Modelos multiestado para análisis de supervivencia en cardiología: una alternativa a los composite endpoints

  1. Natalia Montoya 1
  2. Alicia Quirós 2
  3. José M. de la Torre Hernández 2
  4. Armando Pérez de Prado 3
  1. 1 Departamento de Matemáticas, Universidad de León, León, España
  2. 2 Servicio de Cardiología, Hospital Universitario Marqués de Valdecilla, Instituto de Investigación Sanitaria Valdecilla (IDIVAL), Santander, Cantabria, España
  3. 3 Servicio de Cardiología, Complejo Asistencial Universitario de León, León, España
Show affiliations +
Journal:
REC: Interventional Cardiology

ISSN: 2604-7276,  2604-7306

Year of publication: 2022

Volume: 4

Issue: 3

Pages: 246-248

Type: Article

DOI: 10.24875/RECIC.M22000270 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

More publications in: REC: Interventional Cardiology

Bibliographic References

  • 1. Hajihosseini M, Kazemi T, Faradmal J. Multistate models for survival analysis of cardiovascular disease process. Rev Esp Cardiol. 2016;69:710-716.
  • 2. Hara H, van Klaveren D, Kogame N, et al. Statistical methods for composite endpoints. EuroIntervention. 2021;16:1484-1495.
  • 3. Meira-Machado L, de Uña-Álvarez J, Cadarso-Suárez C, Andersen P. Multistate models for the analysis of time-to-event data. Stat Methods Med Res. 2009;18:195-222.
  • 4. De la Torre Hernandez J, Moreno R, Gonzalo N, et al. The Pt-Cr everolimus stent with bioabsorbable polymer in the treatment of patients with acute coronary syndromes. Results from the SYNERGY ACS registry. Cardiovasc Revasc Med. 2018;20:705-710.
  • 5. Jackson CH. Multi-State Models for Panel Data:The msm Package for R. J Stat Softw. 2011;38:1-29.
  • 6. Armero C, Cabras S, Castellanos ME, et al. Bayesian analysis of a disability model for lung cancer survival. Stat Methods Med Res. 2016;25:336-351.
Data source: Dialnet