How to analyze and interpret recurrent events data in the presence of a terminal event: An application on readmission after colorectal cancer surgery

Charles-Nelson, Anaïs; Katsahian, Sandrine; Schramm, Catherine

doi:10.1002/sim.8168

Cited by 16 publications

(8 citation statements)

References 49 publications

(57 reference statements)

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“…The WLW model performed mediocrely, regardless of if it was penalized or not; this was consistent with previous findings in the literature, exposing WLW models to be more appropriate with events of different types rather than recurrent events [Ozga et al, 2018, Ullah et al, 2014. Nevertheless, these models, each with their own specificities, can respond to differing needs, especially related to the research questions, but only methodological issues were meant to be illustrated in this study [Rogers et al, 2014, Amorim and Cai, 2015, Charles-Nelson et al, 2019.…”

Section: Discussionsupporting

confidence: 87%

Towards Filling the Gaps around Recurrent Events in High-Dimensional Framework: Literature Review and Early Comparison

Murris¹,

Charles-Nelson²,

Lavenu³

et al. 2022

Preprint

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Background Study individuals may face repeated events overtime. However, there is no consensus around learning approaches to use in a high-dimensional framework for survival data (when the number of variables exceeds the number of individuals, i.e., p > n). This study aimed at identifying learning algorithms for analyzing/predicting recurrent events and at comparing them to standard statistical models in various data simulation settings. Methods A literature review (LR) was conducted to provide state-of-the-art methodology. Data were then simulated including variations of the number of variables, the proportion of active variables, and the number of events. Learning algorithms from the LR were compared to standard methods in such simulation scheme. Evaluation measures were Harrell's concordance index, Kim's C-index and error rate for active variables. Results Seven publications were identified, consisting in four methodological studies, one application paper and two reviews. The broken adaptive ridge penalization and the RankDeepSurv deep neural network were used for comparison. On simulated data, the standard models failed when p > n. Penalized Andersen-Gill and frailty models outperformed, whereas RandkDeepSurv reported lower performances. Conclusion As no guidelines support a specific approach, this study helps to better understand mechanisms and limits of investigated methods in such context.

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Section: Discussionsupporting

confidence: 87%

Towards Filling the Gaps around Recurrent Events in High-Dimensional Framework: Literature Review and Early Comparison

Murris¹,

Charles-Nelson²,

Lavenu³

et al. 2022

Preprint

Self Cite

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“…AEs may also be recurrent, 27 but the issue of CEs will remain as relevant as in time‐to‐first‐event analyses. Recent papers by Charles‐Nelson et al 30 and by Andersen et al 31 emphasize the importance of considering competing (terminal) events also in analyses of recurrent events. See these references 27,32,33 for suggestions on the analysis of recurrent AEs.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Estimating and comparing adverse event probabilities in the presence of varying follow‐up times and competing events

Stegherr¹,

Schmoor

Lübbert

et al. 2021

Pharmaceutical Statistics

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Safety analyses of adverse events (AEs) are important in assessing benefit–risk of therapies but are often rather simplistic compared to efficacy analyses. AE probabilities are typically estimated by incidence proportions, sometimes incidence densities or Kaplan–Meier estimation are proposed. These analyses either do not account for censoring, rely on a too restrictive parametric model, or ignore competing events. With the non‐parametric Aalen‐Johansen estimator as the “gold standard”, that is, reference estimator, potential sources of bias are investigated in an example from oncology and in simulations, for both one‐sample and two‐sample scenarios. The Aalen‐Johansen estimator serves as a reference, because it is the proper non‐parametric generalization of the Kaplan–Meier estimator to multiple outcomes. Because of potential large variances at the end of follow‐up, comparisons also consider further quantiles of the observed times. To date, consequences for safety comparisons have hardly been investigated, the impact of using different estimators for group comparisons being unclear. For example, the ratio of two both underestimating or overestimating estimators may not be comparable to the ratio of the reference, and our investigation also considers the ratio of AE probabilities. We find that ignoring competing events is more of a problem than falsely assuming constant hazards by the use of the incidence density and that the choice of the AE probability estimator is crucial for group comparisons.

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“…AEs may also be recurrent 21 , but the issue of CEs will remain as relevant as in timeto-first-event analyses. Recent papers by Charles-Nelson et al 23 and by Andersen et al 24 emphazise the importance of considering competing (terminal) events also in analyses of recurrent events. See 21,25,26 for suggestions on the analysis of recurrent AEs.…”

Section: Variance Estimatorsmentioning

confidence: 99%

Estimating and comparing adverse event probabilities in the presence of varying follow-up times and competing events

Stegherr,

Schmoor,

Lübbert

et al. 2020

Preprint

View full text Add to dashboard Cite

Safety analyses in terms of adverse events (AEs) are an important aspect of benefitrisk assessments of therapies. Compared to efficacy analyses AE analyses are often rather simplistic. The probability of an AE of a specific type is typically estimated by the incidence proportion, sometimes the incidence density or the Kaplan-Meier estimator are proposed. But these analyses either do not account for censoring, rely on a too restrictive parametric model, or ignore competing events. With the nonparametric Aalen-Johansen estimator as the gold-standard, these potential sources of bias are investigated in a data example from oncology and in simulations, both in the one-sample and in the two-sample case. As the estimators may have large variances at the end of follow-up, the estimators are not only compared at the maximal event time but also at two quantiles of the observed times. To date, consequences for safety comparisons have hardly been investigated in the literature. The impact of using different estimators for group comparisons is unclear, as, for example, the ratio of two both underestimating or overestimating estimators may or may not be comparable to the ratio of the gold-standard estimator. Therefore, the ratio of the AE probabilities is also calculated based on different approaches. By simulations investigating constant and non-constant hazards, different censoring mechanisms and event frequencies, we show that ignoring competing events is more of a problem than falsely assuming constant hazards by use of the incidence density and that the choice of the AE probability estimator is crucial for group comparisons.

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How to analyze and interpret recurrent events data in the presence of a terminal event: An application on readmission after colorectal cancer surgery

Cited by 16 publications

References 49 publications

Towards Filling the Gaps around Recurrent Events in High-Dimensional Framework: Literature Review and Early Comparison

Towards Filling the Gaps around Recurrent Events in High-Dimensional Framework: Literature Review and Early Comparison

Estimating and comparing adverse event probabilities in the presence of varying follow‐up times and competing events

Estimating and comparing adverse event probabilities in the presence of varying follow-up times and competing events

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