arXiv subjects

Jeremiah Jones

Publications and source records attributed to Jeremiah Jones.

3 recordsLinked to original sources

arXiv2026

Optimizing Efficiency and Convergence in MMRM: Practical Considerations for Longitudinal Data Analysis

Mixed models for repeated measures (MMRM) are a popular method for analyzing longitudinal data in clinical trials. However, practical challenges, such as small sample sizes, large numbers of time points, and selection of variance-covariance structure for within-subject errors, often present barriers to model convergence and valid inference. This article evaluates different options for using MMRM in different scenarios through extensive simulation studies and an application on diabetes trial data. We demonstrate that the empirical bias-reduced coefficient covariance adjustment with the heterogeneous autoregressive covariance structure yields near-nominal coverage with high convergence rates for moderate and large sample designs. For small sample sizes, the simple model with baseline covariates and treatment by time point interaction achieves good efficiency and high probability of convergence. Based on these results, we provide practitioners with actionable guidance for applying MMRM to clinical trial data.

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arXiv2022

Valid post-selection inference in Robust Q-learning

Q-learning facilitates the development of an optimal adaptive treatment strategy through stagewise regression on a pre-specified set of tailoring variables and confounders. Semiparametric robust Q-learning eliminates the residual confounding that can occur when parametric working models for confounding influences are misspecified. However, in the presence of many potential tailoring variables, constructing an optimal adaptive treatment strategy using either approach may lead to including extraneous variables that contribute little or no benefit while increasing implementation costs, thereby placing an undue burden on patients. Using data-driven selection processes to identify a smaller set of informative prognostic factors is straightforward; however, proper statistical inference must account for this selection process. In this paper, we adapt the Universal Post-Selection Inference (UPoSI) procedure to the semiparametric Robust Q-learning method. UPoSI, introduced for use with linear models, allows for very general variable selection mechanisms. Our approach addresses the unique challenges stemming from the use of UPoSI with semiparametric multistage decision methods. Theoretical and simulation results demonstrate the validity of the proposed confidence regions. We illustrate our proposed methods through an application to adaptive treatment strategy estimation for substance abuse.

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arXiv2021

Causal Mediation Analysis: Selection with Asymptotically Valid Inference

Researchers are often interested in learning not only the effect of treatments on outcomes, but also the pathways through which these effects operate. A mediator is a variable that is affected by treatment and subsequently affects outcome. Existing methods for penalized mediation analyses may lead to ignoring important mediators and either assume that finite-dimensional linear models are sufficient to remove confounding bias, or perform no confounding control at all. In practice, these assumptions may not hold. We propose a method that considers the confounding functions as nuisance parameters to be estimated using data-adaptive methods. We then use a novel regularization method applied to this objective function to identify a set of important mediators. We derive the asymptotic properties of our estimator and establish the oracle property under certain assumptions. Asymptotic results are also presented in a local setting which contrast the proposal with the standard adaptive lasso. We also propose a perturbation bootstrap technique to provide asymptotically valid post-selection inference for the mediated effects of interest. The performance of these methods will be discussed and demonstrated through simulation studies.

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