arXiv subjects

Jessica K. Barrett

Publications and source records attributed to Jessica K. Barrett.

14 recordsLinked to original sources

arXiv2025

Bayesian semiparametric modelling of biomarker variability in joint models

There is growing interest in the role of within-individual variability (WIV) in biomarker trajectories for assessing disease risk and progression. A trajectory-based definition that has attracted recent attention characterises WIV as the curvature-based roughness of the latent biomarker trajectory (TB-WIV). To rigorously evaluate the association between TB-WIV and clinical outcomes and to perform dynamic risk prediction, joint models for longitudinal and time-to-event data (JM) are necessary. However, specifying the longitudinal trajectory is critical in this framework and poses methodological challenges. In this work, we investigate three Bayesian semiparametric approaches for longitudinal modelling and TB-WIV estimation within the JM framework to improve stability and accuracy over existing approaches. Two key methods are newly introduced: one based on Bayesian penalised splines (P-splines) and another on functional principal component analysis (FPCA). Using extensive simulation studies, we compare their performance under two important TB-WIV definitions against established approaches. Our results demonstrate overall inferential and predictive advantages of the proposed P-spline and FPCA-based approaches while also providing insights that guide method choice and interpretation of inference results. The proposed approaches are applied to data from the UK Cystic Fibrosis Registry, where, for the first time, we identify a significant positive association between lung function TB-WIV and mortality risk in patients with cystic fibrosis and demonstrate improved predictive performance for survival.

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arXiv2025

A Bayesian joint model of multiple longitudinal and categorical outcomes with application to multiple myeloma using permutation-based variable importance

Joint models have proven to be an effective approach for uncovering potentially hidden connections between various types of outcomes, mainly continuous, time-to-event, and binary. Typically, longitudinal continuous outcomes are characterized by linear mixed-effects models, survival outcomes are described by proportional hazards models, and the link between outcomes are captured by shared random effects. Other modeling variations include generalized linear mixed-effects models for longitudinal data and logistic regression when a binary outcome is present, rather than time until an event of interest. However, in a clinical research setting, one might be interested in modeling the physician's chosen treatment based on the patient's medical history to identify prognostic factors. In this situation, there are often multiple treatment options, requiring the use of a multiclass classification approach. Inspired by this context, we develop a Bayesian joint model for longitudinal and categorical data. In particular, our motivation comes from a multiple myeloma study, in which biomarkers display nonlinear trajectories that are well captured through bi-exponential submodels, where patient-level information is shared with the categorical submodel. We also present a variable importance strategy to rank prognostic factors. We apply our proposal and a competing model to the multiple myeloma data, compare the variable importance and inferential results for both models, and illustrate patient-level interpretations using our joint model.

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arXiv2024

A Bayesian joint model of multiple nonlinear longitudinal and competing risks outcomes for dynamic prediction in multiple myeloma: joint estimation and corrected two-stage approaches

Predicting cancer-associated clinical events is challenging in oncology. In Multiple Myeloma (MM), a cancer of plasma cells, disease progression is determined by changes in biomarkers, such as serum concentration of the paraprotein secreted by plasma cells (M-protein). Therefore, the time-dependent behaviour of M-protein and the transition across lines of therapy (LoT) that may be a consequence of disease progression should be accounted for in statistical models to predict relevant clinical outcomes. Furthermore, it is important to understand the contribution of the patterns of longitudinal biomarkers, upon each LoT initiation, to time-to-death or time-to-next-LoT. Motivated by these challenges, we propose a Bayesian joint model for trajectories of multiple longitudinal biomarkers, such as M-protein, and the competing risks of death and transition to next LoT. Additionally, we explore two estimation approaches for our joint model: simultaneous estimation of all parameters (joint estimation) and sequential estimation of parameters using a corrected two-stage strategy aiming to reduce computational time. Our proposed model and estimation methods are applied to a retrospective cohort study from a real-world database of patients diagnosed with MM in the US from January 2015 to February 2022. We split the data into training and test sets in order to validate the joint model using both estimation approaches and make dynamic predictions of times until clinical events of interest, informed by longitudinally measured biomarkers and baseline variables available up to the time of prediction.

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arXiv2024

Bayesian shared parameter joint models for heterogeneous populations

Joint models (JMs) for longitudinal and time-to-event data are an important class of biostatistical models in health and medical research. When the study population consists of heterogeneous subgroups, the standard JM may be inadequate and lead to misleading results. Joint latent class models (JLCMs) and their variants have been proposed to incorporate latent class structures into JMs. JLCMs are useful for identifying latent subgroup structures, obtaining a more nuanced understanding of the relationships between longitudinal outcomes, and improving prediction performance. We consider the generic form of JLCM, which poses significant computational challenges for both frequentist and Bayesian approaches due to the numerical intractability and multimodality of the associated model's likelihood or posterior. Focusing on the less explored Bayesian paradigm, we propose a new Bayesian inference framework to tackle key limitations in the existing method. Our algorithm leverages state-of-the-art Markov chain Monte Carlo techniques and parallel computing for parameter estimation and model selection. Through a simulation study, we demonstrate the feasibility and superiority of our proposed method over the existing approach. Our simulations also generate important computational insights and practical guidance for implementing such complex models. We illustrate our method using data from the PAQUID prospective cohort study, where we jointly investigate the association between a repeatedly measured cognitive score and the risk of dementia and the latent class structure defined from the longitudinal outcomes.

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arXiv2023

Optimal risk-assessment scheduling for primary prevention of cardiovascular disease

In this work, we introduce a personalised and age-specific Net Benefit function, composed of benefits and costs, to recommend optimal timing of risk assessments for cardiovascular disease prevention. We extend the 2-stage landmarking model to estimate patient-specific CVD risk profiles, adjusting for time-varying covariates. We apply our model to data from the Clinical Practice Research Datalink, comprising primary care electronic health records from the UK. We find that people at lower risk could be recommended an optimal risk-assessment interval of 5 years or more. Time-varying risk-factors are required to discriminate between more frequent schedules for higher-risk people.

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arXiv2019

Sample Size Estimation using a Latent Variable Model for Mixed Outcome Co-Primary, Multiple Primary and Composite Endpoints

Mixed outcome endpoints that combine multiple continuous and discrete components to form co-primary, multiple primary or composite endpoints are often employed as primary outcome measures in clinical trials. There are many advantages to joint modelling the individual outcomes using a latent variable framework, however in order to make use of the model in practice we require techniques for sample size estimation. In this paper we show how the latent variable model can be applied to the three types of joint endpoints and propose appropriate hypotheses, power and sample size estimation methods for each. We illustrate the techniques using a numerical example based on the four dimensional endpoint in the MUSE trial and find that the sample size required for the co-primary endpoint is larger than that required for the individual endpoint with the smallest effect size. Conversely, the sample size required for the multiple primary endpoint is reduced from that required for the individual outcome with the largest effect size. We show that the analytical technique agrees with the empirical power from simulation studies. We further illustrate the reduction in required sample size that may be achieved in trials of mixed outcome composite endpoints through a simulation study and find that the sample size primarily depends on the components driving response and the correlation structure and much less so on the treatment effect structure in the individual endpoints.

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arXiv2019

Mixed effects models for healthcare longitudinal data with an informative visiting process: a Monte Carlo simulation study

Electronic health records are being increasingly used in medical research to answer more relevant and detailed clinical questions; however, they pose new and significant methodological challenges. For instance, observation times are likely correlated with the underlying disease severity: patients with worse conditions utilise health care more and may have worse biomarker values recorded. Traditional methods for analysing longitudinal data assume independence between observation times and disease severity; yet, with healthcare data such assumptions unlikely holds. Through Monte Carlo simulation, we compare different analytical approaches proposed to account for an informative visiting process to assess whether they lead to unbiased results. Furthermore, we formalise a joint model for the observation process and the longitudinal outcome within an extended joint modelling framework. We illustrate our results using data from a pragmatic trial on enhanced care for individuals with chronic kidney disease, and we introduce user-friendly software that can be used to fit the joint model for the observation process and a longitudinal outcome.

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arXiv2019

Employing latent variable models to improve efficiency in composite endpoint analysis

Composite endpoints that combine multiple outcomes on different scales are common in clinical trials, particularly in chronic conditions. In many of these cases, patients will have to cross a predefined responder threshold in each of the outcomes to be classed as a responder overall. One instance of this occurs in systemic lupus erythematosus (SLE), where the responder endpoint combines two continuous, one ordinal and one binary measure. The overall binary responder endpoint is typically analysed using logistic regression, resulting in a substantial loss of information. We propose a latent variable model for the SLE endpoint, which assumes that the discrete outcomes are manifestations of latent continuous measures and can proceed to jointly model the components of the composite. We perform a simulation study and find the method to offer large efficiency gains over the standard analysis. We find that the magnitude of the precision gains are highly dependent on which components are driving response. Bias is introduced when joint normality assumptions are not satisfied, which we correct for using a bootstrap procedure. The method is applied to the Phase IIb MUSE trial in patients with moderate to severe SLE. We show that it estimates the treatment effect 2.5 times more precisely, offering a 60% reduction in required sample size.

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arXiv2019

Estimating the association between blood pressure variability and cardiovascular disease: An application using the ARIC Study

The association between visit-to-visit systolic blood pressure variability and cardiovascular events has recently received a lot of attention in the cardiovascular literature. But blood pressure variability is usually estimated on a person-by-person basis, and is therefore subject to considerable measurement error. We demonstrate that hazard ratios estimated using this approach are subject to bias due to regression dilution and we propose alternative methods to reduce this bias: a two-stage method and a joint model. For the two-stage method, in stage one repeated measurements are modelled using a mixed effects model with a random component on the residual standard deviation. The mixed effects model is used to estimate the blood pressure standard deviation for each individual, which in stage two is used as a covariate in a time-to-event model. For the joint model, the mixed effects sub-model and time-to-event sub-model are fitted simultaneously using shared random effects. We illustrate the methods using data from the Atherosclerosis Risk in Communities (ARIC) study.

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arXiv2007

A dual lagrangian for non-Abelian tensor gauge fields

For non-Abelian tensor gauge fields of the lower rank we have found an alternative expression for the field strength tensors, which transform homogeneously with respect to the complementary gauge transformations and allow us to construct the dual Lagrangian.

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arXiv2005

Using D-Strings to Describe Monopole Scattering - Numerical Calculations

We calculate the energy radiated during the scattering of two D-strings stretched between two D3-branes, working from the Born-Infeld action for the D-strings. The ends of the D-strings are magnetic monopoles from the point of view of the gauge theory living on the D3-branes, and so the scattering we describe is equivalent to monopole scattering. Our results suggest that no energy is radiated during the scattering, in contrast to the monopole result of ref. [2].

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arXiv2005

A New Perspective on the Nonextremal Enhancon Solution

We discuss the nonextremal generalisation of the enhancon mechanism. We find that the nonextremal shell branch solution does not violate the Weak Energy Condition when the nonextremality parameter is small, in contrast to earlier discussions of this subject. We show that this physical shell branch solution fills the mass gap between the extremal enhancon solution and the nonextremal horizon branch solution.

hep-th↗

arXiv2004

Using D-Strings to Describe Monopole Scattering

We describe the scattering of D-strings stretched between D3-branes, working from the D-string perspective. From the D3-brane perspective the ends of the D-strings are magnetic monopoles, and so the scattering we describe is equivalent to monopole scattering. Our aim is to test the prediction made by Manton for the energy radiated during monopole scattering.

hep-th↗

arXiv2003

Wrapped D-Branes as BPS Monopoles: The Moduli Space Perspective

We study the four dimensional effective action of a system of D6-branes wrapped on the K3 manifold times a torus, allowing the volume of the internal manifolds to remain dynamical. An unwrapped brane is at best a Dirac monopole of the dual R-R sector field to which it couples. After wrapping, a brane is expected to behave as a BPS monopole, where the Higgs vacuum expectation value is set by the size of the K3. We determine the moduli space of an arbitrary number of these wrapped branes by introducing a time dependent perturbation of the static solution, and expanding the supergravity equations of motion to determine the dynamics of this perturbation, in the low velocity limit. The result is the hyper-Kahler generalisation of the Euclidean Taub-NUT metric presented by Gibbons and Manton. We note that our results also pertain to the behavior of bound states of Kaluza-Klein monopoles and wrapped NS5-branes in the T^4 compactified heterotic string.

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