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Focus on: Survival analyses in cardiovascular research, a practical guide
Available online 6 August 2021
Survival analyses in cardiovascular research, part II: statistical methods in challenging situations
Análisis de supervivencia en investigación cardiovascular (II): metodología estadística en situaciones complejas
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Xavier Rosselloa,b,c,d,
Corresponding author
fjrossello@cnic.es

Corresponding author: Servei de Cardiologia, Hospital Universitari Son Espases - IdISBa, Ctra. de Valldemossa, 79, 07120 Palma de Mallorca, Balearic Islands, Spain.
, Maribel González-Del-Hoyoe
a Servei de Cardiologia, Institut d’Investigació Sanitària Illes Balears (IdISBa), Hospital Universitari Son Espases, Palma de Mallorca, Balearic Islands, Spain
b Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain
c Facultad de Medicina, Universitat de les Illes Balears (UIB), Palma de Mallorca, Balearic Islands, Spain
d Medical Statistics Department, London School of Hygiene & Tropical Medicine, London, United Kingdom
e Servei de Cardiologia, Hospital Universitari Vall d’Hebron, Vall d’Hebron Institut de Recerca, Universitat Autònoma de Barcelona, Barcelona, Spain
Related content
10.1016/j.rec.2021.06.003
Xavier Rossello, Maribel González-Del-Hoyo
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Table 1. Summary of differences between the Cox proportional hazards, Poisson and logistic regression models
Abstract

This article is the second of a series of 2 educational articles. In the first article, we described the basic concepts of survival analysis, summarizing the common statistical methods and providing a set of recommendations to guide the strategy of survival analyses in randomized clinical trials and observational studies. Here, we introduce stratified Cox models and frailty models, as well as the immortal time bias arising from a poor assessment of time-dependent variables. To address the issue of multiplicity of outcomes, we provide several modelling strategies to deal with other types of time-to-event data analyses, such as competing risks, multistate models, and recurrent-event methods. This review is illustrated with examples from previous cardiovascular research publications, and each statistical method is discussed alongside its main strengths and limitations. Finally, we provide some general observations about alternative statistical methods with less restrictive assumptions, such as the win ratio method, the restrictive mean survival time, and accelerated failure time model.

Keywords:
Survival analysis
Cardiovascular disease
Methodology
Abbreviations:
CIF
CPH
CVH
DAPT
HF
MI
PH
RCT
RMST
Resumen

Esta revisión es la segunda parte de 2 artículos sobre metodología estadística. En el primero, se describían los conceptos básicos del análisis de supervivencia y los métodos estadísticos más comúnmente utilizados y se aportaba un conjunto de recomendaciones para ayudar a establecer una estrategia de análisis de supervivencia, tanto en el contexto de un ensayo clínico aleatorizado como en el de un estudio observacional. En este segundo artículo, se introducen el modelo estratificado de Cox y el modelo de fragilidad y se ilustra el sesgo de tiempo inmortal secundario a una evaluación errónea de variables dependientes del tiempo. Para abordar el problema de la existencia de múltiples eventos clínicos, se introducen distintas aproximaciones estadísticas, como el análisis de riesgos competitivos, los modelos multiestado y el modelo de eventos recurrentes. Todos ellos se ilustran con ejemplos del campo cardiovascular, y se resumen las principales ventajas y limitaciones de cada uno de los métodos estadísticos. Por último, se presentan algunas consideraciones generales sobre métodos estadísticos alternativos, con asunciones menos restrictivas, como el método win ratio, el tiempo de supervivencia medio restringido y el modelo de tiempo de evento acelerado.

Palabras clave:
Análisis de supervivencia
Enfermedad cardiovascular
Metodología

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