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Clinical Research

Predicting utility scores for prostate cancer: mapping the Prostate Cancer Index to the Patient-Oriented Prostate Utility Scale (PORPUS)

Prostate Cancer and Prostatic Diseases volume 17, pages 47–56 (2014)Cite this article

Subjects

An Erratum to this article was published on 12 February 2014

Abstract

Background:

The Prostate Cancer Index (PCI) is a health profile instrument that measures health-related quality of life with six subscales: urinary, sexual, and bowel function and bother. The Patient-Oriented Prostate Utility Scale (PORPUS-U) measures utility (0=dead and 1=full health). Utility is a preference-based approach to measure health-related quality of life, required for decision analyses and cost-effectiveness analyses. We developed a function to estimate PORPUS-U utilities from PCI scores.

Methods:

The development data set included 676 community-dwelling prostate cancer (PC) survivors who completed the PCI and PORPUS-U by mail. We fit three linear regression models: one used original PORPUS-U scores and two used log-transformed PORPUS-U scores, one with a hierarchy constraint and one without. The model selection was performed using stepwise selection and fivefold cross validation. The validation data included 248 PC outpatients with three assessments on the PCI and PORPUS-U. Scores were retransformed for validation, with Duan’s smearing estimator applied to correct potential bias. The predictive ability of the models was assessed with R2, root mean square error (RMSE) and by comparing predicted and observed utilities.

Results:

The best-fitting model used the log-transformed PORPUS-U with no hierarchy constraint. The R2 was 0.72. The RMSE ranged from 0.040 to 0.061 for the three validation data sets. Differences between predicted and observed utilities ranged from 0.000 to 0.006 but predicted utilities overestimated the lowest 5% of observed PORPUS-U scores and underestimated the highest observed scores.

Conclusions:

Our algorithm can calculate PORPUS-U utility scores from PCI scores, thus supplementing descriptive quality of life measures with utility scores in PC patients. Utilities derived from mapping algorithms are useful for assigning utility to groups of patients but are less accurate at predicting utility of individual patients. We are exploring statistical methods to improve the mapping of utilities from descriptive instruments.

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Acknowledgements

This work was supported by the Canadian Institutes of Health Research (Grants number 006169 and 53114). Dr Krahn is supported by the F Norman Hughes Chair in Pharmacoeconomics, Faculty of Pharmacy, University of Toronto.

Author information

Authors and Affiliations

  1. Toronto General Hospital, Clinical Decision Making and Health Care, University Health Network, Toronto, Ontario, Canada

    K E Bremner & M D Krahn

  2. Toronto Health Economics and Technology Assessment Collaborative (THETA), Toronto, Ontario, Canada

    K E Bremner, N Mitsakakis & M D Krahn

  3. Faculty of Pharmacy, University of California San Francisco, San Francisco, CA, USA

    L Wilson

  4. Department of Medicine, Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada

    M D Krahn

  5. Department of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Ontario, Canada

    M D Krahn

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  1. K E Bremner
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  2. N Mitsakakis
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Correspondence to K E Bremner.

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The authors declare no conflict of interest.

Additional information

Preliminary results of this paper were presented at the 33rd Annual Meeting of the Society for Medical Decision Making, Chicago, IL, USA on 22–26 October 2011. The abstract is published online only (33rd Annual Meeting of the Society of Medical Decision Making: 2011 Abstracts. doi: 10.1177/0272989X12439390. Med Decis Making 2012 32: E10. http://www.mdm.sagepub.com/content/32/2/E10). A summary of this study was presented as a poster at the 68th Annual Meeting of the Canadian Urological Association, in Niagara Falls, Ontario, Canada on 22–25 June 2013.

Appendices

APPENDIX 1

UCLA-Prostate Cancer Index (PCI)

Table 6 Table a1
Full size table

APPENDIX 2

APPENDIX 2 Patient Oriented Prostate Cancer Utility Scale (PORPUS)

Table 7 Table a2
Full size table

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Bremner, K., Mitsakakis, N., Wilson, L. et al. Predicting utility scores for prostate cancer: mapping the Prostate Cancer Index to the Patient-Oriented Prostate Utility Scale (PORPUS). Prostate Cancer Prostatic Dis 17, 47–56 (2014). https://doi.org/10.1038/pcan.2013.44

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