SEMINAR NOTICE: “Dynamic prediction in Survival analysis: an application to patients with high-grade extremity soft tissue sarcoma”
Mathematical Institute Leiden University, Medical Statistics, Department of Biomedical Data Sciences Leiden University Medical Center, The Netherlands
|When:||Wednesday 27th February 2019|
|Time:||14:30:00 Aula F. Saleri|
There is increasing interest in personalized prediction of disease progression. Prediction models are statistical models based on patient and disease characteristics which are used to inform treatment decisions, to provide personalized risk estimates for a patient. Many available prediction models are limited to predictions from a spe- cific baseline time like diagnosis or shortly before treatment is initiated. It is at this time that the most important decisions on primary treatment are made. It is well known that available prognostic models are important tools for physicians to guide treatment decisions at diagnosis. However, once pri- mary treatment has been initiated, the prognosis of the patient will change over the course of time, as a result of the effect of treatment, like treatment toxicity, clinical events such as disease recurrence that may have occurred, or simply, because of the fact that the patient is still alive. This implies that prediction models need to be updated by using new information about a specific patient that has become available since baseline. Prediction models that incorporate this dynamic aspect are called dynamic prediction models. In the first part of the talk the methodology for dynamic prediction will be discussed. The dynamic aspect of dynamic prediction use information on events and/or measurements up to the present, in order to update the prediction. It will be shown how dynamic predictions may be obtained using the concept of landmarking. In the second part of the talk a dynamic prediction model of survival for patients with high-grade extremity soft tissue sarcoma, will be presented. The model provides updated 5-year survival probabilities from different pre- diction time points during follow-up. Baseline covariates as well as time-dependent covariates, such as status of local recurrence and distant metastases, are included in the model. This dynamic prediction model which updates survival probabilities over time can be used to make better individ- ualized treatment decisions based on a dynamic assessment of a patient’s prognosis.
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