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 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..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:40 |
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| Enthalten in: |
Statistics in Medicine - 40(2021), 9, Seite 2177-2196 |
| Beteiligte Personen: |
Conner, Sarah C. [VerfasserIn] |
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| BKL: |
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| Anmerkungen: |
© 2021 John Wiley & Sons, Ltd. |
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| Umfang: |
20 |
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| doi: |
10.1002/sim.8896 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a 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. | ||
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