of this trial. However, there is limited research on leveraging information from secondary endpoints besides using composite endpoints which can be difficult to interpret. In this paper, we develop an estimator for the treatment effect on the primary endpoint based on a joint model for primary and secondary efficacy endpoints. This estimator gains efficiency over the standard treatment effect estimator when the model is correctly specified but is robust to model misspecification via model averaging. We illustrate our approach by estimating the effect of very low nicotine content cigarettes on the proportion of Black people who smoke who achieve abstinence and find our approach reduces the standard error by 27%.

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