On the restricted mean survival time curve in survival analysis.

Lihui Zhao, Brian Claggett, Lu Tian, Hajime Uno, Marc A Pfeffer, Scott D Solomon, Lorenzo Trippa, L J Wei
Author Information
  1. Lihui Zhao: Department of Preventive Medicine, Northwestern University, Chicago, Illinois 60611, U.S.A.
  2. Brian Claggett: Division of Cardiovascular Medicine, Brigham & Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, U.S.A.
  3. Lu Tian: Department of Health Research and Policy, Stanford University, Stanford, California 94305, U.S.A.
  4. Hajime Uno: Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, U.S.A.
  5. Marc A Pfeffer: Division of Cardiovascular Medicine, Brigham & Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, U.S.A.
  6. Scott D Solomon: Division of Cardiovascular Medicine, Brigham & Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, U.S.A.
  7. Lorenzo Trippa: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, U.S.A.
  8. L J Wei: Department of Biostatistics, Harvard University, Boston, Massachusetts 02115, U.S.A.

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.

Keywords

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Grants

  1. R01-GM079330/NIGMS NIH HHS
  2. R01-HL089778/NHLBI NIH HHS
  3. UM1AI068634/NIAID NIH HHS
  4. R01 HL089778/NHLBI NIH HHS
  5. R01-AI024643/NIAID NIH HHS
  6. UM1 AI068634/NIAID NIH HHS
  7. R01 AI024643/NIAID NIH HHS
  8. R01 GM079330/NIGMS NIH HHS
  9. R21-AG049385/NIA NIH HHS
  10. UM1 AI068616/NIAID NIH HHS
  11. UM1AI068616/NIAID NIH HHS
  12. R21 AG049385/NIA NIH HHS

MeSH Term

Computer Simulation
Data Interpretation, Statistical
Endpoint Determination
Humans
Kaplan-Meier Estimate
Life Expectancy
Models, Statistical
Reproducibility of Results
Sensitivity and Specificity
Stochastic Processes
Survival Rate

Word Cloud

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