Adjusted restricted mean survival times in observational studies

In observational studies with censored data, exposure‐outcome associations are commonly measured with adjusted hazard ratios from multivariable Cox proportional hazards models. The difference in restricted mean survival times (RMSTs) up to a pre‐specified time point is an alternative measure that of...

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Bibliographic Details
Published in:Statistics in medicine Vol. 38; no. 20; pp. 3832 - 3860
Main Authors: Conner, Sarah C., Sullivan, Lisa M., Benjamin, Emelia J., LaValley, Michael P., Galea, Sandro, Trinquart, Ludovic
Format: Journal Article
Language:English
Published: England Wiley Subscription Services, Inc 10.09.2019
Subjects:
Analysis of Variance
Cardiovascular disease
Computer Simulation
Health risk assessment
Humans
inverse probability weighting
Kaplan-Meier Estimate
Monte Carlo Method
Observational studies
Observational Studies as Topic - methods
propensity score
restricted mean survival time
Survival Analysis
time‐to‐event data
inverse probability weighting
survival analysis
observational studies
restricted mean survival time
time-to-event data
propensity score
ISSN:0277-6715, 1097-0258, 1097-0258
Online Access:Get full text
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Summary:In observational studies with censored data, exposure‐outcome associations are commonly measured with adjusted hazard ratios from multivariable Cox proportional hazards models. The difference in restricted mean survival times (RMSTs) up to a pre‐specified time point is an alternative measure that offers a clinically meaningful interpretation. Several regression‐based methods exist to estimate an adjusted difference in RMSTs, but they digress from the model‐free method of taking the area under the survival function. We derive the adjusted RMST by integrating an adjusted Kaplan‐Meier estimator with inverse probability weighting (IPW). The adjusted difference in RMSTs is the area between the two IPW‐adjusted survival functions. In a Monte Carlo‐type simulation study, we demonstrate that the proposed estimator performs as well as two regression‐based approaches: the ANCOVA‐type method of Tian et al and the pseudo‐observation method of Andersen et al. We illustrate the methods by reexamining the association between total cholesterol and the 10‐year risk of coronary heart disease in the Framingham Heart Study.
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Boston University School of Public Health, Department of Biostatistics, 801 Massachusetts Avenue, Boston, MA 02118, USA
Present Address
ISSN:0277-6715
1097-0258
1097-0258
DOI:10.1002/sim.8206