Modeling recurrence in colorectal cancer

Dancourt, Vincent; Quantin, Catherine; Abrahamowicz, Michał; Binquet, Christine; Alioum, Ahmadou; Faivre, Jean

doi:10.1016/j.jclinepi.2003.07.012

Cited by 39 publications

(50 citation statements)

References 43 publications

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“…This method also accounted for adherence and duration of treatment. [21][22][23] We also assessed adherence to nonrestricted cardiovascular drugs by calculating the percentage of days on the same drug class during the follow-up period. Discrete data are presented as percentage, and continuous data are presented as mean (standard deviation [SD]) and median (25th-75th percentile) when nonnormally distributed.…”

Section: Discussionmentioning

confidence: 99%

Restrictive access to clopidogrel and mortality following coronary stent implantation

Sheehy¹,

LeLorier²,

Rinfret³

2008

Canadian Medical Association Journal

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Section: Discussionmentioning

confidence: 99%

Restrictive access to clopidogrel and mortality following coronary stent implantation

Sheehy¹,

LeLorier²,

Rinfret³

2008

Canadian Medical Association Journal

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“…Then, representing the number of MS relapses as a fixed-in-time covariate means that the value of this variable will be 3 over the entire period from baseline to the end of the patient's follow-up, 36 months after MS onset. However, this approach may result in a lead time bias and to an underestimation of the pejorative impact of the number of MS attacks [30,31] . Indeed, if we use the case presented above, the patient was considered as having 3 MS attacks immediately after baseline although, in fact, this number should be assigned only to months .…”

Section: Modelling Of the Number Of Relapses During The First 2 Yearsmentioning

confidence: 99%

The Prognostic Value of Initial Relapses on the Evolution of Disability in Patients with Relapsing-Remitting Multiple Sclerosis

et al. 2006

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The evolution of multiple sclerosis (MS) and the resulting disability are unpredictable. To identify clinical variables that could be potential prognostic factors, we followed a cohort of 288 patients diagnosed as having relapsing-remitting MS between 1990 and 2003. The end point was the first occurrence of a non-reversible EDMUS-GS score ≧3 (moderate disability). The impact of the number of MS attacks during the first 2 years of the disease as well as the first interattack interval were assessed in two Cox models, one using a fixed-in-time covariate, the other using a time-dependent covariate. Older age at onset and a higher number of MS attacks during the first 2 years of MS proved to be predictors of unfavourable prognosis. The first interattack interval had no influence on the evolution of the disability, conversely to the first relapse which had a short-term impact on the prognosis. We confirmed that the age at onset and the number of MS attacks during the first 2 years of MS are predictors of the evolution of the disability and demonstrated the importance of using time-dependent covariates.

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“…This approach makes it possible to incorporate all temporal information available. Indeed, it has been shown that ignoring the change of updated covariate values usually result in underestimate of its e ect [13,15] and therefore, it is necessary to use appropriate method to adjust for a time-dependent covariate e ect. We illustrated the importance of representing recurrence as a time-dependent covariate in relative survival analysis.…”

Section: Discussionmentioning

confidence: 99%

“…Further research has to be done to develop relative survival model with competing risks. Multi-state Markov models 'will often provide a relevant modelling framework for event history data' [23] and their advantages have been described [15,23]. These models proposed for crude survival must be adapted in the framework of relative survival.…”

Section: Discussionmentioning

confidence: 99%

“…change in the subject's treatment, recurrence of a disease, repeated measurement on a subject) is often encountered in survival analysis (e.g. References [12][13][14][15]). Specialized analytic methods are required to analyse such data in order to assess the e ect of time-dependent covariate on the role of other prognostic factors and to avoid biased estimates of its impact.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of time‐dependent covariates in a regressive relative survival model

Giorgi¹,

Gouvernet²

2005

Statistics in Medicine

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Relative survival is a method for assessing prognostic factors for disease-specific mortality. However, most relative survival models assume that the effect of covariate on disease-specific mortality is fixed-in-time, which may not hold in some studies and requires adapted modelling. We propose an extension of the Esteve et al. regressive relative survival model that uses the counting process approach to accommodate time-dependent effect of a predictor's on disease-specific mortality. This approach had shown its robustness, and the properties of the counting process give a simple and attractive computational solution to model time-dependent covariates. Our approach is illustrated with the data from the Stanford Heart Transplant Study and with data from a hospital-based study on invasive breast cancer. Advantages of modelling time-dependent covariates in relative survival analysis are discussed.

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Modeling recurrence in colorectal cancer

Cited by 39 publications

References 43 publications

Restrictive access to clopidogrel and mortality following coronary stent implantation

Restrictive access to clopidogrel and mortality following coronary stent implantation

The Prognostic Value of Initial Relapses on the Evolution of Disability in Patients with Relapsing-Remitting Multiple Sclerosis

Analysis of time‐dependent covariates in a regressive relative survival model

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