Outcome Prediction for Heart Failure Telemonitoring Via Generalized Linear Models with Functional Covariates

Baraldo, Stefano; Ieva, Francesca; Paganoni, Anna Maria; Vitelli, Valeria

doi:10.1111/j.1467-9469.2012.00818.x

Cited by 4 publications

(10 citation statements)

References 14 publications

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“…However, it might be more efficient if we consider the intensity or rate function of recurrent events and conduct dynamic prediction in a more systematic way. Baraldo et al (2013) used FPCA to decompose a smoothed intensity function of recurrent events; then a regression model with time-dependent covariate and time varying coefficient reduces to a classical regression model. Certainly, there are many other aspects in which we can make improvements or provide alternative methods, such as using AFT models with time-dependent covariates for dynamic prediction, incorporating the potential of cure after each treatment for disease recurrence and predicting the outcomes under different treatment options for aiding decision making.…”

Section: Discussionmentioning

confidence: 99%

Functional Principal Components Analysis on Moving Time Windows of Longitudinal Data: Dynamic Prediction of Times to Event

Yan

Lin

et al. 2018

Journal of the Royal Statistical Society Series C: Applied Statistics

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Summary Functional principal component analysis (FPCA) is a powerful approach for modelling noisy and irregularly measured longitudinal data. Similarly to principal component analysis that extracts features from multivariate random vectors, FPCA can extract features from longitudinal biomarker data. We propose to use these features to update predictions for patients’ prognoses frequently. Traditional FPCA applies only to data observed in a common time window. In the setting of time‐to‐event analysis, the patterns of the biomarker trajectories may change over time, which poses a challenge for the application of FPCA to dynamic prediction. We propose to use a series of moving time windows to apply FPCA techniques, and we impose smoothness constraints between parameters for these moving windows. Simulation studies show that the approach proposed can provide more robust performance than predictions based on parametric models for longitudinal biomarker data, by prediction judged by performance measures such as the root‐mean‐square errors and area under the curve of receiver operating characteristics. We apply the method to a longitudinal study for chronic myeloid leukaemia patients, predicting their time to disease progression by using the transcript levels of an oncogene, BCR‐ABL, which is repeatedly measured during their follow‐up visits.

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Section: Discussionmentioning

confidence: 99%

Functional Principal Components Analysis on Moving Time Windows of Longitudinal Data: Dynamic Prediction of Times to Event

Yan

Lin

et al. 2018

Journal of the Royal Statistical Society Series C: Applied Statistics

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“…Chronic patients are usually involved in long‐term therapies, that are often characterized by repeated situations like office visits, subsequent drug consumption, hospital admissions, and many others. Examples include recurrences in breast cancer (Rondeau, 2010), asthma attacks (Duchateau et al., 2003), episodic relapses of follicular lymphoma (Rondeau, 2010), readmission after colorectal cancer (Charles‐Nelson et al., 2019; González et al., 2005), epileptic seizures (WHO et al., 2005) or re‐hospitalizations in Heart Failure (HF) (Baraldo et al., 2013; Kennedy, 2001; Rogers et al., 2016) which will be the context considered for the present work. Heart Failure (HF) is a major and growing public health issue, characterized by high costs, steep morbidity, and mortality rates (Lloyd‐Jones et al., 2010).…”

Section: Introductionmentioning

confidence: 99%

“…In this framework, Baraldo et al. (2013) proposed a method to model the realized trajectories of the cumulative hazard functions underlying a recurrent event process of interest (i.e., hospital readmissions in time). Estimated trajectories were treated as functional data and included into a generalized linear model to predict a binary telemonitoring outcome.…”

Section: Introductionmentioning

confidence: 99%

“…Starting from the idea by Baraldo et al. (2013), we developed a marked point process formulation for recurrent events to compute the realized trajectories of the cumulative hazard functions (i.e., the compensators ) underlying specific counting processes of interest, allowing the dependence on the marks. In particular, among the aforementioned methods to deal with recurrent events, we modeled the compensators through AG models by Andersen and Gill (1982), ending up with functional data that represent the dynamic evolution of the events risk.…”

Section: Introductionmentioning

confidence: 99%

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Functional modeling of recurrent events on time‐to‐event processes

Spreafico

Ieva

2021

Biometrical J

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In clinical practice, it is often the case where the association between the occurrence of events and time‐to‐event outcomes is of interest; thus, it can be modeled within the framework of recurrent events. The purpose of our study is to enrich the information available for modeling survival with relevant dynamic features, properly taking into account their possibly time‐varying nature, as well as to provide a new setting for quantifying the association between time‐varying processes and time‐to‐event outcomes. We propose an innovative methodology to model information carried out by time‐varying processes by means of functional data, modeling each time‐varying variable as the compensator of marked point process the recurrent events are supposed to derive from. By means of Functional Principal Component Analysis, a suitable dimensional reduction of these objects is carried out in order to plug them into a Cox‐type functional regression model for overall survival. We applied our methodology to data retrieved from the administrative databases of Lombardy Region (Italy), related to patients hospitalized for Heart Failure (HF) between 2000 and 2012. We focused on time‐varying processes of HF hospitalizations and multiple drugs consumption and we studied how they influence patients' overall survival. This novel way to account for time‐varying variables allowed to model self‐exciting behaviors, for which the occurrence of events in the past increases the probability of a new event, and to quantify the effect of personal behaviors and therapeutic patterns on survival, giving new insights into the direction of personalized treatment.

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“…Recent developments on estimation of derivatives are contained in Sangalli et al (2009) and in Pigoli and Sangalli (2012). See also Baraldo et al (2013), who have obtained derivatives in the context of survival analysis, and Hall et al (2009) who have estimated derivatives in a non-parametric model.…”

Section: Introductionmentioning

confidence: 99%

Best estimation of functional linear models

Aletti

May

Tommasi

2016

Journal of Multivariate Analysis

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Observations which are realizations from some continuous process are frequent in sciences, engineering, economics, and other fields. We consider linear models, with possible random effects, where the responses are random functions in a suitable Sobolev space. The processes cannot be observed directly. With smoothing procedures from the original data, both the response curves and their derivatives can be reconstructed, even separately. From both these samples of functions, just one sample of representatives is obtained to estimate the vector of functional parameters. A simulation study shows the benefits of this approach over the common method of using information either on curves or derivatives. The main theoretical result is a strong functional version of the Gauss-Markov theorem. This ensures that the proposed functional estimator is more efficient than the best linear unbiased estimator based only on curves or derivatives.Comment: the best information from the two samples of functions and derivatives: a strong version of the Gauss-Markov theorem. Relaxed an hidden hypothesis on linear independence of the Riesz representation of the Karhunen-Loeve bas

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Outcome Prediction for Heart Failure Telemonitoring Via Generalized Linear Models with Functional Covariates

Cited by 4 publications

References 14 publications

Functional Principal Components Analysis on Moving Time Windows of Longitudinal Data: Dynamic Prediction of Times to Event

Functional Principal Components Analysis on Moving Time Windows of Longitudinal Data: Dynamic Prediction of Times to Event

Functional modeling of recurrent events on time‐to‐event processes

Best estimation of functional linear models

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