Apparatus and method for prediction and management of subject compliance in clinical research
DC CAFCFirst Claim
1. A method of predicting subject noncompliance, comprising the steps of:
- providing historical subject compliance data;
generating at least one predictive algorithm for predicting subject noncompliance by quantitative analysis of the historical subject compliance; and
translating the at least one predictive algorithm into at least one prediction rule for use with a clinical trial.
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Abstract
A system for developing and implementing empirically derived algorithms to generate decision rules to predict subject noncompliance and fraud with research protocols in clinical trials allows for the identification of complex patterns of variables that detect or predict subject noncompliance and fraud with research protocol in the clinical trial. The present invention can also be used to monitor subject compliance with the research protocol to determine preferred actions to be performed. Optionally, the invention may provide a spectrum of noncompliance, from minor noncompliance needing only corrective feedback, to significant noncompliance requiring subject removal from the clinical trial. The algorithms and decision rules can also be domain-specific, such as detecting non-compliance or fraud among subjects in a cardiovascular drug trial, or demographically specific, such as taking into account gender or age which provides for algorithms and decision rules to be optimized for the specific sample of subjects being studied.
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Citations
37 Claims
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1. A method of predicting subject noncompliance, comprising the steps of:
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providing historical subject compliance data;
generating at least one predictive algorithm for predicting subject noncompliance by quantitative analysis of the historical subject compliance; and
translating the at least one predictive algorithm into at least one prediction rule for use with a clinical trial. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A method of determining subject noncompliance, comprising the steps of:
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providing at least one of the group of historical subject compliance data and historical protocol data;
generating at least one algorithm reflective of at least one of the group of the historical subject compliance data and the historical protocol data by quantitative analysis of the historical subject compliance data and the historical protocol data;
translating the at least one algorithm into at least one decision rule for analyzing subject compliance information;
obtaining the subject compliance information; and
comparing the subject compliance information to the at least one decision rule to determine if corrective action is needed. - View Dependent Claims (10, 11, 12, 13)
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14. A method of determining subject noncompliance, comprising the steps of:
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providing historical subject compliance data and historical protocol data;
generating a spectrum of noncompliance representative of the historical subject compliance data not compliant with the historical protocol data by quantitative analysis of the historical subject compliance data and the historical protocol data;
obtaining subject compliance information; and
comparing the spectrum of noncompliance to the subject compliance information to determine if corrective action is needed. - View Dependent Claims (15, 16)
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17. A method of detecting subject fraud, comprising the steps of:
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providing historical subject compliance data and historical protocol data;
generating at least one fraud detection algorithm for detecting subject fraud by quantitative analysis of the historical subject compliance data and the historical protocol data; and
translating the at least one fraud detection algorithm into at least one fraud detection rule for use with a clinical trial.
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18. A method of detecting subject fraud, comprising the steps of:
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providing subject compliance data;
generating at least one fraud detection algorithm for detecting subject fraud by quantitative analysis of the compliance data; and
translating the at least one fraud detection algorithm into at least one fraud detection rule for use with a clinical trial. - View Dependent Claims (19, 20, 21, 22, 23, 24)
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25. A medium suitable for use in an electronic device and having instructions for execution on the electronic device, the instructions comprising the steps of:
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providing at least one of the group of historical subject compliance data and historical protocol data;
generating at least one predictive algorithm for predicting subject noncompliance by quantitative analysis of at least one of the group of the historical subject compliance data and the historical protocol data; and
translating the at least one predictive algorithm into at least one prediction rule for use with a clinical trial. - View Dependent Claims (26, 27, 28)
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29. A medium suitable for use in an electronic device and having instructions for execution on the electronic device, the instructions comprising the steps of:
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providing at least one of the group of historical subject compliance data and historical protocol data;
generating at least one algorithm reflective of at least one of the group of the historical subject compliance data and the historical protocol data by quantitative analysis of the historical subject compliance data and the historical protocol data;
translating the at least one algorithm into at least one decision rule for analyzing subject compliance information;
obtaining the subject compliance information; and
comparing the subject compliance information to the at least one decision rule to determine if corrective action is needed. - View Dependent Claims (30, 31, 32)
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33. A medium suitable for use in an electronic device and having instructions for execution on the electronic device, the instructions comprising the steps of:
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providing historical subject compliance data and historical protocol data;
generating a spectrum of noncompliance representative of the historical subject compliance data not compliant with the historical protocol data by quantitative analysis of the historical subject compliance data and the historical protocol data;
obtaining subject compliance information; and
comparing the spectrum of noncompliance to the subject compliance information to determine if corrective action is needed. - View Dependent Claims (34, 35)
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36. A medium suitable for use in an electronic device and having instructions for execution on the electronic device, the instructions comprising the steps of:
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providing historical subject compliance data and historical protocol data;
generating at least one fraud detection algorithm for detecting subject fraud by quantitative analysis of the historical subject compliance data and the historical protocol data; and
translating the at least one fraud detection algorithm into at least one fraud detection rule for use with a clinical trial.
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37. A medium suitable for use in an electronic device and having instructions for execution on the electronic device, the instructions comprising the steps of:
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providing subject compliance data;
generating at least one fraud detection algorithm for detecting subject fraud by quantitative analysis of the compliance data; and
translating the at least one fraud detection algorithm into at least one fraud detection rule for use with a clinical trial.
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Specification