Predicting Utility Ratings for Joint Health States from Single Health States

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1 Predicting Utility Ratings for Joint Health States from Single Health States in Prostate Cancer: Empirical Testing of Three Alternative Theories William Dale, MD, PhD Anirban Basu, PhD Arthur Elstein, PhD David Meltzer, MD, PhD University of Chicago Department of Medicine

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2 The Issues Definition of Joint Health State: A health state composed of two or more distinguishable single health states. Importance Co-morbidities are common Multiple (adverse) outcomes from treatments common Empirical data lacking Current modeling for cost-effectiveness analysis not based on empirical findings Challenge Combinatorial math ? Large numbers of elicitations Complexity of analysis Increased respondent burden Only need include reasonably prevalent joint health states If not, joint health states can be safely ignored

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3 Measurement vs. Prediction of Joint Health States Can joint health state utilities be predicted based on the utilities of single health states, thereby overcoming the need for direct elicitation? OR Which model, if any, best fits the data?

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4 Commonly Proposed Models for Predicting Joint Health State Utilities Additive (or constant decrement) UJS = (Uss1) + (Uss2) - 1 Multiplicative UJS = (Uss1) * (Uss2) Minimum UJS = Min (Uss1, Uss2)

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5 Standards for Evaluating Models Bias Overall: Mean Residuals Mean Correlation Efficiency Root Mean Square Error (RMSE) Mean Absolute Error (MAE) Measured Utility Predicted Utility 0 1.0 1.0

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6 Choosing and Eliciting Utilities for Health States for Prostate Cancer Elicitation Computer-based (ProSPEQT) Time-tradeoff technique Reflect most prevalent health states, SEER database Single States Urinary Incontinence Impotence Watchful Waiting (High Anxiety) Post-prostatectomy (Low Anxiety) Joint States Urinary Incontinence Post-prostatectomy Watchful Waiting & Impotence

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7 Sample Urology Clinics University of Chicago (45% positive biopsy) Northwestern (25% positive biopsy) Survey Setting 30 minutes between appointments Embedded in larger survey At time of biopsy, referral for cause 75% - elevated PSA (>4.0 ng/dL) 25% - symptom, abnormal DRE

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8 Patient Characteristics (n=102)

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9 Distribution of Utilities: Sample Means, Medians

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10 * p < 0.05; Standard errors obtained via 1000 bootstrap replicates.

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11 * p < 0.05; Standard errors obtained via 1000 bootstrap replicates.

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12 Observed vs. Predicted Joint State Utilities: Minimum Model

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14 Model Residuals of Predicted Joint Health State Utilities: Impotence & Incontinence

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15 Conclusions: Non-parametric Modeling of Joint Health State Utilities All three models are biased Minimum is the only unbiased model on average (residuals), but shows systematic bias across the range of joint state utilities (correlations). When reasonably prevalent, joint health state utilities should be measured, if possible Additional parametric modeling should be done Split-sample and out-of-sample testing Future studies should evaluate the impact of mis-predictions on cost-effectiveness results

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16 Acknowledgements Collaborators on University of Chicago Prostate Cancer Cost-effectiveness Project U of C, General Internal Medicine: Harry Zhang, PhD U of C, Urology: Gregory Zagaja, MD Northwestern, Urology: Misop Han, MD Toronto: Ahmed Bayoumi, MD (ProSPEQT) Data Management Josh Hemmerich, PhD S. Pinar Bilir, BA Jessica Wilson, BA Financial Support: Chicago Center for Health Promotion Economics, Pilot Grant Paul Beeson Career Development Award, K23 AG24812-01

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	wdale@medicine.bsd.uchicago.edu 5 Years ago.



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