Comments on: Inference and computation with Generalized Additive Models and their extensions

Greven, Sonja; Scheipl, Fabian

doi:10.1007/s11749-020-00714-2

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…Especially for small and medium sized data sets, the computation time is barely noticeable (even when using all unique event times as cut-points). For high-dimensional data efficient estimation methods exist [53,54]. For models on the gap timescale, computation time could be further improved without sacrificing predictive performance by reducing the number of interval cut points (cf.…”

Section: Discussionmentioning

confidence: 99%

“…For example, on a standard laptop with Intel core-i7 CPU with 1.90GHz, 16GB RAM, fitting the interaction model in Section 5.1 (Equation (5.1)) takes about 1 second; the model estimating incidences of Malaria infection (with bivariate effects, Equation (5.3)), in Section 5.2, takes about 10 seconds. For high-dimensional data efficient estimation methods exist (Wood et al, 2017;Greven and Scheipl, 2020). For models on the gap timescale, computation time could be further improved without sacrificing predictive performance by reducing the number of interval cut points (cf.…”

Section: Discussionmentioning

confidence: 99%

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Recurrent events analysis with piece-wise exponential additive mixed models

Ramjith¹,

Bender

Roes³

et al. 2022

Statistical Modelling

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Recurrent events analysis plays an important role in many applications, including the study of chronic diseases or recurrence of infections. Historically, many models for recurrent events have been variants of the Cox model. In this article we introduce and describe the application of the piece-wise exponential Additive Mixed Model (PAMM) for recurrent events analysis and illustrate how PAMMs can be used to flexibly model the dependencies in recurrent events data. Simulations confirm that PAMMs provide unbiased estimates as well as equivalence to the Cox model when proportional hazards are assumed. Applications to recurrence of staphylococcus aureus and malaria in children illustrate the estimation of seasonality, bivariate non-linear effects, multiple timescales and relaxation of the proportional hazards assumption via time-varying effects. The R package pammtools is extended to facilitate estimation and visualization of PAMMs for recurrent events data.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Recurrent events analysis with piece-wise exponential additive mixed models

Ramjith¹,

Bender

Roes³

et al. 2022

Statistical Modelling

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Functional Regression Models in Biomechanics

Gunning,

Warmenhoven,

Harrison

et al. 2024

SpringerBriefs in Statistics

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Recurrent Events Analysis with Piece-wise exponential Additive Mixed Models

Ramjith

Bender

Roes

et al. 2021

Preprint

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Background: Recurrent events analysis plays an important role in many applications, including the study of chronic diseases or recurrence of infections. Historically, most models for the analysis of time-to-event data, including recurrent events, have been based on Cox proportional hazards regression. Recently, however, the Piece-wise exponential Additive Mixed Model (PAMM) has gained popularity as a flexible framework for survival analysis. While many papers and tutorials have been presented in the literature on the application of Cox based models, few papers have provided detailed instructions for the application of PAMMs and to our knowledge, none exist for recurrent events analysis. Methods: The PAMM is introduced as a framework for recurrent events analysis. We describe the application of the model to unstratified and stratified shared frailty models for recurrent events. We illustrate how penalized splines can be used to estimate non-linear and time-varying covariate effects without a priori assumptions about their functional shape. The model is motivated for both, analysis on the gap timescale ("clock-reset") and calendar timescale ("clock-forward"). The data augmentation necessary for the application to recurrent events is described and explained in detail. Results: Simulations confirmed that the model provides unbiased estimates of covariate effects and the frailty variance, as well as equivalence to the Cox model when proportional hazards are assumed. Applications to recurrence of staphylococcus aureus and malaria in children illustrates the estimation of seasonality, bivariate non-linear effects, multiple timescales and relaxation of the proportional hazards assumption via time-varying effects. The R package pammtools has been extended to facilitate estimation, visualization and interpretation of PAMMs for recurrent events analysis. Conclusion: PAMMs provide a flexible framework for the analysis of time-to-event and recurrent events data. The estimation of PAMMs is based on Generalized Additive Mixed Models and thus extends the researcher’s toolbox for recurrent events analysis.

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Comments on: Inference and computation with Generalized Additive Models and their extensions

Cited by 3 publications

References 28 publications

Recurrent events analysis with piece-wise exponential additive mixed models

Recurrent events analysis with piece-wise exponential additive mixed models

Functional Regression Models in Biomechanics

Recurrent Events Analysis with Piece-wise exponential Additive Mixed Models

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