Parametric mixture models to evaluate and summarize hazard ratios in the presence of competing risks with time-dependent hazards and delayed entry

Bryan M Lau, Stephen R. Cole, Stephen J Gange

Research output: Contribution to journalArticle

Abstract

In the analysis of survival data, there are often competing events that preclude an event of interest from occurring. Regression analysis with competing risks is typically undertaken using a cause-specific proportional hazards model. However, modern alternative methods exist for the analysis of the subdistribution hazard with a corresponding subdistribution proportional hazards model. In this paper, we introduce a flexible parametric mixture model as a unifying method to obtain estimates of the cause-specific and subdistribution hazards and hazard-ratio functions. We describe how these estimates can be summarized over time to give a single number comparable to the hazard ratio that is obtained from a corresponding cause-specific or subdistribution proportional hazards model. An application to the Women's Interagency HIV Study is provided to investigate injection drug use and the time to either the initiation of effective antiretroviral therapy, or clinical disease progression as a competing event.

Original languageEnglish (US)
Pages (from-to)654-665
Number of pages12
JournalStatistics in Medicine
Volume30
Issue number6
DOIs
StatePublished - Mar 15 2011

Fingerprint

Competing Risks
Parametric Model
Mixture Model
Proportional Hazards Models
Hazard
Proportional Hazards Model
Evaluate
Cause-specific Hazard
Survival Analysis
Disease Progression
Survival Data
Progression
Regression Analysis
HIV
Estimate
Therapy
Injection
Drugs
Injections
Pharmaceutical Preparations

Keywords

  • Cause-specific hazards
  • Competing risks
  • Hazard ratio
  • Mixture model
  • Subdistribution
  • Subdistribution hazards
  • Survival analysis

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

Cite this

@article{23b726107c1e404081a5f700b3925bb3,
title = "Parametric mixture models to evaluate and summarize hazard ratios in the presence of competing risks with time-dependent hazards and delayed entry",
abstract = "In the analysis of survival data, there are often competing events that preclude an event of interest from occurring. Regression analysis with competing risks is typically undertaken using a cause-specific proportional hazards model. However, modern alternative methods exist for the analysis of the subdistribution hazard with a corresponding subdistribution proportional hazards model. In this paper, we introduce a flexible parametric mixture model as a unifying method to obtain estimates of the cause-specific and subdistribution hazards and hazard-ratio functions. We describe how these estimates can be summarized over time to give a single number comparable to the hazard ratio that is obtained from a corresponding cause-specific or subdistribution proportional hazards model. An application to the Women's Interagency HIV Study is provided to investigate injection drug use and the time to either the initiation of effective antiretroviral therapy, or clinical disease progression as a competing event.",
keywords = "Cause-specific hazards, Competing risks, Hazard ratio, Mixture model, Subdistribution, Subdistribution hazards, Survival analysis",
author = "Lau, {Bryan M} and Cole, {Stephen R.} and Gange, {Stephen J}",
year = "2011",
month = "3",
day = "15",
doi = "10.1002/sim.4123",
language = "English (US)",
volume = "30",
pages = "654--665",
journal = "Statistics in Medicine",
issn = "0277-6715",
publisher = "John Wiley and Sons Ltd",
number = "6",

}

TY - JOUR

T1 - Parametric mixture models to evaluate and summarize hazard ratios in the presence of competing risks with time-dependent hazards and delayed entry

AU - Lau, Bryan M

AU - Cole, Stephen R.

AU - Gange, Stephen J

PY - 2011/3/15

Y1 - 2011/3/15

N2 - In the analysis of survival data, there are often competing events that preclude an event of interest from occurring. Regression analysis with competing risks is typically undertaken using a cause-specific proportional hazards model. However, modern alternative methods exist for the analysis of the subdistribution hazard with a corresponding subdistribution proportional hazards model. In this paper, we introduce a flexible parametric mixture model as a unifying method to obtain estimates of the cause-specific and subdistribution hazards and hazard-ratio functions. We describe how these estimates can be summarized over time to give a single number comparable to the hazard ratio that is obtained from a corresponding cause-specific or subdistribution proportional hazards model. An application to the Women's Interagency HIV Study is provided to investigate injection drug use and the time to either the initiation of effective antiretroviral therapy, or clinical disease progression as a competing event.

AB - In the analysis of survival data, there are often competing events that preclude an event of interest from occurring. Regression analysis with competing risks is typically undertaken using a cause-specific proportional hazards model. However, modern alternative methods exist for the analysis of the subdistribution hazard with a corresponding subdistribution proportional hazards model. In this paper, we introduce a flexible parametric mixture model as a unifying method to obtain estimates of the cause-specific and subdistribution hazards and hazard-ratio functions. We describe how these estimates can be summarized over time to give a single number comparable to the hazard ratio that is obtained from a corresponding cause-specific or subdistribution proportional hazards model. An application to the Women's Interagency HIV Study is provided to investigate injection drug use and the time to either the initiation of effective antiretroviral therapy, or clinical disease progression as a competing event.

KW - Cause-specific hazards

KW - Competing risks

KW - Hazard ratio

KW - Mixture model

KW - Subdistribution

KW - Subdistribution hazards

KW - Survival analysis

UR - http://www.scopus.com/inward/record.url?scp=79951697665&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79951697665&partnerID=8YFLogxK

U2 - 10.1002/sim.4123

DO - 10.1002/sim.4123

M3 - Article

C2 - 21337360

AN - SCOPUS:79951697665

VL - 30

SP - 654

EP - 665

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 0277-6715

IS - 6

ER -