Study Results
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Basic Information
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COMPLETED
5194 participants
OBSERVATIONAL
2013-01-31
Brief Summary
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One difficulty, common to prognostic studies of cancer, concerns the need to separate the effects of prognostic factors on different clinical endpoints, such as disease recurrence vs recurrence-free death. Another difficulty, encountered in prognostic studies, is that the cause of death is not available or not accurately coded. Yet, some patients are likely to die of causes not related to the disease of primary interest, especially in cancers with longer survival and in those that affect older subjects. Until recently, the existing statistical methodology was not able to simultaneously, deal with both difficulties, i.e. to account for (i) possibly different effects of prognostic factors on death vs recurrence, and (ii) unknown causes of death. However, this challenge has been addressed by the recent development of the Markov relative survival model (MRS) , which extends the Markov multi-state model to incorporate relative survival modelling. Simulations demonstrate that MRS is able to accurately estimate different effects of prognostic factors on the risk of each of several events, including separate effects on disease-specific vs other causes of death. To date, the MRS had not been applied in clinical or epidemiological studies.
The aim of this study was to assess the potential advantages of the new multi-state relative survival model (MRS), proposed by Huszti et al. (2012), in a prognostic cancer study. To this end, we compared the MRS results with those obtained with two more conventional analyses, based on Cox's proportional hazards model, and the multi-state Markov model proposed by Alioum and Commenges (2001). The three models were applied to explore the impact of prognostic factors on cancer-specific mortality and recurrence, in a large population-based French registry of colorectal cancer, with up to 25 years of follow-up.
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Detailed Description
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Conditions
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Study Design
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COHORT
RETROSPECTIVE
Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
ALL
No
Sponsors
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Institut National de la Santé Et de la Recherche Médicale, France
OTHER_GOV
Centre Hospitalier Universitaire Dijon
OTHER
Responsible Party
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Locations
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Chu Dijon Bourgogne
Dijon, , France
Countries
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Other Identifiers
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PIOC 2015
Identifier Type: -
Identifier Source: org_study_id
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