Chapter 18: The LASSO method in variable selection for right censored time-to-event data with specific application to Chronic Myelogonous Leukemia

Lili Yu; Dennis K. Pearl

Chapter 18: The LASSO method in variable selection for right censored time-to-event data with specific application to Chronic Myelogonous Leukemia

Lili Yu, Dennis K. Pearl

Biostatistics, Epidemiology & Environmental Health Sciences

Ohio State University

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Abstract

<p> We propose models of potential relationships in time-to-event data and we infer whether these associations occur based on the support of the available data. Our inferences are only as good as the models that we use to de&filig;ne these associations of interest. Cox proportional hazard model [1,2] is the most popular model used in survival analysis due to the computational simplicity of the inference methods and well-established asymptotic properties of the partial likelihood. However, the proportional hazards assumption is not always true since the hazard ratio for real data often converges to 1 as time increases.</p>

Original language	American English
Title of host publication	Design and Analysis of Clinical Trials with Time-to-Event Endpoints
State	Published - Jan 1 2009

Disciplines

Biostatistics
Environmental Public Health
Epidemiology
Public Health

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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https://www.taylorfrancis.com/books/e/9780429191954/chapters/10.1201/9781420066401-23

Cite this

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title = "Chapter 18: The LASSO method in variable selection for right censored time-to-event data with specific application to Chronic Myelogonous Leukemia",

abstract = " We propose models of potential relationships in time-to-event data and we infer whether these associations occur based on the support of the available data. Our inferences are only as good as the models that we use to de&filig;ne these associations of interest. Cox proportional hazard model [1,2] is the most popular model used in survival analysis due to the computational simplicity of the inference methods and well-established asymptotic properties of the partial likelihood. However, the proportional hazards assumption is not always true since the hazard ratio for real data often converges to 1 as time increases.",

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AB - We propose models of potential relationships in time-to-event data and we infer whether these associations occur based on the support of the available data. Our inferences are only as good as the models that we use to de&filig;ne these associations of interest. Cox proportional hazard model [1,2] is the most popular model used in survival analysis due to the computational simplicity of the inference methods and well-established asymptotic properties of the partial likelihood. However, the proportional hazards assumption is not always true since the hazard ratio for real data often converges to 1 as time increases.

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UR - https://www.taylorfrancis.com/books/e/9780429191954/chapters/10.1201/9781420066401-23

M3 - Chapter

BT - Design and Analysis of Clinical Trials with Time-to-Event Endpoints

ER -

Chapter 18: The LASSO method in variable selection for right censored time-to-event data with specific application to Chronic Myelogonous Leukemia

Abstract

Disciplines

UN SDGs

Access to Document

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