Dynamic optimal strategy for monitoring disease recurrence

Li, Hong; Gatsonis, Constantine

doi:10.1007/s11425-012-4475-y

Cited by 7 publications

(7 citation statements)

References 58 publications

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“…Nowadays, epidemic models have attracted much attention [14], especially, avian influenza model. The study of our work tries to describe the spreading process of the disease, and encourages people to take good strategies to prevent the avian influenza virus from transmitting to human world.…”

Section: Discussionmentioning

confidence: 99%

The spreading frontiers of avian-human influenza described by the free boundary

2013

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In this paper, a reaction-diffusion system is proposed to investigate avian-human influenza. Two free boundaries are introduced to describe the spreading frontiers of the avian influenza. The basic reproduction numbers r F 0 (t) and R F 0 (t) are defined for the bird with the avian influenza and for the human with the mutant avian influenza of the free boundary problem, respectively. Properties of these two time-dependent basic reproduction numbers are obtained. Sufficient conditions both for spreading and for vanishing of the avian influenza are given. It is shown that if r F 0 (0) < 1 and the initial number of the infected birds is small, the avian influenza vanishes in the bird world. Furthermore, if r F 0 (0) < 1 and R F 0 (0) < 1, the avian influenza vanishes in the bird and human worlds. In the case that r F 0 (0) < 1 and R F 0 (0) > 1, spreading of the mutant avian influenza in the human world is possible. It is also shown that if r F 0 (t 0 ) 1 for any t 0 0, the avian influenza spreads in the bird world.

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Section: Discussionmentioning

confidence: 99%

The spreading frontiers of avian-human influenza described by the free boundary

2013

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“…By recommending less frequent testing for patients who are at low risk of progression or unlikely to benefit from treatment, the framework can reduce patients' financial burden and optimize health care resource utilization. 51 Previous approaches for dynamically updating surveillance schedules have either optimized timing at the population level 16 ; individualized timing by considering only predicted risk of progression, ignoring health and cost outcomes 18,19 ; or not incorporated patient history. [22][23][24][25][26][27] Our approach dynamically individualizes timing by embedding predictions within a comprehensive decisionmaking framework to optimize outcomes.…”

Section: Discussionmentioning

confidence: 99%

“…Previous approaches for dynamically updating surveillance schedules have either optimized timing at the population level 16 ; individualized timing by considering only predicted risk of progression, ignoring health and cost outcomes 18,19 ; or not incorporated patient history. 22–27 Our approach dynamically individualizes timing by embedding predictions within a comprehensive decision-making framework to optimize outcomes.…”

Section: Discussionmentioning

confidence: 99%

“…16,17 However, these are population-level models, lacking sufficient detail to inform individual decision making. 16 Others have proposed individualizing testing schedules through dynamic predictions 18,19 ; however, these methods consider only the risk of progression, ignoring health and cost outcomes. Working within the context where the timing of surveillance visits is a decision problem, we allow individualized timing of surveillance to be informed by biomarker-driven prediction models for the underlying etiology of disease.…”

Section: Introductionmentioning

confidence: 99%

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A Value-of-Information Framework for Personalizing the Timing of Surveillance Testing

et al. 2021

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Background Patient surveillance using repeated biomarker measurements presents an opportunity to detect and treat disease progression early. Frequent surveillance testing using biomarkers is recommended and routinely conducted in several diseases, including cancer and diabetes. However, frequent testing involves tradeoffs. Although surveillance tests provide information about current disease status, the complications and costs of frequent tests may not be justified for patients who are at low risk of progression. Predictions based on patients’ earlier biomarker values may be used to inform decision making; however, predictions are uncertain, leading to decision uncertainty. Methods We propose the Personalized Risk-Adaptive Surveillance (PRAISE) framework, a novel method for embedding predictions into a value-of-information (VOI) framework to account for the cost of uncertainty over time and determine the time point at which collection of biomarker data would be most valuable. The proposed sequential decision-making framework is innovative in that it leverages the patient’s longitudinal history, considers individual benefits and harms, and allows for dynamic tailoring of surveillance intervals by considering the uncertainty in current information and estimating the probability that new information may change treatment decisions, as well as the impact of this change on patient outcomes. Results When applied to data from cystic fibrosis patients, PRAISE lowers costs by allowing some patients to skip a visit, compared to an “always test” strategy. It does so without compromising expected survival, by recommending less frequent testing among those who are unlikely to be treated at the skipped time point. Conclusions A VOI-based approach to patient monitoring is feasible and could be applied to several diseases to develop more cost-effective and personalized strategies for ongoing patient care. Highlights In many patient-monitoring settings, the complications and costs of frequent tests are not justified for patients who are at low risk of disease progression. Predictions based on patient history may be used to individualize the timing of patient visits based on evolving risk. We propose Personalized Risk-Adaptive Surveillance (PRAISE), a novel method for personalizing the timing of surveillance testing, where prediction modeling projects the disease trajectory and a value-of-information (VOI)–based pragmatic decision-theoretic framework quantifies patient- and time-specific benefit-harm tradeoffs. A VOI-based approach to patient monitoring could be applied to several diseases to develop more personalized and cost-effective strategies for ongoing patient care.

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“…Such individualized predictive tools based on JM are valuable in clinical settings for patient monitoring and decision making because such predictions can be dynamically updated according to the observations of biomarkers ( 83 , 85 , 86 ). Availability of free software ( 18 , 44 ) and a web-based calculator ( 86 ) implementing such algorithms should facilitate the growing use of this methodology in practical applications.…”

Section: Approaches To Joint Analyses Of Longitudinal and Time-to-evementioning

confidence: 99%

Joint Analyses of Longitudinal and Time-to-Event Data in Research on Aging: Implications for Predicting Health and Survival

Arbeev

Akushevich

Kulminski

et al. 2014

Front. Public Health

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Longitudinal data on aging, health, and longevity provide a wealth of information to investigate different aspects of the processes of aging and development of diseases leading to death. Statistical methods aimed at analyses of time-to-event data jointly with longitudinal measurements became known as the “joint models” (JM). An important point to consider in analyses of such data in the context of studies on aging, health, and longevity is how to incorporate knowledge and theories about mechanisms and regularities of aging-related changes that accumulate in the research field into respective analytic approaches. In the absence of specific observations of longitudinal dynamics of relevant biomarkers manifesting such mechanisms and regularities, traditional approaches have a rather limited utility to estimate respective parameters that can be meaningfully interpreted from the biological point of view. A conceptual analytic framework for these purposes, the stochastic process model of aging (SPM), has been recently developed in the biodemographic literature. It incorporates available knowledge about mechanisms of aging-related changes, which may be hidden in the individual longitudinal trajectories of physiological variables and this allows for analyzing their indirect impact on risks of diseases and death. Despite, essentially, serving similar purposes, JM and SPM developed in parallel in different disciplines with very limited cross-referencing. Although there were several publications separately reviewing these two approaches, there were no publications presenting both these approaches in some detail. Here, we overview both approaches jointly and provide some new modifications of SPM. We discuss the use of stochastic processes to capture biological variation and heterogeneity in longitudinal patterns and important and promising (but still largely underused) applications of JM and SPM to predictions of individual and population mortality and health-related outcomes.

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Dynamic optimal strategy for monitoring disease recurrence

Cited by 7 publications

References 58 publications

The spreading frontiers of avian-human influenza described by the free boundary

The spreading frontiers of avian-human influenza described by the free boundary

A Value-of-Information Framework for Personalizing the Timing of Surveillance Testing

Joint Analyses of Longitudinal and Time-to-Event Data in Research on Aging: Implications for Predicting Health and Survival

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