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Mapping the Memorial Anxiety Scale for Prostate Cancer to the SF-6D.

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Title: Mapping the Memorial Anxiety Scale for Prostate Cancer to the SF-6D.
Authors: Erim, Daniel O., Bennett, Antonia V., Gaynes, Bradley N., Basak, Ram Sankar, Usinger, Deborah, Chen, Ronald C.
Source: Quality of Life Research. Oct2021, Vol. 30 Issue 10, p2919-2928. 10p. 4 Charts, 1 Graph.
Subjects: Prostate cancer patients, Anxiety, Metadata mapping, Medical economics, Cancer patients
Abstract: Purpose: To create a crosswalk that predicts Short Form 6D (SF-6D) utilities from Memorial Anxiety Scale for Prostate Cancer (MAX-PC) scores. Methods: The data come from prostate cancer patients enrolled in the North Carolina Prostate Cancer Comparative Effectiveness & Survivorship Study (NC ProCESS, N = 1016). Cross-sectional data from 12- to 24-month follow-up were used as estimation and validation datasets, respectively. Participants' SF-12 scores were used to generate SF-6D utilities in both datasets. Beta regression mixture models were used to evaluate SF-6D utilities as a function of MAX-PC scores, race, education, marital status, income, employment status, having health insurance, year of cancer diagnosis and clinically significant prostate cancer-related anxiety (PCRA) status in the estimation dataset. Models' predictive accuracies (using mean absolute error [MAE], root mean squared error [RMSE], Akaike information criterion [AIC] and Bayesian information criterion [BIC]) were examined in both datasets. The model with the highest prediction accuracy and the lowest prediction errors was selected as the crosswalk. Results: The crosswalk had modest prediction accuracy (MAE = 0.092, RMSE = 0.114, AIC = − 2708 and BIC = − 2595.6), which are comparable to prediction accuracies of other SF-6D crosswalks in the literature. About 24% and 52% of predictions fell within ± 5% and ± 10% of observed SF-6D, respectively. The observed mean disutility associated with acquiring clinically significant PCRA is 0.168 (standard deviation = 0.179). Conclusion: This study provides a crosswalk that converts MAX-PC scores to SF-6D utilities for economic evaluation of clinically significant PCRA treatment options for prostate cancer survivors. [ABSTRACT FROM AUTHOR]
Copyright of Quality of Life Research is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Mapping the Memorial Anxiety Scale for Prostate Cancer to the SF-6D.
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  Data: <searchLink fieldCode="AR" term="%22Erim%2C+Daniel+O%2E%22">Erim, Daniel O.</searchLink><br /><searchLink fieldCode="AR" term="%22Bennett%2C+Antonia+V%2E%22">Bennett, Antonia V.</searchLink><br /><searchLink fieldCode="AR" term="%22Gaynes%2C+Bradley+N%2E%22">Gaynes, Bradley N.</searchLink><br /><searchLink fieldCode="AR" term="%22Basak%2C+Ram+Sankar%22">Basak, Ram Sankar</searchLink><br /><searchLink fieldCode="AR" term="%22Usinger%2C+Deborah%22">Usinger, Deborah</searchLink><br /><searchLink fieldCode="AR" term="%22Chen%2C+Ronald+C%2E%22">Chen, Ronald C.</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22Quality+of+Life+Research%22">Quality of Life Research</searchLink>. Oct2021, Vol. 30 Issue 10, p2919-2928. 10p. 4 Charts, 1 Graph.
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  Data: <searchLink fieldCode="DE" term="%22Prostate+cancer+patients%22">Prostate cancer patients</searchLink><br /><searchLink fieldCode="DE" term="%22Anxiety%22">Anxiety</searchLink><br /><searchLink fieldCode="DE" term="%22Metadata+mapping%22">Metadata mapping</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+economics%22">Medical economics</searchLink><br /><searchLink fieldCode="DE" term="%22Cancer+patients%22">Cancer patients</searchLink>
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  Data: Purpose: To create a crosswalk that predicts Short Form 6D (SF-6D) utilities from Memorial Anxiety Scale for Prostate Cancer (MAX-PC) scores. Methods: The data come from prostate cancer patients enrolled in the North Carolina Prostate Cancer Comparative Effectiveness & Survivorship Study (NC ProCESS, N = 1016). Cross-sectional data from 12- to 24-month follow-up were used as estimation and validation datasets, respectively. Participants' SF-12 scores were used to generate SF-6D utilities in both datasets. Beta regression mixture models were used to evaluate SF-6D utilities as a function of MAX-PC scores, race, education, marital status, income, employment status, having health insurance, year of cancer diagnosis and clinically significant prostate cancer-related anxiety (PCRA) status in the estimation dataset. Models' predictive accuracies (using mean absolute error [MAE], root mean squared error [RMSE], Akaike information criterion [AIC] and Bayesian information criterion [BIC]) were examined in both datasets. The model with the highest prediction accuracy and the lowest prediction errors was selected as the crosswalk. Results: The crosswalk had modest prediction accuracy (MAE = 0.092, RMSE = 0.114, AIC = − 2708 and BIC = − 2595.6), which are comparable to prediction accuracies of other SF-6D crosswalks in the literature. About 24% and 52% of predictions fell within ± 5% and ± 10% of observed SF-6D, respectively. The observed mean disutility associated with acquiring clinically significant PCRA is 0.168 (standard deviation = 0.179). Conclusion: This study provides a crosswalk that converts MAX-PC scores to SF-6D utilities for economic evaluation of clinically significant PCRA treatment options for prostate cancer survivors. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Quality of Life Research is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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