Abstract
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.
Highlights
The method of Ref. [15] lost the correlation between the classifications by the mathematical model and those by the medical doctors when we restricted the datasets to the second half of patients, the proposed method kept the correlation by using the prior distribution (see Table 2, which achieved the smaller p-value less than 0.001 due to the fact that most patients without relapse were classified into Type (i), while most patients with metastasis and androgen independence were classified into Type (ii))
There are some correlations between pairs of the combined parameters obtained by fitting time courses of the tumor marker for the mathematical model
By enforcing the prior distribution of the combined parameters constructed by the whole datasets of the past other patients, we can eventually shrink the space of the combined parameters to the space where the combined parameters for the past other patients are distributed
Summary
Mathematical models for diseases [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20] are valuable for helping medical doctors optimize therapy based on characteristics of the individual patient’s tumor behavior. When we apply a mathematical model to a series of data points obtained over time for an individual patient, the PLOS ONE | DOI:10.1371/journal.pone.0130372. Personalized Estimation of Parameters for IAS Therapy When we apply a mathematical model to a series of data points obtained over time for an individual patient, the PLOS ONE | DOI:10.1371/journal.pone.0130372 June 24, 2015
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