Estimation and modeling of the restricted mean time lost in the presence of competing risks

Survival data with competing or semi‐competing risks are common in observational studies. As an alternative to cause‐specific and subdistribution hazard ratios, the between‐group difference in cause‐specific restricted mean times lost (RMTL) gives the mean difference in life expectancy lost to a spe...

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Published in:Statistics in medicine Vol. 40; no. 9; pp. 2177 - 2196
Main Authors: Conner, Sarah C., Trinquart, Ludovic
Format: Journal Article
Language:English
Published: England Wiley Subscription Services, Inc 01.04.2021
Subjects:
competing risks
Computer Simulation
cumulative incidence
Female
Gender differences
Humans
Male
Probability
Proportional Hazards Models
restricted mean survival
restricted mean time lost
survival analysis
restricted mean survival
competing risks
restricted mean time lost
survival analysis
cumulative incidence
ISSN:0277-6715, 1097-0258, 1097-0258
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Abstract Survival data with competing or semi‐competing risks are common in observational studies. As an alternative to cause‐specific and subdistribution hazard ratios, the between‐group difference in cause‐specific restricted mean times lost (RMTL) gives the mean difference in life expectancy lost to a specific cause of death or in disease‐free time lost, in the case of a nonfatal outcome, over a prespecified period. To adjust for covariates, we introduce an inverse probability weighted estimator and its variance for the marginal difference in RMTL. We also introduce an inverse probability of censoring weighted regression model for the RMTL. In simulation studies, we examined the finite sample performance of the proposed methods under proportional and nonproportional subdistribution hazards scenarios. We illustrated both methods with competing risks data from the Framingham Heart Study. We estimated sex differences in atrial fibrillation (AF)‐free times lost over 40 years. We also estimated sex differences in mean lifetime lost to cardiovascular disease (CVD) and non‐CVD death over 10 years among individuals with AF.
AbstractList Survival data with competing or semi‐competing risks are common in observational studies. As an alternative to cause‐specific and subdistribution hazard ratios, the between‐group difference in cause‐specific restricted mean times lost (RMTL) gives the mean difference in life expectancy lost to a specific cause of death or in disease‐free time lost, in the case of a nonfatal outcome, over a prespecified period. To adjust for covariates, we introduce an inverse probability weighted estimator and its variance for the marginal difference in RMTL. We also introduce an inverse probability of censoring weighted regression model for the RMTL. In simulation studies, we examined the finite sample performance of the proposed methods under proportional and nonproportional subdistribution hazards scenarios. We illustrated both methods with competing risks data from the Framingham Heart Study. We estimated sex differences in atrial fibrillation (AF)‐free times lost over 40 years. We also estimated sex differences in mean lifetime lost to cardiovascular disease (CVD) and non‐CVD death over 10 years among individuals with AF.
Survival data with competing or semi-competing risks are common in observational studies. As an alternative to cause-specific and subdistribution hazard ratios, the between-group difference in cause-specific restricted mean times lost (RMTL) gives the mean difference in life expectancy lost to a specific cause of death or in disease-free time lost, in the case of a nonfatal outcome, over a pre-specified period. To adjust for covariates, we introduce an inverse probability weighted estimator and its variance for the marginal difference in RMTL. We also introduce an inverse probability of censoring weighted regression model for the RMTL. In simulation studies, we examined the finite sample performance of the proposed methods under proportional and nonproportional subdistribution hazards scenarios. We illustrated both methods with competing risks data from the Framingham Heart Study. We estimated sex differences in atrial fibrillation (AF)-free times lost over 40 years. We also estimated sex differences in mean lifetime lost to cardiovascular disease (CVD) and non-CVD death over 10 years among individuals with AF.
Survival data with competing or semi-competing risks are common in observational studies. As an alternative to cause-specific and subdistribution hazard ratios, the between-group difference in cause-specific restricted mean times lost (RMTL) gives the mean difference in life expectancy lost to a specific cause of death or in disease-free time lost, in the case of a nonfatal outcome, over a prespecified period. To adjust for covariates, we introduce an inverse probability weighted estimator and its variance for the marginal difference in RMTL. We also introduce an inverse probability of censoring weighted regression model for the RMTL. In simulation studies, we examined the finite sample performance of the proposed methods under proportional and nonproportional subdistribution hazards scenarios. We illustrated both methods with competing risks data from the Framingham Heart Study. We estimated sex differences in atrial fibrillation (AF)-free times lost over 40 years. We also estimated sex differences in mean lifetime lost to cardiovascular disease (CVD) and non-CVD death over 10 years among individuals with AF.Survival data with competing or semi-competing risks are common in observational studies. As an alternative to cause-specific and subdistribution hazard ratios, the between-group difference in cause-specific restricted mean times lost (RMTL) gives the mean difference in life expectancy lost to a specific cause of death or in disease-free time lost, in the case of a nonfatal outcome, over a prespecified period. To adjust for covariates, we introduce an inverse probability weighted estimator and its variance for the marginal difference in RMTL. We also introduce an inverse probability of censoring weighted regression model for the RMTL. In simulation studies, we examined the finite sample performance of the proposed methods under proportional and nonproportional subdistribution hazards scenarios. We illustrated both methods with competing risks data from the Framingham Heart Study. We estimated sex differences in atrial fibrillation (AF)-free times lost over 40 years. We also estimated sex differences in mean lifetime lost to cardiovascular disease (CVD) and non-CVD death over 10 years among individuals with AF.
Author Conner, Sarah C.
Trinquart, Ludovic
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Issue 9
Keywords restricted mean survival
competing risks
restricted mean time lost
survival analysis
cumulative incidence
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Snippet Survival data with competing or semi‐competing risks are common in observational studies. As an alternative to cause‐specific and subdistribution hazard...
Survival data with competing or semi-competing risks are common in observational studies. As an alternative to cause-specific and subdistribution hazard...
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SubjectTerms competing risks
Computer Simulation
cumulative incidence
Female
Gender differences
Humans
Male
Probability
Proportional Hazards Models
restricted mean survival
restricted mean time lost
survival analysis
Title Estimation and modeling of the restricted mean time lost in the presence of competing risks
URI https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fsim.8896
https://www.ncbi.nlm.nih.gov/pubmed/33567477
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Volume 40
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