Individualized dynamic prediction of prostate cancer recurrence with and without the initiation of a second treatment: Development and validation.
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.
INSERM, Centre INSERM U897-Epidemiologie-Biostatistique, Bordeaux, France.|Université de Bordeaux, ISPED, Bordeaux, France.
Mbéry
M
Sène
Department of Biostatistics, Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
Jeremy Mg
JM
Taylor
Department of Health Studies, University of Chicago, Chicago, IL, USA.|Radiation Therapy Oncology Group, American College of Radiology, Philadelphia, PA, USA.
James J
JJ
Dignam
INSERM, Centre INSERM U897-Epidemiologie-Biostatistique, Bordeaux, France.|Université de Bordeaux, ISPED, Bordeaux, France.
Hélène
H
Jacqmin-Gadda
INSERM, Centre INSERM U897-Epidemiologie-Biostatistique, Bordeaux, France cecile.proust-lima@inserm.fr.|Université de Bordeaux, ISPED, Bordeaux, France.
Cécile
C
Proust-Lima
D006801:Humans; D008297:Male; D009364:Neoplasm Recurrence, Local / Q000097:blood / Q000175:diagnosis*; D011379:Prognosis; D017430:Prostate-Specific Antigen / Q000097:blood; D011471:Prostatic Neoplasms / Q000097:blood / Q000175:diagnosis* / Q000188:drug therapy*; D015203:Reproducibility of Results; D018570:Risk Assessment
D017430:Prostate-Specific Antigen
Brier score; dynamic predictions; hormonal treatment; joint model; prognostic cross-entropy; prostate cancer; shared random-effect models