title: On the restricted mean survival time curve in survival analysis.
abstract: For a study with an event time as the endpoint, its survival function contains all the information regarding the temporal, stochastic profile of this outcome variable. The survival probability at a specific time point, say t, however, does not transparently capture the temporal profile of this endpoint up to t. An alternative is to use the restricted mean survival time (RMST) at time t to summarize the profile. The RMST is the mean survival time of all subjects in the study population followed up to t, and is simply the area under the survival curve up to t. The advantages of using such a quantification over the survival rate have been discussed in the setting of a fixed-time analysis. In this article, we generalize this approach by considering a curve based on the RMST over time as an alternative summary to the survival function. Inference, for instance, based on simultaneous confidence bands for a single RMST curve and also the difference between two RMST curves are proposed. The latter is informative for evaluating two groups under an equivalence or noninferiority setting, and quantifies the difference of two groups in a time scale. The proposal is illustrated with the data from two clinical trials, one from oncology and the other from cardiology.
authors: Department of Preventive Medicine, Northwestern University, Chicago, Illinois 60611, U.S.A.
authors: Lihui
authors: L
authors: Zhao
authors: Division of Cardiovascular Medicine, Brigham & Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, U.S.A.
authors: Brian
authors: B
authors: Claggett
authors: Department of Health Research and Policy, Stanford University, Stanford, California 94305, U.S.A.
authors: Lu
authors: L
authors: Tian
authors: Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, U.S.A.
authors: Hajime
authors: H
authors: Uno
authors: Division of Cardiovascular Medicine, Brigham & Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, U.S.A.
authors: Marc A
authors: MA
authors: Pfeffer
authors: Division of Cardiovascular Medicine, Brigham & Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, U.S.A.
authors: Scott D
authors: SD
authors: Solomon
authors: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, U.S.A.|Department of Biostatistics, Harvard University, Boston, Massachusetts 02115, U.S.A.
authors: Lorenzo
authors: L
authors: Trippa
authors: Department of Biostatistics, Harvard University, Boston, Massachusetts 02115, U.S.A.
authors: L J
authors: LJ
authors: Wei
mesh_terms: D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D023381:Endpoint Determination / Q000379:methods*; D006801:Humans; D053208:Kaplan-Meier Estimate; D008017:Life Expectancy; D015233:Models, Statistical; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013269:Stochastic Processes; D015996:Survival Rate
keywords: Equivalence/noninferiority study; Gaussian process; Martingale; Simultaneous confidence band; Survival function