TY - JOUR
T1 - Intermittent androgen suppression
T2 - Estimating parameters for individual patients based on initial PSA data in response to androgen deprivation therapy
AU - Hirata, Yoshito
AU - Morino, Kai
AU - Akakura, Koichiro
AU - Higano, Celestia S.
AU - Bruchovsky, Nicholas
AU - Gambol, Teresa
AU - Hall, Susan
AU - Tanaka, Gouhei
AU - Aihara, Kazuyuki
N1 - Publisher Copyright:
© 2015 Hirata et al.
PY - 2015/6/24
Y1 - 2015/6/24
N2 - When a physician decides on a treatment and its schedule for a specific patient, information gained from prior patients and experience in the past is taken into account. A more objective way to make such treatment decisions based on actual data would be useful to the clinician. Although there are many mathematical models proposed for various diseases, so far there is no mathematical method that accomplishes optimization of the treatment schedule using the information gained from past patients or "rapid learning" technology. In an attempt to use this approach, we integrate the information gained from patients previously treated with intermittent androgen suppression (IAS) with that from a current patient by first fitting the time courses of clinical data observed from the previously treated patients, then constructing the prior information of the parameter values of the mathematical model, and finally, maximizing the posterior probability for the parameters of the current patient using the prior information. Although we used data from prostate cancer patients, the proposed method is general, and thus can be applied to other diseases once an appropriate mathematical model is established for that disease.
AB - When a physician decides on a treatment and its schedule for a specific patient, information gained from prior patients and experience in the past is taken into account. A more objective way to make such treatment decisions based on actual data would be useful to the clinician. Although there are many mathematical models proposed for various diseases, so far there is no mathematical method that accomplishes optimization of the treatment schedule using the information gained from past patients or "rapid learning" technology. In an attempt to use this approach, we integrate the information gained from patients previously treated with intermittent androgen suppression (IAS) with that from a current patient by first fitting the time courses of clinical data observed from the previously treated patients, then constructing the prior information of the parameter values of the mathematical model, and finally, maximizing the posterior probability for the parameters of the current patient using the prior information. Although we used data from prostate cancer patients, the proposed method is general, and thus can be applied to other diseases once an appropriate mathematical model is established for that disease.
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U2 - 10.1371/journal.pone.0130372
DO - 10.1371/journal.pone.0130372
M3 - Article
C2 - 26107379
AN - SCOPUS:84939162928
VL - 10
JO - PLoS One
JF - PLoS One
SN - 1932-6203
IS - 6
M1 - e0130372
ER -