Tutorial in Joint Modeling and Prediction: A Statistical Software for Correlated Longitudinal Outcomes, Recurrent Events and a Terminal Event

Król, Agnieszka; Mauguen, Audrey; Mazroui, Yassin; Laurent, Alexandre; Michiels, Stefan; Rondeau, Virginie

doi:10.18637/jss.v081.i03

Cited by 41 publications

(29 citation statements)

References 47 publications

(69 reference statements)

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“…The effects of different treatment strategies in patients treated with individualized therapeutic approaches were evaluated using various extensions of Cox models (variance corrected or frailty models) or joint models to account for time varying treatments, repeated data, subject heterogeneity, event dependence, and informative censoring. A detailed explanation of all models used is given elsewhere (11–18). We encourage readers to review a brief summary of the method used, which is available in the Supplementary Material, to better appreciate the methodical background for interpretation.…”

Section: Methodsmentioning

confidence: 99%

Individualization of Treatment Improves the Survival of Children With High-Risk Solid Tumors: Comparative Patient Series Analysis in a Real-Life Scenario

Kýr¹,

Polaskova²,

Kuttnerova³

et al. 2019

Front. Oncol.

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Introduction: The individualization of treatment is attractive, especially in children with high-risk cancer. In such a rare and very heterogeneous group of diseases, large population-based clinical randomized trials are not feasible without international collaboration. We therefore propose comparative patient series analysis in a real-life scenario. Methods: Open cohort observational study, comparative analysis. Seventy patients with high-risk solid tumors diagnosed between 2003 and 2015 and in whom the treatment was individualized either empirically or based on biomarkers were analyzed. The heterogeneity of the cohort and repeated measurements were advantageously utilized to increase effective sample size using appropriate statistical tools. Results: We demonstrated a beneficial effect of empirically given low-dose metronomic chemotherapy (HR 0.46 for relapses, p = 0.017) as well as various repurposed or targeted agents (HR 0.15 for deaths, p = 0.004) in a real-life scenario. However, targeted agents given on the basis of limited biological information were not beneficial. Conclusions: Comparative patient series analysis provides institutional-level evidence for treatment individualization in high-risk pediatric malignancies. Our findings emphasize the need for a comprehensive, multi omics assessment of the tumor and the host as well whenever molecularly driven targeted therapies are being considered. Low-dose metronomic chemotherapy or local control of the disease may be a more rational option in situations where targeted treatment cannot be justified by robust evidence and comprehensive biological information. “Targeted drugs” may be given empirically with a realistic benefit expectation when based on robust rationale.

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Section: Methodsmentioning

confidence: 99%

Individualization of Treatment Improves the Survival of Children With High-Risk Solid Tumors: Comparative Patient Series Analysis in a Real-Life Scenario

Kýr¹,

Polaskova²,

Kuttnerova³

et al. 2019

Front. Oncol.

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“…The Brier scores were calculated on patients alive at time point t = 40 months. We predicted those patients' survival probability at t + w = 50, 60, 70, 80, 90 and plotted the Brier scores at each time point 23 in Figure 6. The results showed that our method has the lowest error between the predicted and actual survival conditions compared with TJM and RJFM.…”

Section: Applicationmentioning

confidence: 99%

Dynamic prediction of disease processes based on recurrent history and functional principal component analysis of longitudinal biomarkers: Application for ovarian epithelial cancer

Hong

Song

et al. 2021

Statistics in Medicine

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Ovarian epithelial cancer is a gynecological tumor with a high risk of recurrence and death. In the clinical diagnosis of ovarian epithelial cancer, CA125 has become an important indicator of disease burden. To account for patient recurrence and death, a proper method is needed to integrate information from biomarkers and recurrence simultaneously. In the past 10 years, many methods have been proposed for joint modeling of longitudinal biomarkers and survival data, but few of them are applicable to longitudinal data and disease processes, including recurrence and death. In this article, we proposed a new joint frailty model based on functional principal component analysis for dynamic prediction of survival probabilities on the total time scale, which took recurrent history and longitudinal data into account simultaneously. The estimation of the joint frailty model is achieved by maximizing the penalized log‐likelihood function. The simulation results demonstrated the advantages of our method in both discrimination and accuracy under different scenarios. To indicate the method's practicality, it is applied to an actual dataset of patients with ovarian epithelial cancer to predict survival dynamically using longitudinal data of biomarker CA125 and recurrent history data.

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“…Joint models is an active area of research in statistics with numerous extensions of the basic model (analyzed in this paper) suggested in the literature that cover a wide range of research applications such as latent classes, competing risks, multivariate models, nonlinear models, dynamic predictions, stochastic processes, etc. (see books [15,16] and recent review papers and tutorials [25][26][27][28][29][30][31][32][33]). Such extended models can be applied to analyze dynamic characteristics of composite measures such as DM with various outcomes in more comprehensive ways.…”

Section: Applications Of Joint Models To Composite Measures Of Physiomentioning

confidence: 99%

Genetics of physiological dysregulation: findings from the long life family study using joint models

Arbeev

Bagley

Ukraintseva

et al. 2020

Aging

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Aging is a complex process that involves multiple systems, leading to physiological dysregulation, health deterioration, and eventually death. Changes that occur at the molecular and cellular levels as individuals grow older propagate to changes detectable in laboratory tests of blood or other tissues and observable in measurements of various physiological variables in an individual at different ages. Cross-sectional

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Tutorial in Joint Modeling and Prediction: A Statistical Software for Correlated Longitudinal Outcomes, Recurrent Events and a Terminal Event

Cited by 41 publications

References 47 publications

Individualization of Treatment Improves the Survival of Children With High-Risk Solid Tumors: Comparative Patient Series Analysis in a Real-Life Scenario

Individualization of Treatment Improves the Survival of Children With High-Risk Solid Tumors: Comparative Patient Series Analysis in a Real-Life Scenario

Dynamic prediction of disease processes based on recurrent history and functional principal component analysis of longitudinal biomarkers: Application for ovarian epithelial cancer

Genetics of physiological dysregulation: findings from the long life family study using joint models

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