Abstract:
:With the emergence of rich information on biomarkers after treatments, new types of prognostic tools are being developed: dynamic prognostic tools that can be updated at each new biomarker measurement. Such predictions are of interest in oncology where after an initial treatment, patients are monitored with repeated biomarker data. However, in such setting, patients may receive second treatments to slow down the progression of the disease. This paper aims to develop and validate dynamic individual predictions that allow the possibility of a new treatment in order to help understand the benefit of initiating new treatments during the monitoring period. The prediction of the event in the next x years is done under two scenarios: (1) the patient initiates immediately a second treatment, (2) the patient does not initiate any treatment in the next x years. Predictions are derived from shared random-effect models. Applied to prostate cancer data, different specifications for the dependence between the prostate-specific antigen repeated measures, the initiation of a second treatment (hormonal therapy), and the risk of clinical recurrence are investigated and compared. The predictive accuracy of the dynamic predictions is evaluated with two measures (Brier score and prognostic cross-entropy) for which approximated cross-validated estimators are proposed.
journal_name
Stat Methods Med Resjournal_title
Statistical methods in medical researchauthors
Sène M,Taylor JM,Dignam JJ,Jacqmin-Gadda H,Proust-Lima Cdoi
10.1177/0962280214535763subject
Has Abstractpub_date
2016-12-01 00:00:00pages
2972-2991issue
6eissn
0962-2802issn
1477-0334pii
0962280214535763journal_volume
25pub_type
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