Name: survival tree in time-to-event analysis
Uploaded: 2025-01-09T15:14:17.000Z
Duration: 1 min 19 s
Channel: JoVE

Un árbol de supervivencia se utiliza para modelar y visualizar la relación entre un conjunto de covariables y el tiempo hasta que ocurre un evento de interés. Por lo general, se construye mediante un proceso de partición recursiva.
Las ramas del árbol representan las divisiones en los valores de una variable. Los nodos representan subconjuntos de los datos, y los nodos terminales indican el número de sujetos en el nodo y podrían proporcionar predicciones finales del análisis.
La construcción de un árbol de supervivencia requiere principalmente covariables, criterios de división, tamaño mínimo de nodo y umbrales de poda.
Las covariables o variables predictoras pueden ser continuas, ordinales o categóricas.
Un criterio de división es un método para elegir la mejor división en cada nodo. Se aplica para minimizar el riesgo dentro del nodo o para maximizar el grado de separación entre nodos.
El tamaño mínimo del nodo es el número más pequeño de observaciones necesarias para que un nodo se divida aún más. Esto ayuda a controlar el tamaño del árbol y evita el sobreajuste.
Por último, el umbral de poda es una medida para decidir cuándo dejar de podar el árbol.

Árboles de supervivencia

Survival Tree

Los árboles de supervivencia son un método no paramétrico que se utiliza en el análisis de supervivencia para modelar la relación entre un conjunto de covariables y el tiempo hasta que ocurre un evento de interés, a menudo denominado "tiempo hasta el evento" o "tiempo de supervivencia". Este método es particularmente útil cuando se trabaja con datos censurados, donde el evento no se ha producido para algunos individuos al final del período de estudio, o cuando se desconoce el momento exacto del evento.

Construcción de un árbol de supervivencia

La construcción de un árbol de supervivencia comienza con un conjunto de datos que incluye covariables (variables predictoras) y el tiempo de supervivencia, junto con un indicador de censura para cada sujeto. El proceso implica los siguientes pasos:

<ol>
	<li>Preparación de los datos: el conjunto de datos se prepara asegurándose de que se incluyan todas las covariables necesarias y que tengan el formato adecuado. Los valores faltantes se pueden manejar utilizando métodos como la imputación o tratándolos como una categoría separada.</li>
	<li>Construcción del árbol: el árbol de supervivencia se construye utilizando un proceso de partición recursiva. En cada paso, el conjunto de datos se divide en dos subconjuntos según una covariable que diferencie mejor los resultados de supervivencia. Esto se hace normalmente utilizando un criterio de división como la prueba de log-rank, que compara las distribuciones de supervivencia entre grupos.</li>
	<li>Evaluación de nodos: cada nodo del árbol representa un subconjunto de los datos, y los nodos terminales (hojas) se evalúan según la estimación de Kaplan-Meier de la función de supervivencia. Esto proporciona una estimación de la probabilidad de supervivencia de los sujetos que caen en ese nodo.</li>
	<li>Poda: para evitar el sobreajuste, el árbol se poda eliminando los nodos que no proporcionan una mejora significativa en la precisión del modelo. Este paso garantiza que el árbol sea generalizable a nuevos datos.</li>
</ol>

Ventajas y desventajas

Ventajas:

<ol style="list-style-type: circle;">
	<li>Flexibilidad: los árboles de supervivencia pueden manejar una amplia gama de tipos de datos y son robustos a los valores atípicos y faltantes.</li>
	<li>Interpretabilidad: la estructura del árbol es fácil de interpretar, lo que permite una visualización sencilla de la relación entre las covariables y el tiempo de supervivencia.</li>
	<li>Naturaleza no paramétrica: no requieren suposiciones sobre la distribución de los tiempos de supervivencia o la forma funcional de la relación entre las covariables y la supervivencia.</li>
</ol>

Desventajas:

<ol style="list-style-type: circle;">
	<li>Sobreajuste: sin una poda adecuada, los árboles de supervivencia pueden sobreajustarse a los datos de entrenamiento, lo que conduce a una generalización deficiente.</li>
	<li>Inestabilidad: pequeños cambios en los datos pueden conducir a cambios significativos en la estructura del árbol, haciéndolos menos estables en comparación con otros métodos como los bosques de supervivencia</li>
</ol>

Un árbol de supervivencia se utiliza para modelar y visualizar la relación entre un conjunto de covariables y el tiempo hasta que ocurre un evento de interés. Lo es...

Education

Educación

JoVE Core

Statistics

Estadísticas

Unit 1

Unidad 1

Survival Analysis

Análisis de supervivencia

KEY TERMS AND CONCEPTS

TÉRMINOS Y CONCEPTOS CLAVE

survival tree in time-to-event analysis

A survival tree is a non-parametric tool in survival analysis that links predictor variables, or covariates, to survival time. Survival time is the time until an event of interest occurs. It is useful when some data are censored, which means the event has not happened for some people by the end of the study or the exact event time is unknown.

Building a survival tree starts with a dataset that includes covariates, survival time, and a censoring indicator for each subject. The data must be prepared so the needed covariates are included and formatted correctly. Missing values can be handled by imputation or by treating them as a separate category.

The tree is built through recursive partitioning. At each step, the data are split into two groups based on the covariate that best separates the survival outcomes. A common splitting rule is the log-rank test, which compares survival distributions between groups.

Each node in the tree represents a subset of the data. The terminal nodes, or leaves, are evaluated with the Kaplan-Meier estimate of the survival function. This gives a survival probability estimate for subjects in that node. The tree is then pruned by removing nodes that do not improve model accuracy enough, which helps reduce overfitting and makes the model more useful for new data.

Survival trees have several strengths. They can handle many data types and are robust to outliers and missing values. They are also easy to interpret because the tree structure clearly shows how covariates relate to survival time. As a non-parametric method, they do not require assumptions about the distribution of survival times or the exact form of the relationship between covariates and survival.

They also have limits. Without pruning, survival trees can overfit the training data and perform poorly on new data. They can also be unstable, because small changes in the data may produce a very different tree. This makes them less stable than methods such as survival forests.

Survival Tree in Time-to-Event Analysis

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survival tree

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key terms and concepts

Learn how survival trees use covariates, censoring, and recursive partitioning to model time-to-event data, estimate survival, and manage overfitting.

Survival Tree in Time-to-Event Analysis | JoVE K12

statistics

JoVE Core Estadística presenta a los estudiantes los conceptos y las aplicaciones de la recopilación, análisis, presentación e interpretación de datos. Estas lecciones animadas proporcionan un entendimiento de la estadística a estudiantes de una amplia variedad de disciplinas. Además, los vídeos de científicos en acción muestran la aplicación de los conceptos en experimentos de investigación clásicos y originales, llevados a cabo hoy en día en laboratorios de ámbito internacional.

Survival Tree in Time-to-Event Analysis

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