Joint modelling of repeated measurement and time-to-event data: an introductory tutorial

Asar, Özgür; Ritchie, James; Kalra, Philip A.; Diggle, Peter J.

doi:10.1093/ije/dyu262

Cited by 145 publications

(117 citation statements)

References 34 publications

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“…Longitudinal biomarkers usually contain potential random variation both due to the laboratory measurement process as well as short and long term biological fluctuations, and are only observed until an event occurs. Whereas not accounting for the random fluctuations in a time-varying Cox model might result in an underestimation of the hazard ratio [12], ignoring the latter might distort the estimation of covariate effects in the longitudinal model. By jointly analyzing the longitudinal and survival model we could address these issues and gained further insights as to how covariates affected both the autoantibody titers over time, and the risk of T1D progression.…”

Section: Discussionmentioning

confidence: 99%

“…In contrast to previous analyses, we used joint models of longitudinal and survival data. This class of models has the advantage to avoid potential bias due to characteristics of the longitudinal markers (here autoantibodies), such as random biological fluctuations, informative censoring and discrete measurement time points [12]. By applying a novel approach of joint modeling, we gained further insights into the potentially complex relationship between longitudinal islet autoantibody measures and the time to T1D progression, particularly with respect to time-varying associations of both.…”

Section: Introductionmentioning

confidence: 99%

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Joint modeling of longitudinal autoantibody patterns and progression to type 1 diabetes: results from the TEDDY study

et al. 2017

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Aims The onset of clinical type 1 diabetes (T1D) is preceded by the occurrence of disease-specific autoantibodies. The level of autoantibody titers is known to be associated with progression time from the first emergence of autoantibodies to the onset of clinical symptoms, but detailed analyses of this complex relationship are lacking. We aimed to fill this gap by applying advanced statistical models. Methods We investigated data of 613 children from the prospective TEDDY study who were persistent positive for IAA, GADA and/or IA2A autoantibodies. We used a novel approach of Bayesian joint modeling of longitudinal and survival data to assess the potentially time- and covariate dependent association between the longitudinal autoantibody titers and progression time to T1D. Results For all autoantibodies we observed a positive association between the titers and the T1D progression risk. This association was estimated as time constant for IA2A, but decreased over time for IAA and GADA. For example the hazard ratio [95% credibility interval] for IAA (per transformed unit) was 3.38 [2.66, 4.38] at 6 months after seroconversion, and 2.02 [1.55, 2.68] at 36 months after seroconversion. Conclusions These findings indicate that T1D progression risk stratification based on autoantibody titers should focus on time points early after seroconversion. Joint modeling techniques allow for new insights into these associations.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Joint modeling of longitudinal autoantibody patterns and progression to type 1 diabetes: results from the TEDDY study

et al. 2017

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“…These studies show improved predictive accuracy for both survival and FEV 1 , particularly when there is a high probability of censoring. Although the models require more careful interpretation and sophisticated software, introductory examples are provided in the chronic disease literature (36). …”

Section: Fev1 In Cf Epidemiologic Studies: Analytic Approaches Formentioning

confidence: 99%

Use of FEV1 in cystic fibrosis epidemiologic studies and clinical trials: A statistical perspective for the clinical researcher

Szczesniak

Heltshe

Stanojevic

et al. 2017

Journal of Cystic Fibrosis

106

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Background Forced expiratory volume in 1 second (FEV1) is an established marker of cystic fibrosis (CF) disease progression that is used to capture clinical course and evaluate therapeutic efficacy. The research community has established FEV1 surveillance data through a variety of observational data sources such as patient registries, and there is a growing pipeline of new CF therapies demonstrated to be efficacious in clinical trials by establishing improvements in FEV1. Results In this review, we summarize from a statistical perspective the clinical relevance of FEV1 based on its association with morbidity and mortality in CF, its role in epidemiologic studies of disease progression and comparative effectiveness, and its utility in clinical trials. In addition, we identify opportunities to advance epidemiologic research and the clinical development pipeline through further statistical considerations. Conclusions Our understanding of CF disease course, therapeutics, and clinical care has evolved immensely in the past decades, in large part due to the thoughtful application of rigorous research methods and meaningful clinical endpoints such as FEV1. A continued commitment to conduct research that minimizes the potential for bias, maximizes the limited patient population, and harmonizes approaches to FEV1 analysis while maintaining clinical relevance, will facilitate further opportunities to advance CF care.

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“…Extensions of these include, among others, models for multiple longitudinal outcomes (Hatfield, Boye, Hackshaw, and Carlin 2012), multiple failure times (Elashoff, Li, and Li 2008) and both (Chi and Ibrahim 2006). A review of the joint modeling of longitudinal and survival data was already given elsewhere (McCrink, Marshall, and Cairns 2013;Lawrence Gould et al 2015;Asar, Ritchie, Kalra, and Diggle 2015).…”

Section: Joint Modelsmentioning

confidence: 99%

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

Król¹,

Mauguen²,

Mazroui³

et al. 2017

J. Stat. Soft.

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Joint modelling of repeated measurement and time-to-event data: an introductory tutorial

Abstract: Joint models should be preferred for simultaneous analyses of repeated measurement and survival data, especially when the former is measured with error and the association between the underlying error-free measurement process and the hazard for survival is of scientific interest.

Cited by 145 publications

References 34 publications

Joint modeling of longitudinal autoantibody patterns and progression to type 1 diabetes: results from the TEDDY study

Joint modeling of longitudinal autoantibody patterns and progression to type 1 diabetes: results from the TEDDY study

Use of FEV1 in cystic fibrosis epidemiologic studies and clinical trials: A statistical perspective for the clinical researcher

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

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