Abstract
Physicians use clinical guidelines to inform judgment about therapy. Clinical guidelines do not address three important uncertainties: (1) uncertain relevance of tested populations to the individual patient, (2) the patient's uncertain preferences among possible outcomes, and (3) uncertain subjective and financial costs of intervention. Unreliable probabilistic information is available for some of these uncertainties; no probabilities are available for others. The uncertainties are in the values of parameters and in the shapes of functions. We explore the usefulness of info-gap decision theory in patient-physician decision making in managing cholesterol level using clinical guidelines. Info-gap models of uncertainty provide versatile tools for quantifying diverse uncertainties. Info-gap theory provides two decision functions for evaluating alternative therapies. The robustness function assesses the confidence-in light of uncertainties-in attaining acceptable outcomes. The opportuneness function assesses the potential for better-than-anticipated outcomes. Both functions assist in forming preferences among alternatives. Hypothetical case studies demonstrate that decisions using the guidelines and based on best estimates of the expected utility are sometimes, but not always, consistent with robustness and opportuneness analyses. The info-gap analysis provides guidance when judgment suggests that a deviation from the guidelines would be productive. Finally, analysis of uncertainty can help resolve ambiguous situations.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1046-1065 |
| Number of pages | 20 |
| Journal | International Journal of Approximate Reasoning |
| Volume | 50 |
| Issue number | 7 |
| DOIs | |
| State | Published - Jul 2009 |
| Externally published | Yes |
Fingerprint
Keywords
- Cholesterol management
- Clinical guidelines
- Info-gap decision theory
- Judgment under uncertainty
- Patient satisfaction
ASJC Scopus subject areas
- Artificial Intelligence
- Software
- Theoretical Computer Science
- Applied Mathematics
Cite this
Heterogeneous uncertainties in cholesterol management. / Ben-Haim, Yakov; Dacso, Clifford C.; Carrasco, Jonathon; Rajan, Nithin.
In: International Journal of Approximate Reasoning, Vol. 50, No. 7, 07.2009, p. 1046-1065.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Heterogeneous uncertainties in cholesterol management
AU - Ben-Haim, Yakov
AU - Dacso, Clifford C.
AU - Carrasco, Jonathon
AU - Rajan, Nithin
PY - 2009/7
Y1 - 2009/7
N2 - Physicians use clinical guidelines to inform judgment about therapy. Clinical guidelines do not address three important uncertainties: (1) uncertain relevance of tested populations to the individual patient, (2) the patient's uncertain preferences among possible outcomes, and (3) uncertain subjective and financial costs of intervention. Unreliable probabilistic information is available for some of these uncertainties; no probabilities are available for others. The uncertainties are in the values of parameters and in the shapes of functions. We explore the usefulness of info-gap decision theory in patient-physician decision making in managing cholesterol level using clinical guidelines. Info-gap models of uncertainty provide versatile tools for quantifying diverse uncertainties. Info-gap theory provides two decision functions for evaluating alternative therapies. The robustness function assesses the confidence-in light of uncertainties-in attaining acceptable outcomes. The opportuneness function assesses the potential for better-than-anticipated outcomes. Both functions assist in forming preferences among alternatives. Hypothetical case studies demonstrate that decisions using the guidelines and based on best estimates of the expected utility are sometimes, but not always, consistent with robustness and opportuneness analyses. The info-gap analysis provides guidance when judgment suggests that a deviation from the guidelines would be productive. Finally, analysis of uncertainty can help resolve ambiguous situations.
AB - Physicians use clinical guidelines to inform judgment about therapy. Clinical guidelines do not address three important uncertainties: (1) uncertain relevance of tested populations to the individual patient, (2) the patient's uncertain preferences among possible outcomes, and (3) uncertain subjective and financial costs of intervention. Unreliable probabilistic information is available for some of these uncertainties; no probabilities are available for others. The uncertainties are in the values of parameters and in the shapes of functions. We explore the usefulness of info-gap decision theory in patient-physician decision making in managing cholesterol level using clinical guidelines. Info-gap models of uncertainty provide versatile tools for quantifying diverse uncertainties. Info-gap theory provides two decision functions for evaluating alternative therapies. The robustness function assesses the confidence-in light of uncertainties-in attaining acceptable outcomes. The opportuneness function assesses the potential for better-than-anticipated outcomes. Both functions assist in forming preferences among alternatives. Hypothetical case studies demonstrate that decisions using the guidelines and based on best estimates of the expected utility are sometimes, but not always, consistent with robustness and opportuneness analyses. The info-gap analysis provides guidance when judgment suggests that a deviation from the guidelines would be productive. Finally, analysis of uncertainty can help resolve ambiguous situations.
KW - Cholesterol management
KW - Clinical guidelines
KW - Info-gap decision theory
KW - Judgment under uncertainty
KW - Patient satisfaction
UR - http://www.scopus.com/inward/record.url?scp=67549127093&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=67549127093&partnerID=8YFLogxK
U2 - 10.1016/j.ijar.2009.04.002
DO - 10.1016/j.ijar.2009.04.002
M3 - Article
AN - SCOPUS:67549127093
VL - 50
SP - 1046
EP - 1065
JO - International Journal of Approximate Reasoning
JF - International Journal of Approximate Reasoning
SN - 0888-613X
IS - 7
ER -