Method and apparatus for predicting therapeutic outcomes
First Claim
1. A method for evaluating the utility of a treatment regimen for treating a disease for the application of such treatment to a patient having such disease, the method comprising the steps of:
- a) determining at least one diagnostic variable relating to a statistical model describing the utility of said treatment regimen, said statistical model being derived by the steps ofi) developing a discriminant function which is effective for classifying the response of individuals afflicted with said disease to said treatment regimen, said discriminant function being based at least in part on a data set including clinical profiles of individual patients who have been treated for said disease using said treatment regimen, said clinical profiles of individual patients including said diagnostic variable; and
ii) performing a logistic regression using said discriminant function to assign thereby a probability of treatment outcome for said individuals; and
b) applying said diagnostic variable to said statistical model to obtain an estimate of the utility of said treatment regimen for the treatment of said disease in said patient.
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Accused Products
Abstract
Methods, software, and systems for evaluating the response of a patient afflicted with a disease to a therapeutic regimen for the disease are described. In one aspect, the present methods, systems, and software are provided for evaluating the utility of a treatment regimen for treating a patient afflicted with a disease. In one embodiment of this aspect, the value of at least one diagnostic variable relating to a statistical model describing the utility of the treatment regimen is determined. The statistical model is derived using a robustified similarity metric least squares (SMILES) analysis of the response to the treatment regimen which has been adapted to include discriminant and logistical analysis. The value of the diagnostic variable is then applied to the model to provide an estimated utility of the treatment regimen in treating the patient. Using the methods, software, and apparatus described herein, robust, statistically significant models of patient responsiveness that reduce the problems associated with present treatment response prediction methods that are brittle and oversimplify the complex interactions among treatment variables can assist patients and clinicians in determining therapies.
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Citations
68 Claims
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1. A method for evaluating the utility of a treatment regimen for treating a disease for the application of such treatment to a patient having such disease, the method comprising the steps of:
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a) determining at least one diagnostic variable relating to a statistical model describing the utility of said treatment regimen, said statistical model being derived by the steps of i) developing a discriminant function which is effective for classifying the response of individuals afflicted with said disease to said treatment regimen, said discriminant function being based at least in part on a data set including clinical profiles of individual patients who have been treated for said disease using said treatment regimen, said clinical profiles of individual patients including said diagnostic variable; and ii) performing a logistic regression using said discriminant function to assign thereby a probability of treatment outcome for said individuals; and b) applying said diagnostic variable to said statistical model to obtain an estimate of the utility of said treatment regimen for the treatment of said disease in said patient. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A method for producing a statistical model of a likely response to a treatment regimen for treating a disease in a mammal, the method comprising the steps of:
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a) obtaining at least one sample population of individuals representative of said disease, said sample population being treated for said disease using said treatment regimen for treating said disease; b) determining from said sample population a set of data for at least one variable relating to said population, said disease, or said regimen, said variable having a putative correlation with said regimen for treating said disease; c) deriving from said set of data said statistical model of said likely response to said regimen for treating said disease, wherein said step of deriving includes the sub-steps of; i) standardizing said data; ii) developing a discriminant function which is effective for classifying the response of individuals afflicted with said disease to said treatment regimen, said discriminant function being based at least in part on clinical profiles of individual patients who have been treated for said disease using said treatment regimen, said clinical profiles of individual patients including said diagnostic variable; and iii) performing a logistic regression using said discriminant function to assign thereby a probability of treatment outcome for said individuals. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68)
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26. A computer system for producing a statistical model of a regimen for treating a disease in a mammal using a set of data derived from at least one sample population of individuals representative of said disease, said sample population being treated for said disease using a treatment regimen for treating said disease, the system comprising:
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a) a pre-processing mechanism for standardizing said set of data to produce a set of normalized data; and b) a processing mechanism for processing said standardized data, said processing mechanism configured to i) develop a discriminant function which is effective for classifying the response of individuals afflicted with said disease to said treatment regimen, said discriminant function being based at least in part on clinical profiles of individual patients who have been treated for said disease using said treatment regimen, said clinical profiles of individual patients including said diagnostic variable; and ii) perform a logistic regression using said discriminant function to assign thereby a probability of treatment outcome for said individuals. - View Dependent Claims (27, 28, 29, 30, 31, 32, 33, 34)
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45. A computer program product including a computer-readable medium having computer-readable program code devices embodied therein for producing a statistical model of a regimen for treating a disease in a mammal using data obtained from at least one sample population of individuals representative of said disease, said sample population being treated for said disease using said treatment regimen, said program code devices being configured to cause a computer to perform the steps of:
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a) standardizing said data to produce standardized data; b) processing said standardized data to develop a discriminant function which is effective for classifying the response of said individuals to said treatment regimen, said discriminant function being based at least in part on clinical profiles of individual patients who have been treated for said disease using said treatment regimen, said clinical profiles of individual patients including said diagnostic variable; and c) performing a logistic regression using said discriminant function to assign thereby a probability of treatment outcome for said individuals. - View Dependent Claims (46, 47, 48, 49, 50, 51, 52, 53)
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Specification