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Parametric Estimation of the Mean Number of Events in the Presence of Competing Risks

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Název: Parametric Estimation of the Mean Number of Events in the Presence of Competing Risks
Autoři: Joshua P. Entrop, Lasse H. Jakobsen, Michael J. Crowther, Mark Clements, Sandra Eloranta, Caroline E. Dietrich
Zdroj: Biom J
Entrop, J P, Jakobsen, L H, Crowther, M J, Clements, M, Eloranta, S & Dietrich, C E 2025, 'Parametric Estimation of the Mean Number of Events in the Presence of Competing Risks', Biometrical Journal, vol. 67, no. 1, e70038. https://doi.org/10.1002/bimj.70038
Informace o vydavateli: Wiley, 2025.
Rok vydání: 2025
Témata: Risk, Biometry, Models, Statistical, Patient Readmission, survival analysis, flexible parametric survival models, recurrent events, Biometry/methods, competing events, Recurrence, Humans, Patient Readmission/statistics & numerical data, Colorectal Neoplasms, Research Article
Popis: Recurrent events, for example, hospitalizations or drug prescriptions, are common in time‐to‐event research. One useful summary measure of the recurrent event process is the mean number of events. Methods for estimating the mean number of events exist and are readily implemented for situations in which the recurrent event is the only possible outcome. However, estimation gets more challenging in the competing risk setting, in which methods are so far limited to nonparametric approaches. To this end, we propose a postestimation command for estimating the mean number of events in the presence of competing risks by jointly modeling the intensity function of the recurrent event and the survival function for the competing events. The proposed method is implemented in the R‐package JointFPM which is available on CRAN. Simulations demonstrate low bias and good coverage in scenarios where the intensity of the recurrent event does not depend on the number of previous events. We illustrate our method using data on readmissions after colorectal cancer surgery included in the frailtypack package for R. Estimates of the mean number of events can be used to augment time‐to‐event analyses when both recurrent and competing events exist. The proposed parametric approach offers estimation of a smooth function across time as well as easy estimation of different contrasts which is not available using a nonparametric approach.
Druh dokumentu: Article
Other literature type
Popis souboru: application/pdf
Jazyk: English
ISSN: 1521-4036
0323-3847
DOI: 10.1002/bimj.70038
Přístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/39967277
https://vbn.aau.dk/ws/files/773352953/Entrop_et_al._2025_._Parametric_Estimation_of_the_Mean_Number_of_Events_in_the_Presence_of_Competing_Risks.pdf
https://vbn.aau.dk/da/publications/0afb1dad-d46e-4dd8-8baa-a59b7114e5dd
http://www.scopus.com/inward/record.url?scp=85219137154&partnerID=8YFLogxK
https://doi.org/10.1002/bimj.70038
Rights: CC BY
Přístupové číslo: edsair.doi.dedup.....131b7859ce4fdebcfa7adf23f70a2c9c
Databáze: OpenAIRE
Popis
Abstrakt:Recurrent events, for example, hospitalizations or drug prescriptions, are common in time‐to‐event research. One useful summary measure of the recurrent event process is the mean number of events. Methods for estimating the mean number of events exist and are readily implemented for situations in which the recurrent event is the only possible outcome. However, estimation gets more challenging in the competing risk setting, in which methods are so far limited to nonparametric approaches. To this end, we propose a postestimation command for estimating the mean number of events in the presence of competing risks by jointly modeling the intensity function of the recurrent event and the survival function for the competing events. The proposed method is implemented in the R‐package JointFPM which is available on CRAN. Simulations demonstrate low bias and good coverage in scenarios where the intensity of the recurrent event does not depend on the number of previous events. We illustrate our method using data on readmissions after colorectal cancer surgery included in the frailtypack package for R. Estimates of the mean number of events can be used to augment time‐to‐event analyses when both recurrent and competing events exist. The proposed parametric approach offers estimation of a smooth function across time as well as easy estimation of different contrasts which is not available using a nonparametric approach.
ISSN:15214036
03233847
DOI:10.1002/bimj.70038