Using predictions from a joint model for longitudinal and survival data to inform the optimal time of intervention in an abdominal aortic aneurysm screening programme

Sweeting, Michael

doi:10.1002/bimj.201600222

Cited by 4 publications

(2 citation statements)

References 22 publications

(33 reference statements)

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“…lcmm R package for JLCMs, frailtypack R package for JFMs. Time-varying effects [ 13 ], Bayesian moving average [ 53 , 107 ], various functions of random effects [ 13 , 53 , 73 – 75 , 113 ], third JM to handle missing data and cure fraction models [ 66 , 114 – 116 ]. Shared random effects JM employed for real-time predictions of prostate cancer recurrence [ 74 ].…”

Section: Resultsmentioning

confidence: 99%

Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods

et al. 2020

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Background: Clinical prediction models (CPMs) predict the risk of health outcomes for individual patients. The majority of existing CPMs only harness cross-sectional patient information. Incorporating repeated measurements, such as those stored in electronic health records, into CPMs may provide an opportunity to enhance their performance. However, the number and complexity of methodological approaches available could make it difficult for researchers to explore this opportunity. Our objective was to review the literature and summarise existing approaches for harnessing repeated measurements of predictor variables in CPMs, primarily to make this field more accessible for applied researchers. Methods: MEDLINE, Embase and Web of Science were searched for articles reporting the development of a multivariable CPM for individual-level prediction of future binary or time-to-event outcomes and modelling repeated measurements of at least one predictor. Information was extracted on the following: the methodology used, its specific aim, reported advantages and limitations, and software available to apply the method. Results: The search revealed 217 relevant articles. Seven methodological frameworks were identified: time-dependent covariate modelling, generalised estimating equations, landmark analysis, two-stage modelling, joint-modelling, trajectory classification and machine learning. Each of these frameworks satisfies at least one of three aims: to better represent the predictor-outcome relationship over time, to infer a covariate value at a pre-specified time and to account for the effect of covariate change. Conclusions: The applicability of identified methods depends on the motivation for including longitudinal information and the method's compatibility with the clinical context and available patient data, for both model development and risk estimation in practice.

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Section: Resultsmentioning

confidence: 99%

Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods

et al. 2020

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“…Other approaches to individualised dynamic prediction include stacking and pseudo-BMA [23]; and landmarking [24]. A number of other papers have also compared prediction models for dynamic prediction [25]; [26]; [27].…”

Section: Discussionmentioning

confidence: 99%

Dynamic Predictions From Longitudinal CD4 Count Measures And Time To Death of HIV/AIDS Patients Using a Bayesian Joint Model

Muhammed

Sebu

Presanis

et al. 2021

Preprint

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Background: Personalised or stratified medicine has played an increasingly important role in improving bio-medical care in recent years. A Bayesian joint modelling approach to dynamic prediction of HIV progression and mortality allows such individualised predictions to be made for HIV patients, based on monitoring of their CD4 counts. This study aims to provide predictions of patient-specific trajectories of HIV disease progression and survival.Methods: Longitudinal data on 254 HIV/AIDS patients who received ART between 2009 and 2014, and who had at least one CD4 count observed, were employed in a Bayesian joint model of disease progression, as measured by CD4 counts, and survival, to obtain individualised dynamic predictions of both processes that were updated at each visit time in the follow-up period. Different forms of association structure that relate the longitudinal CD4 biomarker and time to death were assessed; and predictions were averaged over the different models using Bayesian model averaging.Results: A total of 254 subjects were observed in the dataset with a median age of 30 years (interquartile range, IQR, 26–38). The individual follow-up times ranged from 1 to 120 months, with a median of 22 months and IQR 7 -39 months. The median baseline CD4 count was 129 cells/mm3 (IQR 61–247 cells/mm3). From the joint model with highest posterior weight, subjects whose functional status was working were significantly associated with a higher baseline CD4 count (β = 1.86; 95% CI: 0.65 3.04) whereas subjects who were bedridden were significantly associated with a lower baseline CD4 count (estimated effect β = -3.54; 95% CI: -5.65, -1.39), compared to ambulatory patients. A unit increase in weight of the individual increased the mean square root CD4 measurement by 0.06. The estimates of the association structure parameters from all three models considered indicated that the HIV mortality hazard at any time point is associated with the current underlying value of the CD4 count at the same time point. The model with highest posterior weight also had a time-dependent slope, indicating that HIV mortality is also associated with the rate of change in CD4 count. From both the model-averaged predictions and the highest posterior weight model alone, an increase in CD4 count was predicted at different visit times from the dynamic predictions. It was also found that there was an increase in the width of prediction intervals as time progressed.Conclusions: Functional status, weight and alcohol intake are important contributing factors that affect the mean square root of CD4 measurements. For this particular dataset, model averaging the dynamic predictions resulted in only one of the hypothesised association structures having non-zero weight at the majority of time points for each individual. The predictions were therefore similar whether we averaged them over models or derived them from the highest posterior weight model alone. We also observed that the parameter estimates in the both the CD4 count and survival sub-models showed slight variability between the postulated association structures.

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Dynamic prediction of transition to psychosis using joint modelling

Yuen

Mackinnon

Hartmann

et al. 2018

Schizophrenia Research

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Using predictions from a joint model for longitudinal and survival data to inform the optimal time of intervention in an abdominal aortic aneurysm screening programme

Cited by 4 publications

References 22 publications

Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods

Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods

Dynamic Predictions From Longitudinal CD4 Count Measures And Time To Death of HIV/AIDS Patients Using a Bayesian Joint Model

Dynamic prediction of transition to psychosis using joint modelling

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