Improving the mapping of condition-specific health-related quality of life onto SF-6D score
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- 作者：Yingsi Yang ; M. Y. Wong ; Cindy L. K. Lam ; Carlos K. H. Wong
- 关键词：Quality of life ; Colorectal cancer ; FACT ; C ; SF ; 6D ; Mapping models ; Cubic spline
- 刊名：Quality of Life Research
- 出版年：2014
- 出版时间：October 2014
- 年：2014
- 卷：23
- 期：8
- 页码：2343-2353
- 全文大小：374 KB
- 参考文献：1. National Institute for Health and Care Excellence (NICE). (2013). / Guide to the methods of technology appraisal. cations.nice.org.uk/pmg9" class="a-plus-plus">http://publications.nice.org.uk/pmg9
2. Brazier, J. E., Yang, Y., Tsuchiya, A., & Rowen, D. L. (2010). A review of studies mapping (or cross walking) non-preference based measures of health to generic preference-based measures. / The European Journal of Health Economics, / 11(2), 215-25. class="external" href="http://dx.doi.org/10.1007/s10198-009-0168-z" target="_blank" title="It opens in new window">CrossRef
3. Mortimer, D., & Segal, L. (2008). Comparing the incomparable? A systematic review of competing techniques for converting descriptive measures of health status into QALY-weights. / Medical Decision Making, / 28(1), 66-9. class="external" href="http://dx.doi.org/10.1177/0272989X07309642" target="_blank" title="It opens in new window">CrossRef
4. Wong, C. K., Lam, C. L., Law, W. L., Poon, J. T., Kwong, D. L., Tsang, J., et al. (2013). Condition-specific measure was more responsive than generic measure in colorectal cancer: All but social domains. / Journal of Clinical Epidemiology, / 5, 557-65. class="external" href="http://dx.doi.org/10.1016/j.jclinepi.2012.11.010" target="_blank" title="It opens in new window">CrossRef
5. Wong, C. K., Lam, C. L., Rowen, D., McGhee, S. M., Ma, K. P., Law, W. L., et al. (2012). Mapping the functional assessment of cancer therapy-general or -colorectal to SF-6D in Chinese patients with colorectal neoplasm. / Value in Health, / 15(3), 495-03.
6. Edge, S. B., & Compton, C. C. (2010). The American joint committee on cancer: The 7th edition of the AJCC cancer staging manual and the future of TNM. / Annals of Surgical Oncology, / 17(6), 1471-474. class="external" href="http://dx.doi.org/10.1245/s10434-010-0985-4" target="_blank" title="It opens in new window">CrossRef
7. Wong, C. K., Lam, C. L., Poon, J. T., McGhee, S. M., Law, W. L., Kwong, D. L., et al. (2012). Direct medical costs of care for Chinese patients with colorectal neoplasia: A health care service provider perspective. / Journal of Evaluation in Clinical Practice, / 18(6), 1203-210. class="external" href="http://dx.doi.org/10.1111/j.1365-2753.2011.01776.x" target="_blank" title="It opens in new window">CrossRef
8. Wong, C. K., Lam, C. L., Law, W. L., Poon, J. T., Chan, P., Kwong, D. L., et al. (2012). Validity and reliability study on traditional Chinese FACT-C in Chinese patients with colorectal neoplasm. / Journal of Evaluation in Clinical Practice, / 18(6), 1186-195. class="external" href="http://dx.doi.org/10.1111/j.1365-2753.2011.01753.x" target="_blank" title="It opens in new window">CrossRef
9. Wong, C. K., Lam, C. L., Poon, J. T., & Kwong, D. L. (2013). Clinical correlates of health preference and generic health-related quality of life in patients with colorectal neoplasms. / PloS One, / 8(3), e58341. class="external" href="http://dx.doi.org/10.1371/journal.pone.0058341" target="_blank" title="It opens in new window">CrossRef
10. Wong, C. K. H., Lam, C. L., Wan, Y., & Rowen, D. (2013). Predicting SF-6D from the European Organization for Treatment and Research of Cancer Quality of Life Questionnaire Scores in Patients with Colorectal Cancer. / Value in Health, / 16(2), 373-84. class="external" href="http://dx.doi.org/10.1016/j.jval.2012.12.004" target="_blank" title="It opens in new window">CrossRef
11. Wong, C. K., Lam, C. L., Mulhern, B., Law, W. L., Poon, J. T., Kwong, D. L., et al. (2013). Measurement invariance of the functional assessment of cancer therapy colorectal quality-of-life instrument among modes of administration. / Quality of Life Research, / 22(6), 1415-426. class="external" href="http://dx.doi.org/10.1007/s11136-012-0272-x" target="_blank" title="It opens in new window">CrossRef
12. Wong, C. K., Mulhern, B., Wan, Y. F., & Lam, C. L. (2014). Responsiveness was similar between direct and mapped SF-6D in colorectal cancer patients who declined. / Journal of Clinical Epidemiology, / 67(2), 219-27. class="external" href="http://dx.doi.org/10.1016/j.jclinepi.2013.08.011" target="_blank" title="It opens in new window">CrossRef
13. Webster, K., Cella, D., & Yost, K. (2003). The functional assessment of chronic illness therapy (FACIT) measurement system: Properties, applications, and interpretation. / Health and Quality of Life Outcomes, / 1(1), 79. class="external" href="http://dx.doi.org/10.1186/1477-7525-1-79" target="_blank" title="It opens in new window">CrossRef
14. Lam, C. L., Brazier, J., & McGhee, S. M. (2008). Valuation of the SF-6D health states is feasible, acceptable, reliable, and valid in a Chinese population. / Value in Health, / 11(2), 295-03. class="external" href="http://dx.doi.org/10.1111/j.1524-4733.2007.00233.x" target="_blank" title="It opens in new window">CrossRef
15. McGhee, S. M., Brazier, J., Lam, C. L., Chin, W. L., Chau, J., Cheung, A., et al. (2011). Quality-adjusted life years: Population-specific measurement of the quality component. / Hong Kong Medical Journal, / 17(6 Suppl. 6), 17.
16. Cella, D. (1997). / FACIT manual: Manual of the functional assessment of chronic illness therapy (FACIT) measurement system. (4th ed.). Center on Outcomes, Research and Education.
17. Brand J. P. (1999) Development, implementation and evaluation of multiple imputation strategies for the statistical analysis of incomplete data sets. Rotterdam: Erasmus University
18. Van Buuren, S., Brand, J. P., Groothuis-Oudshoorn, C., & Rubin, D. B. (2006). Fully conditional specification in multivariate imputation. / Journal of Statistical Computation and Simulation, / 76(12), 1049-064. class="external" href="http://dx.doi.org/10.1080/10629360600810434" target="_blank" title="It opens in new window">CrossRef
19. Durrleman, S., & Simon, R. (1989). Flexible regression models with cubic splines. / Statistics in Medicine, / 8(5), 551-61. class="external" href="http://dx.doi.org/10.1002/sim.4780080504" target="_blank" title="It opens in new window">CrossRef
20. Poirier, D. J. (1973). Piecewise regression using cubic splines. / Journal of the American Statistical Association, / 68(343), 515-24.
21. Austin, P. C., Escobar, M., & Kopec, J. A. (2000). The use of the Tobit model for analyzing measures of health status. / Quality of Life Research, / 9(8), 901-10. class="external" href="http://dx.doi.org/10.1023/A:1008938326604" target="_blank" title="It opens in new window">CrossRef
22. Huang, I., Frangakis, C., Atkinson, M. J., Willke, R. J., Leite, W. L., Vogel, W. B., et al. (2008). Addressing ceiling effects in health status measures: A comparison of techniques applied to measures for people with HIV disease. / Health Services Research, / 43(1p1), 327-39. class="external" href="http://dx.doi.org/10.1111/j.1475-6773.2007.00745.x" target="_blank" title="It opens in new window">CrossRef
23. Akaike, H. (1974). A new look at the statistical model identification. / IEEE Transactions on Automatic Control, / 19(6), 716-23. class="external" href="http://dx.doi.org/10.1109/TAC.1974.1100705" target="_blank" title="It opens in new window">CrossRef
24. Schwarz, G. (1978). Estimating the dimension of a model. / The Annals of Statistics, / 6(2), 461-64. class="external" href="http://dx.doi.org/10.1214/aos/1176344136" target="_blank" title="It opens in new window">CrossRef
25. Buuren, S., & Groothuis-Oudshoorn, K. (2011). MICE: Multivariate imputation by chained equations in R. / Journal of Statistical Software., / 45(3), 1-7
26. Schafer, J. L. (1999). Multiple imputation: A primer. / Statistical Methods in Medical Research, / 8(1), 3-5. class="external" href="http://dx.doi.org/10.1191/096228099671525676" target="_blank" title="It opens in new window">CrossRef
27. Austin, P. C. (2002). A comparison of methods for analyzing health-related quality-of-life measures. / Value in Health, / 5(4), 329-37. class="external" href="http://dx.doi.org/10.1046/j.1524-4733.2002.54128.x" target="_blank" title="It opens in new window">CrossRef
28. Pullenayegum, E. M., Tarride, J. E., Xie, F., Goeree, R., Gerstein, H. C., & O’Reilly, D. (2010). Analysis of health utility data when some subjects attain the upper bound of 1: Are Tobit and CLAD models appropriate? / Value in Health, / 13(4), 487-94. class="external" href="http://dx.doi.org/10.1111/j.1524-4733.2010.00695.x" target="_blank" title="It opens in new window">CrossRef - 作者单位：Yingsi Yang (1)
M. Y. Wong (1)
Cindy L. K. Lam (2)
Carlos K. H. Wong (2)
1. Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, China
2. Department of Family Medicine and Primary Care, The University of Hong Kong, 3/F, Ap Lei Chau Clinic, 161 Ap Lei Chau Main Street, Ap Lei Chau, Hong Kong, China - ISSN：1573-2649
文摘
Background This study sought to improve the predicative performance and goodness-of-fit of mapping models, as part of indirect valuation, by introducing cubic spline smoothing to map a group of health-related quality of life (HRQOL) measures onto a preference-based measure. Methods This study was a secondary analysis of a cross-sectional health survey data assessing the HRQOL for patients with colorectal neoplasms. Mapping functions of condition-specific functional assessment of cancer therapy—colorectal (FACT-C) onto preference-based SF-6D measure were developed using a dataset of 553 Chinese subjects with different stages of colorectal neoplasm. The missing values of FACT-C were imputed using multiple imputation. Then three widely applicable models (ordinary least square (OLS), Tobit and two-part models) were employed for the mapping function after applying the cubic spline smoothing on the data. For the evaluation of the effectiveness of cubic spline smoothing and multiple imputation, the goodness-of-fit and prediction performance of each model were compared. Results Analyses showed that the models fitted with transformed data from cubic spline smoothing offered better performance in goodness-of-fit and prediction than the models fitted with the original data. The values of \(R^2\) were improved by over 10?%, and the root mean square error and the mean absolute error were both reduced. The best goodness-of-fit and performance were achieved by OLS model using transformed data from cubic spline smoothing. Conclusions Cubic spline smoothing and multiple imputation were recommended for the mapping of HRQOL measures onto the preference-based measure. Among the three mapping models, the simple-to-use OLS model had the best performance.