title: Beyond Composite Endpoints Analysis: Semicompeting Risks as an Underutilized Framework for Cancer Research.
abstract: Composite endpoints (CEP), such as progression-free survival, are commonly used in cancer research. Notwithstanding their popularity, however, CEP analyses suffer from a number of drawbacks, especially when death is combined with a nonterminal event (ie, progression or recurrence), exemplifying the semicompeting risks setting. We investigated the semicompeting risks framework as a complementary analysis strategy that avoids certain drawbacks of CEPs.
  
  
  
  The illness-death model under the semicompeting risks framework was compared with standard analysis approaches: CEP analyses and (separate) univariate analyses for each component endpoint. Data from a previously published phase III randomized clinical trial in metastatic colon cancer including 1419 participants in the N9741 trial (conducted between 1997 and 2003) were used to determine the impact of the loss of information associated with combining multiple endpoints, as well as of ignoring the potentially informative role of death. A simulation study was conducted to further explore these issues.
  
  
  
  Failure to account for critical features of semicompeting risks data can lead to potentially severely misleading conclusions. Advantages of semicompeting risks analyses include a clear delineation of treatment effects on both events, the ability to draw conclusions about a patient's joint risk of the two events, and an assessment of the dependence between the two event types.
  
  
  
  Embedding and analyzing component outcomes in the semicompeting risks framework, either as a supplement or alternative to CEP analyses, represents an important, underutilized, and feasible opportunity for cancer research.
authors.affiliation: Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA (IJ, SH); Division of Population Sciences, Dana Farber Cancer Institute, Boston, MA (DS); Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN (DJS) ijazic@fas.harvard.edu.
authors.forename: Ina
authors.initials: I
authors.lastname: Jazić
authors.affiliation: Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA (IJ, SH); Division of Population Sciences, Dana Farber Cancer Institute, Boston, MA (DS); Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN (DJS).
authors.forename: Deborah
authors.initials: D
authors.lastname: Schrag
authors.affiliation: Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA (IJ, SH); Division of Population Sciences, Dana Farber Cancer Institute, Boston, MA (DS); Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN (DJS).
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Sargent
authors.affiliation: Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA (IJ, SH); Division of Population Sciences, Dana Farber Cancer Institute, Boston, MA (DS); Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN (DJS).
authors.forename: Sebastien
authors.initials: S
authors.lastname: Haneuse
mesh_terms: D000971:Antineoplastic Combined Chemotherapy Protocols / Q000627:therapeutic use; D035843:Biomedical Research / Q000379:methods / Q000706:statistics & numerical data*; D002986:Clinical Trials as Topic; D015179:Colorectal Neoplasms / Q000188:drug therapy* / Q000473:pathology; D003198:Computer Simulation; D003643:Death; D018450:Disease Progression; D018572:Disease-Free Survival; D023381:Endpoint Determination / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D009362:Neoplasm Metastasis; D018570:Risk Assessment