arXiv subjects

Robyn L. McClelland

Publications and source records attributed to Robyn L. McClelland.

2 recordsLinked to original sources

arXiv2026

A joint model of the individual mean and within-subject variability of a longitudinal outcome with a competing risks time-to-event outcome

Motivated by a growing body of research emphasizing the importance of modeling within-subject (WS) variability in longitudinal biomarkers and its association with health outcomes, this paper proposes a semiparametric joint model for both the mean and WS variability of a longitudinal biomarker, jointly with competing-risk time-to-event outcomes. We derive an expectation-maximization algorithm for parameter estimation and a profile-likelihood method for standard error estimation and inference, which allows time-dependent covariates and general forms of the latent association structure. Furthermore, we optimize the implementation of our joint model when the survival submodel includes only time-independent baseline covariates and shared random effects, allowing it to scale effectively to biobank-scale data involving tens of thousands of subjects. Our method demonstrates satisfactory performance in simulations, whereas classical joint models that assume homogeneous WS variability may suffer from substantial estimation bias, invalid inference, and inferior prediction when confronted with heterogeneous WS variability. We illustrate the utility of our method using the Multi-Ethnic Study of Atherosclerosis (MESA) cohort. Our analysis demonstrates that associations between WS blood pressure variability and cardiovascular outcomes, previously observed in clinical trials involving relatively homogeneous populations, extend to a more ethnically diverse and generally healthier cohort, and that explicitly modeling heterogeneous WS variability substantially enhances risk discrimination. A user-friendly R package, \textbf{JMH}, has been developed for the proposed shared random effects model with efficient implementation and is publicly available on the Comprehensive R Archive Network https://CRAN.R-project.org/package=JMH.

stat.ME↗

arXiv2021

Semi-parametric estimation of biomarker age trends with endogenous medication use in longitudinal data

In cohort studies, non-random medication use can pose barriers to estimation of the natural history trend in a mean biomarker value (namely, the association between a predictor of interest and a biomarker outcome that would be observed in the absence of biomarker-specific treatment). Common causes of treatment and outcomes are often unmeasured, obscuring our ability to easily account for medication use with commonly invoked assumptions such as ignorability. Further, absent some variable satisfying the exclusion restriction, use of instrumental variable approaches may be difficult to justify. Heckman's hybrid model with structural shift (sometimes referred to less specifically as the treatment effects model) can be used to correct endogeneity bias via a homogeneity assumption (i.e., that average treatment effects do not vary across covariates) and parametric specification of a joint model for the outcome and treatment. In recent work, we relaxed the homogeneity assumption by allowing observed covariates to serve as treatment effect modifiers. While this method has been shown to be reasonably robust in settings of cross-sectional data, application of this methodology to settings of longitudinal data remains unexplored. We demonstrate how the assumptions of the treatment effects model can be extended to accommodate clustered data arising from longitudinal studies. Our proposed approach is semi-parametric in nature in that valid inference can be obtained without the need to specify the longitudinal correlation structure. As an illustrative example, we use data from the Multi-Ethnic Study of Atherosclerosis to evaluate trends in low-density lipoprotein by age and gender. We confirm that our generalization of the treatment effects model can serve as a useful tool to uncover natural history trends in longitudinal data that are obscured by endogenous treatment.

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