Ina Jazić1, Deborah Schrag2, Daniel J Sargent2, Sebastien Haneuse2. 1. 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. 2. 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).
Abstract
BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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: Javier Cortes; Joyce O'Shaughnessy; David Loesch; Joanne L Blum; Linda T Vahdat; Katarina Petrakova; Philippe Chollet; Alexey Manikas; Veronique Diéras; Thierry Delozier; Vladimir Vladimirov; Fatima Cardoso; Han Koh; Philippe Bougnoux; Corina E Dutcus; Seth Seegobin; Denis Mir; Nicole Meneses; Jantien Wanders; Chris Twelves Journal: Lancet Date: 2011-03-02 Impact factor: 79.321
Authors: Luís Meira-Machado; Jacobo de Uña-Alvarez; Carmen Cadarso-Suárez; Per K Andersen Journal: Stat Methods Med Res Date: 2008-06-18 Impact factor: 3.021
Authors: Marniker Wijesinha; Jon Mark Hirshon; Michael Terrin; Laurence Magder; Clayton Brown; Kristen Stafford; Aldo Iacono Journal: JAMA Netw Open Date: 2019-08-02