arXiv subjects

Eleanor Pullenayegum

Publications and source records attributed to Eleanor Pullenayegum.

5 recordsLinked to original sources

arXiv2025

Inverse-intensity weighted generalized estimating equations with irregularly measured longitudinal data and informative dropout

Longitudinal data are commonly encountered in biomedical research, including randomized trials and retrospective cohort studies. Subjects are typically followed over a period of time and may be scheduled for follow-up at pre-determined time points. However, subjects may miss their appointments or return at non-specified times, leading to irregularity in the visit process. IIW-GEEs have been developed as one method to account for this irregularity, whereby estimates from a visit intensity model are used as weights in a GEE model with an independent correlation structure. We show that currently available methods can be biased for situations in which the health outcome of interest may influence a subject's dropout from the study. We have extended the IIW-GEE framework to adjust for informative dropout and have demonstrated via simulation studies that this bias can be significantly reduced. We have illustrated this method using the STAR*D clinical trial data, and observed that the disease trajectory was generally overestimated when informative dropout was not accounted for.

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arXiv2025

Estimating Optimal Dynamic Treatment Regimes Using Irregularly Observed Data: A Target Trial Emulation and Bayesian Joint Modeling Approach

An optimal dynamic treatment regime (DTR) is a sequence of decision rules aimed at providing the best course of treatments individualized to patients. While conventional DTR estimation uses longitudinal data, such data can also be irregular, where patient-level variables can affect visit times, treatment assignments and outcomes. In this work, we first extend the target trial framework - a paradigm to estimate statistical estimands specified under hypothetical randomized trials using observational data - to the DTR context; this extension allows treatment regimes to be defined with intervenable visit times. We propose an adapted version of G-computation marginalizing over random effects for rewards that encapsulate a treatment strategy's value. To estimate components of the G-computation formula, we then articulate a Bayesian joint model to handle correlated random effects between the outcome, visit and treatment processes. We show via simulation studies that, in the estimation of regime rewards, failure to account for the observational treatment and visit processes produces bias which can be removed through joint modeling. We also apply our proposed method on data from INSPIRE 2 and 3 studies to estimate optimal injection cycles of Interleukin 7 to treat HIV-infected individuals.

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arXiv2024

Bias in mixed models when analysing longitudinal data subject to irregular observation: when should we worry about it and how can recommended visit intervals help in specifying joint models when needed?

In longitudinal studies using routinely collected data, such as electronic health records (EHRs), patients tend to have more measurements when they are unwell; this informative observation pattern may lead to bias. While semi-parametric approaches to modelling longitudinal data subject to irregular observation are known to be sensitive to misspecification of the visit process, parametric models may provide a more robust alternative. Robustness of parametric models on the outcome alone has been assessed under the assumption that the visit intensity is independent of the time since the last visit, given the covariates and random effects. However, this assumption of a memoryless visit process may not be realistic in the context of EHR data. In a special case which includes memory embedded into the visit process, we derive an expression for the bias in parametric models for the outcome alone and use this to identify factors that lead to increasing bias. Using simulation studies, we show that this bias is often small in practice. We suggest diagnostics for identifying the specific cases when the outcome model may be susceptible to meaningful bias, and propose a novel joint model of the outcome and visit processes that can eliminate or reduce the bias. We apply these diagnostics and the joint model to a study of juvenile dermatomyositis. We recommend that future studies using EHR data avoid relying only on the outcome model and instead first evaluate its appropriateness with our proposed diagnostics, applying our proposed joint model if necessary.

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arXiv2024

Why recommended visit intervals should be extracted when conducting longitudinal analyses using electronic health record data: examining visit mechanism and sensitivity to assessment not at random

Electronic health records (EHRs) provide an efficient approach to generating rich longitudinal datasets. However, since patients visit as needed, the assessment times are typically irregular and may be related to the patient's health. Failing to account for this informative assessment process could result in biased estimates of the disease course. In this paper, we show how estimation of the disease trajectory can be enhanced by leveraging an underutilized piece of information that is often in the patient's EHR: physician-recommended intervals between visits. Specifically, we demonstrate how recommended intervals can be used in characterizing the assessment process, and in investigating the sensitivity of the results to assessment not at random (ANAR). We illustrate our proposed approach in a clinic-based cohort study of juvenile dermatomyositis (JDM). In this study, we found that the recommended intervals explained 78% of the variability in the assessment times. Under a specific case of ANAR where we assumed that a worsening in disease led to patients visiting earlier than recommended, the estimated population average disease activity trajectory was shifted downward relative to the trajectory assuming assessment at random. These results demonstrate the crucial role recommended intervals play in improving the rigour of the analysis by allowing us to assess both the plausibility of the AAR assumption and the sensitivity of the results to departures from this assumption. Thus, we advise that studies using irregular longitudinal data should extract recommended visit intervals and follow our procedure for incorporating them into analyses.

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arXiv2020

A Bayesian response-adaptive dose finding and comparative effectiveness trial

Aims: Combinations of treatments can offer additional benefit over the treatments individually. However, trials of these combinations are lower priority than the development of novel therapies, which can restrict funding, timelines and patient availability. This paper develops a novel trial design to facilitate the evaluation of novel combination therapies that combines elements of phase II and phase III trials. Methods: This trial uses response adaptive randomisation to increase the information collected about successful novel drug combinations and Bayesian dose-response modelling to undertake a comparative-effectiveness analysis for the most successful dose combination against a relevant comparator. We used simulation methods to evaluate the probability of selecting the correct optimal dose combination, the operating characteristics and predictive power of this design for a trial in pain management and sedation in paediatric emergency departments. Results: With 410 participants, 5 interim updates of the randomisation ratio and a probability of effectiveness of 0.93, 0.88 and 0.83 for the three dose combinations, we have an 83% chance of randomising the largest number of patients to the drug with the highest probability of effectiveness. Based on this adaptive randomisation procedure, the comparative effectiveness analysis has a type I error of less than 5% and a 93% chance of correctly concluding non-inferiority when the probability of effectiveness for the optimal combination therapy is 0.9. In this case, the trial has a 77% chance of meeting its dual aims of dose finding and comparative effectiveness. Finally, the Bayesian predictive power of the trial is over 90%. Conclusion: The proposed trial has high potential to meet the dual study objectives within a feasible level of recruitment, minimising the administrative burden and recruitment time for a trial.

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