Literature DB >> 10372579

The danger of applying group-level utilities in decision analyses of the treatment of localized prostate cancer in individual patients.

M E Cowen1, B J Miles, D F Cahill, R B Giesler, J R Beck, M W Kattan.   

Abstract

The optimal management strategy for men who have localized prostate cancer remains controversial. This study examines the extent to which suggested treatment based on the perspective of a group or society agrees with that derived from individual patients' preferences. A previously published decision analysis for localized prostate cancer was used to suggest the treatment that maximized quality-adjusted life expectancy. Two treatment recommendations were obtained for each patient: the first (group-level) was derived using the mean utilities of the cohort; the second (individual-level) used his own set of utilities. Group-level utilities misrepresented 25-48% of individuals' preferences depending on the grade of tumor modeled. The best kappa measure achieved between group and individual preferences was 0.11. The average quality-adjusted life years lost due to misrepresentation of preference was as high as 1.7 quality-adjusted life years. Use of aggregated utilities in a group-level decision analysis can ignore the substantial variability at the individual level. Caution is needed when applying a group-level recommendation to the treatment of localized prostate cancer in an individual patient.

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Year:  1998        PMID: 10372579     DOI: 10.1177/0272989X9801800404

Source DB:  PubMed          Journal:  Med Decis Making        ISSN: 0272-989X            Impact factor:   2.583


  14 in total

Review 1.  Measuring patients' preferences for treatment and perceptions of risk.

Authors:  A Bowling; S Ebrahim
Journal:  Qual Health Care       Date:  2001-09

2.  The ethics of aggregation and hormone replacement therapy.

Authors:  A D Lyerly; E R Myers; R R Faden
Journal:  Health Care Anal       Date:  2001

3.  A new graphic for quality adjusted life years (Q-TWiST) survival analysis: the Q-TWiST plot.

Authors:  Jeff A Sloan; Daniel J Sargent; Jed Lindman; Cristine Allmer; Delfino Vargas-Chanes; Edward T Creagan; James A Bonner; Michael J O'Connell; Robert J Dalton; Kendrith M Rowland; Burke J Brooks; John A Laurie
Journal:  Qual Life Res       Date:  2002-02       Impact factor: 4.147

4.  Concordance of couples' prostate cancer screening recommendations from a decision analysis.

Authors:  Scott B Cantor; Robert J Volk; Murray D Krahn; Alvah R Cass; Jawaria Gilani; Susan C Weller; Stephen J Spann
Journal:  Patient       Date:  2008-01-01       Impact factor: 3.883

5.  Time trade-off utility modified to accommodate degenerative and life-threatening conditions.

Authors:  M W Kattan; P A Fearn; B J Miles
Journal:  Proc AMIA Symp       Date:  2001

6.  Telephone interviews vs. workstation sessions for acquiring quality of life data.

Authors:  M W Kattan; P A Fearn; S B Cantor; J Hu; M E Cowen; R B Giesler; B J Miles
Journal:  Proc AMIA Symp       Date:  1999

7.  Postoperative nomogram predicting the 10-year probability of prostate cancer recurrence after radical prostatectomy.

Authors:  Andrew J Stephenson; Peter T Scardino; James A Eastham; Fernando J Bianco; Zohar A Dotan; Christopher J DiBlasio; Alwyn Reuther; Eric A Klein; Michael W Kattan
Journal:  J Clin Oncol       Date:  2005-10-01       Impact factor: 44.544

8.  Patient preference and decision-making for initiating metastatic colorectal cancer medical treatment.

Authors:  Alex Z Fu; Kristi D Graves; Roxanne E Jensen; John L Marshall; Margaret Formoso; Arnold L Potosky
Journal:  J Cancer Res Clin Oncol       Date:  2015-11-18       Impact factor: 4.553

9.  Biases in Individualized Cost-effectiveness Analysis: Influence of Choices in Modeling Short-Term, Trial-Based, Mortality Risk Reduction and Post-Trial Life Expectancy.

Authors:  David van Klaveren; John B Wong; David M Kent; Ewout W Steyerberg
Journal:  Med Decis Making       Date:  2017-03-20       Impact factor: 2.583

10.  Agreement between prostate cancer patients and their clinicians about utilities and attribute importance.

Authors:  Arthur S Elstein; Gretchen B Chapman; Joan S Chmiel; Sara J Knight; Cheeling Chan; Robert B Nadler; Timothy M Kuzel; Amy K Siston; Charles L Bennett
Journal:  Health Expect       Date:  2004-06       Impact factor: 3.377

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