OBJECTIVE: To define objectively and describe a set of clinically relevant health states that encompass the typical effects of depression on quality of life in an actual patient population. Our model was designed to facilitate the elicitation of patients' and the public's values (utilities) for outcomes of depression. DATA SOURCES: From the depression panel of the Medical Outcomes Study. Data include scores on the 12-Item Short Form Health Survey (SF-12) as well as independently obtained diagnoses of depression for 716 patients. Follow-up information, one year after baseline, was available for 166 of these patients. METHODOLOGY: We use k-means cluster analysis to group the patients according to appropriate dimensions of health derived from the SF-12 scores. Chi-squared and exact permutation tests are used to validate the health states thus obtained, by checking for baseline and longitudinal correlation of cluster membership and clinical diagnosis. PRINCIPAL FINDINGS: We find, on the basis of a combination of statistical and clinical criteria, that six states are optimal for summarizing the range of health experienced by depressed patients. Each state is described in terms of a subject who is typical in a sense that is articulated with our cluster-analytic approach. In all of our models, the relationship between health state membership and clinical diagnosis is highly statistically significant. The models are also sensitive to changes in patients' clinical status over time. CONCLUSIONS: Cluster analysis is demonstrably a powerful methodology for forming clinically valid health states from health status data. The states produced are suitable for the experimental elicitation of preference and analyses of costs and utilities.
OBJECTIVE: To define objectively and describe a set of clinically relevant health states that encompass the typical effects of depression on quality of life in an actual patient population. Our model was designed to facilitate the elicitation of patients' and the public's values (utilities) for outcomes of depression. DATA SOURCES: From the depression panel of the Medical Outcomes Study. Data include scores on the 12-Item Short Form Health Survey (SF-12) as well as independently obtained diagnoses of depression for 716 patients. Follow-up information, one year after baseline, was available for 166 of these patients. METHODOLOGY: We use k-means cluster analysis to group the patients according to appropriate dimensions of health derived from the SF-12 scores. Chi-squared and exact permutation tests are used to validate the health states thus obtained, by checking for baseline and longitudinal correlation of cluster membership and clinical diagnosis. PRINCIPAL FINDINGS: We find, on the basis of a combination of statistical and clinical criteria, that six states are optimal for summarizing the range of health experienced by depressedpatients. Each state is described in terms of a subject who is typical in a sense that is articulated with our cluster-analytic approach. In all of our models, the relationship between health state membership and clinical diagnosis is highly statistically significant. The models are also sensitive to changes in patients' clinical status over time. CONCLUSIONS: Cluster analysis is demonstrably a powerful methodology for forming clinically valid health states from health status data. The states produced are suitable for the experimental elicitation of preference and analyses of costs and utilities.
Authors: Jeffrey M Pyne; John C Fortney; Shanti Tripathi; David Feeny; Peter Ubel; John Brazier Journal: Health Serv Res Date: 2009-04-21 Impact factor: 3.402