nach oben

Quality of Life Research

Erschienen in:

01.06.2019

Mapping the Strengths and Difficulties Questionnaire onto the Child Health Utility 9D in a large study of children

verfasst von: Rajan Sharma, Yuanyuan Gu, Kompal Sinha, Mona Aghdaee, Bonny Parkinson

Erschienen in: Quality of Life Research | Ausgabe 9/2019

Einloggen, um Zugang zu erhalten

Abstract

Purpose

Non-preference-based measures cannot be used to directly obtain utilities but can be converted to preference-based measures through mapping. The only mapping algorithm for estimating Child Health Utility-9D (CHU9D) utilities from Strengths and Difficulties Questionnaire (SDQ) responses has limitations. This study aimed to develop a more accurate algorithm.

Methods

We used a large sample of children (n = 6898), with negligible missing data, from the Longitudinal Study of Australian Children. Exploratory factor analysis (EFA) and Spearman’s rank correlation coefficients were used to assess conceptual overlap between SDQ and CHU9D. Direct mapping (involving seven regression methods) and response mapping (involving one regression method) approaches were considered. The final model was selected by ranking the performance of each method by averaging the following across tenfold cross-validation iterations: mean absolute error (MAE), mean squared error (MSE), and MAE and MSE for two subsamples where predicted utility values were < 0.50 (poor health) or > 0.90 (healthy). External validation was conducted using data from the Child and Adolescent Mental Health Services study.

Results

SDQ and CHU9D were moderately correlated (ρ = − 0.52, p < 0.001). EFA demonstrated that all CHU9D domains were associated with four SDQ subscales. The best-performing model was the Generalized Linear Model with SDQ items and gender as predictors (full sample MAE: 0.1149; MSE: 0.0227). The new algorithm performed well in the external validation.

Conclusions

The proposed mapping algorithm can produce robust estimates of CHU9D utilities from SDQ data for economic evaluations. Further research is warranted to assess the applicability of the algorithm among children with severe health problems.

Nur mit Berechtigung zugänglich

Utilities are measured on a cardinal scale, anchored at 0 (death) and 1 (full health) [1, 5, 9]. Utilities less than zero are possible if a health state is considered to be worse than death [1].

MAUIs are a health-related quality of life questionnaires which are associated with an algorithm to convert responses to the included questions into utilities.

Mapping is the process of establishing a statistical relationship between a non-PBM and a PBM using regression techniques.

It can certainly be updated using the new version of the CHU9D instrument but to our best knowledge such an updated algorithm is not publicly available. In this study we obtained a subsample of the original data used in Furber et al. [24] from the lead author and updated their algorithm to be used as a comparator of our new algorithm in the external validation.

For GLM and EEE the disutility was used as the outcome variable to avoid non-negative values.

Brant test was used to check proportional odds assumption to determine the ordered nature of CHU9D domains. Parallel assumption or proportional odds assumption is an assumption of an ordered logit model. This assumption assumes that coefficients between different categories of the dependent variable are equal.

The MAE was calculated as mean of the absolute values of the difference between the observed and predicted CHU9D utilities.

The MSE were computed as the mean squared differences between the predicted and observed CHU9D utilities.

Formula: $\% \; {\text{Error}}\; {\text{for}}\;{\text{GMAE}} = 100 \times \frac{\text{MAE}}{{\left( {{\text{Max}}\left( {{\text{CHU}}9{\text{D}}\; {\text{utilities}}} \right) - {\text{Min}}\left( {{\text{CHU}}9{\text{D}}\; {\text{utilities}}} \right)} \right)}}.$

Formula: $\% \;{\text{Error}}\; {\text{for}}\; {\text{GMSE}} = 100 \times \frac{\text{MSE}}{{\left( {{\text{Max}}\left( {{\text{CHU}}9{\text{D}}\;{\text{utilities}}} \right) - {\text{Min}}\left( {{\text{CHU}}9{\text{D }}\;{\text{utilities}}} \right)} \right)}}.$

Note: $\left( {{\text{Max}}\left( {{\text{CHU}}9{\text{D}}\;{\text{utilities}}} \right)} \right) = 1\;\;{\text{and}}\;\;\left( {{\text{Min}}\left( {{\text{CHU}}9{\text{D}}\;{\text{utilities}}} \right)} \right) = - \,0.1059$

We used new CHU9D tariff to obtain observed CHU9D variable in the CAMHS dataset, ran an OLS regression with SDQ subscales as predictors and obtained coefficients to be used as new or modified mapping algorithm for Furber et al.

Drummond, M. F., Sculpher, M. J., Claxton, K., Stoddart, G. L., & Torrance, G. W. (2015). Methods for the economic evaluation of health care programmes. Oxford: Oxford University Press.

Canadian Agency for Drugs Technologies in Health. (2006). Guidelines for economic evaluation of pharmaceuticals: Canada. Ottawa: Canadian Agency for Drugs and Technologies in Health.

National Institute for Health and Clinical Excellence. (2013). Guide to the methods of technology appraisal 2013.

Pharmaceutical Benefits Advisory Committee. (2016). Guidelines for preparing a submission to the Pharmaceutical Benefits Advisory Committee (version 5.0). Australian Government Department of Health.

Whitehead, S. J., & Ali, S. (2010). Health outcomes in economic evaluation: The QALY and utilities. British Medical Bulletin, 96(1), 5–21. https://doi.org/10.1093/bmb/ldq033.CrossRefPubMed

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), 215–225.PubMedCrossRef

Harris, A. H., Hill, S. R., Chin, G., Li, J. J., & Walkom, E. (2008). The role of value for money in public insurance coverage decisions for drugs in Australia: A retrospective analysis 1994–2004. Medical Decision Making, 28(5), 713–722.PubMedCrossRef

Neumann, P. J., Cohen, J. T., & Weinstein, M. C. (2014). Updating cost-effectiveness—The curious resilience of the $50,000-per-QALY threshold. New England Journal of Medicine, 371(9), 796–797.PubMedCrossRef

Shiell, A., Donaldson, C., Mitton, C., & Currie, G. (2002). Health economic evaluation. Journal of Epidemiology and Community Health, 56(2), 85–88.PubMedPubMedCentralCrossRef

10.

Tolley, K. (2009). What are health utilities. London: Hayward Medical Communications.

11.

Torrance, G. W. (1987). Utility approach to measuring health-related quality of life. Journal of Chronic Diseases, 40(6), 593–600.PubMedCrossRef

12.

Dolan, P. (1997). Modeling valuations for EuroQol health states. Medical Care, 35, 1095–1108.PubMedCrossRef

13.

Shaw, J. W., Johnson, J. A., & Coons, S. J. (2005). US valuation of the EQ-5D health states: Development and testing of the D1 valuation model. Medical Care, 43, 203–220.PubMedCrossRef

14.

Brazier, J., Roberts, J., & Deverill, M. (2002). The estimation of a preference-based measure of health from the SF-36. Journal of Health Economics, 21(2), 271–292.PubMedCrossRef

15.

Brazier, J. E., & Roberts, J. (2004). The estimation of a preference-based measure of health from the SF-12. Medical Care, 42(9), 851–859.PubMedCrossRef

16.

Torrance, G. W. (1976). Social preferences for health states: An empirical evaluation of three measurement techniques. Socio-economic Planning Sciences, 10(3), 129–136.CrossRef

17.

Farquhar, P. H. (1984). State of the art—Utility assessment methods. Management Science, 30(11), 1283–1300.CrossRef

18.

Kontodimopoulos, N., Argiriou, M., Theakos, N., & Niakas, D. (2011). The impact of disease severity on EQ-5D and SF-6D utility discrepancies in chronic heart failure. The European Journal of Health Economics, 12(4), 383–391.PubMedCrossRef

19.

Kularatna, S., Byrnes, J., Chan, Y. K., Carrington, M. J., Stewart, S., & Scuffham, P. A. (2017). Comparison of contemporaneous responses for EQ-5D-3L and Minnesota living with heart failure: A case for disease specific multiattribute utility instrument in cardiovascular conditions. International Journal of Cardiology, 227, 172–176.PubMedCrossRef

20.

Wailoo, A. J., Hernandez-Alava, M., Manca, A., Mejia, A., Ray, J., Crawford, B., et al. (2017). Mapping to estimate health-state utility from non-preference-based outcome measures: An ISPOR good practices for outcomes research task force report. Value in Health, 20(1), 18–27.PubMedCrossRef

21.

Calxton, K., Martin, S., Soares, M., Rice, N., Spackman, E., Hinde, S., et al. (2013). Methods for the estimation of the NICE cost effectiveness threshold. New York: Centre for Health Economics, University of York.

22.

Committee, Pharmaceutical Benefits Advisory. (2016). Guidelines for preparing submissions to the Pharmaceutical Benefits Advisory Committee (PBAC). Version 5.0. Canberra: Department of Health.

23.

Kearns, B., Ara, R., Wailoo, A., Manca, A., Alava, M. H., Abrams, K., et al. (2013). Good practice guidelines for the use of statistical regression models in economic evaluations. Pharmacoeconomics, 31(8), 643–652.PubMedCrossRef

24.

Furber, G., Segal, L., Leach, M., & Cocks, J. (2014). Mapping scores from the strengths and difficulties questionnaire (SDQ) to preference-based utility values. Quality of Life Research, 23(2), 403–411.PubMedCrossRef

25.

Ratcliffe, J., Flynn, T., Terlich, F., Stevens, K., Brazier, J., & Sawyer, M. (2012). Developing adolescent-specific health state values for economic evaluation. Pharmacoeconomics, 30(8), 713–727.PubMedCrossRef

26.

Gray, L. A., Alava, M. H., & Wailoo, A. J. (2017). Development of methods for the mapping of utilities using mixture models: Mapping the AQLQ-S to the EQ-5D-5L and the HUI3 in patients with asthma. Value in Health, 21(6), 748–757.PubMedCrossRef

27.

Varni, J. W., Burwinkle, T. M., & Lane, M. M. (2005). Health-related quality of life measurement in pediatric clinical practice: An appraisal and precept for future research and application. Health and Quality of Life Outcomes, 3(1), 34.PubMedPubMedCentralCrossRef

28.

Edwards, B. (2014). Growing up in Australia: The longitudinal study of Australian children: Entering adolescence and becoming a young adult. Family Matters, 95, 5.

29.

Bennett, D. A. (2001). How can I deal with missing data in my study? Australian and New Zealand Journal of Public Health, 25(5), 464–469.PubMedCrossRef

30.

Schafer, J. L. (1999). Multiple imputation: A primer. Statistical Methods in Medical Research, 8(1), 3–15.PubMedCrossRef

31.

Refaeilzadeh, P., Tang, L., & Liu, H. (2016). Cross-validation. Encyclopedia of database systems (pp. 1–7). Boston: Springer.

32.

Goodman, R. (1997). The strengths and difficulties questionnaire: A research note. Journal of Child Psychology and Psychiatry, 38(5), 581–586.PubMedCrossRef

33.

Stevens, K. (2011). Assessing the performance of a new generic measure of health-related quality of life for children and refining it for use in health state valuation. Applied Health Economics and Health Policy, 9(3), 157–169.PubMedCrossRef

34.

Stevens, K. (2009). Developing a descriptive system for a new preference-based measure of health-related quality of life for children. Quality of Life Research, 18(8), 1105–1113.PubMedCrossRef

35.

StataCorp. (2017). Stata statistical software: Release 15. College Station, TX: StataCorp LLC.

36.

Petrou, S., Rivero-Arias, O., Dakin, H., Longworth, L., Oppe, M., Froud, R., et al. (2015). The MAPS reporting statement for studies mapping onto generic preference-based outcome measures: Explanation and elaboration. Pharmacoeconomics, 33(10), 993–1011.PubMedCrossRef

37.

Tosh, J. C., Longworth, L. J., & George, E. (2011). Utility values in National Institute for Health and Clinical Excellence (NICE) technology appraisals. Value in Health, 14(1), 102–109.PubMedCrossRef

38.

Round, J., & Hawton, A. (2017). Statistical alchemy: Conceptual validity and mapping to generate health state utility values. PharmacoEconomics-Open, 1(4), 233–239.PubMedPubMedCentralCrossRef

39.

Schroeder, M. A., Lander, J., & Levine-Silverman, S. (1990). Diagnosing and dealing with multicollinearity. Western Journal of Nursing Research, 12(2), 175–187.PubMedCrossRef

40.

Dziuban, C. D., & Shirkey, E. C. (1974). When is a correlation matrix appropriate for factor analysis? Some decision rules. Psychological Bulletin, 81(6), 358.CrossRef

41.

Tobias, S., & Carlson, J. E. (1969). Brief report: Bartlett’s test of sphericity and chance findings in factor analysis. Multivariate Behavioral Research, 4(3), 375–377.PubMedCrossRef

42.

Brown, J. (2001). What is an eigenvalue? JALT Testing & Evaluation SIG Newsletter, 5(1), 15–19.

43.

Izquierdo, I., Olea, J., & Abad, F. J. (2014). Exploratory factor analysis in validation studies: Uses and recommendations. Psicothema, 26(3), 395–400.PubMed

44.

Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. London: MIT Press.

45.

Nelder, J. A., & Baker, R. J. (2004). Generalized linear models. Encyclopedia of Statistical Sciences. New York: Wiley.

46.

Masyn, K., Nathan, P., & Little, T. (2013). The Oxford handbook of quantitative methods. Statistical analysis (Vol. 2). Oxford: Oxford University Press.

47.

Manning, W. G., & Mullahy, J. (2001). Estimating log models: To transform or not to transform? Journal of Health Economics, 20(4), 461–494.PubMedCrossRef

48.

Pregibon, D. (1980). Goodness of link tests for generalized linear models. Applied Statistics, 29, 14–15.CrossRef

49.

Pearson, E., & Please, N. (1975). Relation between the shape of population distribution and the robustness of four simple test statistics. Biometrika, 62(2), 223–241.CrossRef

50.

Hosmer, D. W., Jr., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (Vol. 398). New York: Wiley.CrossRef

51.

Glick, H. A., Doshi, J. A., Sonnad, S. S., & Polsky, D. (2014). Economic evaluation in clinical trials. Oxford: Oxford University Press.CrossRef

52.

Basu, A. (2005). Extended generalized linear models: Simultaneous estimation of flexible link and variance functions. Stata Journal, 5(4), 501.CrossRef

53.

Swearingen, C. J., Castro, M. M., & Bursac, Z. (2012). Inflated beta regression: Zero, one and everything in between. In: SAS global forum, 2012 (pp. 1–11)

54.

McDonald, J. F., & Moffitt, R. A. (1980). The uses of Tobit analysis. The Review of Economics and Statistics, 62, 318–321.CrossRef

55.

Longworth, L., Yang, Y., Young, T., Mulhern, B., Hernandez Alava, M., Mukuria, C., et al. (2014). Use of generic and condition-specific measures of health-related quality of life in NICE decision-making: A systematic review, statistical modelling and survey. Health Technology Assessment, 18, 1–224.PubMedCrossRef

56.

Brennan, D. S., & Spencer, A. J. (2006). Mapping oral health related quality of life to generic health state values. BMC Health Services Research, 6(1), 96.PubMedPubMedCentralCrossRef

57.

Powell, J. L. (1984). Least absolute deviations estimation for the censored regression model. Journal of Econometrics, 25(3), 303–325.CrossRef

58.

Sullivan, P. W., & Ghushchyan, V. (2006). Mapping the EQ-5D index from the SF-12: US general population preferences in a nationally representative sample. Medical Decision Making, 26(4), 401–409.PubMedPubMedCentralCrossRef

59.

McLachlan, G., & Peel, D. (2004). Finite mixture models. New York: Wiley.

60.

Alava, M. H., Wailoo, A. J., & Ara, R. (2012). Tails from the peak district: Adjusted limited dependent variable mixture models of EQ-5D questionnaire health state utility values. Value in Health, 15(3), 550–561.CrossRef

61.

Hernandez Alava, M., & Wailoo, A. (2015). Fitting adjusted limited dependent variable mixture models to EQ-5D. Stata Journal, 15(3), 737–750.CrossRef

62.

Grun, B., & Leisch, F. (2008). FlexMix version 2: Finite mixtures with concomitant variables and varying and constant parameters. Journal of Statistical Software, 28(4), 1–35.CrossRef

63.

Ratcliffe, J., Huynh, E., Chen, G., Stevens, K., Swait, J., Brazier, J., et al. (2016). Valuing the Child Health Utility 9D: Using profile case best worst scaling methods to develop a new adolescent specific scoring algorithm. Social Science and Medicine, 157, 48–59.PubMedCrossRef

64.

Le, Q. A., & Doctor, J. N. (2011). Probabilistic mapping of descriptive health status responses onto health state utilities using Bayesian networks: An empirical analysis converting SF-12 into EQ-5D utility index in a national US sample. Medical Care, 49, 451–460.PubMedCrossRef

65.

Gray, A. M., Rivero-Arias, O., & Clarke, P. M. (2006). Estimating the association between SF-12 responses and EQ-5D utility values by response mapping. Medical Decision Making, 26(1), 18–29.PubMedCrossRef

66.

Steyerberg, E. (2009). Validation of prediction models. Clinical prediction models (pp. 299–311). Berlin: Springer.CrossRef

67.

Kaiser, H. F., & Rice, J. (1974). Little jiffy, mark IV. Educational and Psychological Measurement, 34(1), 111–117.CrossRef

68.

Collado-Mateo, D., Chen, G., Garcia-Gordillo, M. A., Iezzi, A., Adsuar, J. C., Olivares, P. R., et al. (2017). Fibromyalgia and quality of life: Mapping the revised fibromyalgia impact questionnaire to the preference-based instruments. Health and Quality of Life Outcomes, 15(1), 114.PubMedPubMedCentralCrossRef

69.

Teckle, P., McTaggart-Cowan, H., Van der Hoek, K., Chia, S., Melosky, B., Gelmon, K., et al. (2013). Mapping the FACT-G cancer-specific quality of life instrument to the EQ-5D and SF-6D. Health and Quality of Life Outcomes, 11(1), 203.PubMedPubMedCentralCrossRef

70.

Kay, S., Tolley, K., Colayco, D., Khalaf, K., Anderson, P., & Globe, D. (2013). Mapping EQ-5D utility scores from the Incontinence Quality of Life Questionnaire among patients with neurogenic and idiopathic overactive bladder. Value in Health, 16(2), 394–402.PubMedCrossRef

71.

Jones, A. M., Lomas, J., Moore, P., & Rice, N. (2013). A quasi-Monte Carlo comparison of developments in parametric and semi-parametric regression methods for heavy tailed and non-normal data: With an application to healthcare costs. Health Econometrics and Data Group Working Paper, 13, 30.

72.

Lamu, A. N., & Olsen, J. A. (2018). Testing alternative regression models to predict utilities: Mapping the QLQ-C30 onto the EQ-5D-5L and the SF-6D. Quality of Life Research, 27(11), 2823–2839.PubMedCrossRef

73.

Rowen, D., Brazier, J., & Roberts, J. (2009). Mapping SF-36 onto the EQ-5D index: How reliable is the relationship? Health and Quality of Life Outcomes, 7(1), 27.PubMedPubMedCentralCrossRef

74.

Goldsmith, K. A., Dyer, M. T., Buxton, M. J., & Sharples, L. D. (2010). Mapping of the EQ-5D index from clinical outcome measures and demographic variables in patients with coronary heart disease. Health and Quality of Life Outcomes, 8(1), 54.PubMedPubMedCentralCrossRef

Titel: Mapping the Strengths and Difficulties Questionnaire onto the Child Health Utility 9D in a large study of children
verfasst von: Rajan Sharma
Yuanyuan Gu
Kompal Sinha
Mona Aghdaee
Bonny Parkinson
Publikationsdatum: 01.06.2019
Verlag: Springer International Publishing
Erschienen in: Quality of Life Research / Ausgabe 9/2019
Print ISSN: 0962-9343
Elektronische ISSN: 1573-2649
DOI: https://doi.org/10.1007/s11136-019-02220-x

Springer Medizin

Abstract

Purpose

Methods

Results

Conclusions

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Weitere Artikel der Ausgabe 9/2019

A psychometric evaluation of the Chinese version of the Nursing Home Adjustment Scale

Communicating psychosocial well-being in motor neurone disease to staff: results from a World Café approach

Socioeconomic inequalities in oral health-related quality of life in adolescents: a cohort study

The role of family functioning and self-esteem in the quality of life of adolescents referred for psychiatric services: a 3-year follow-up

Methods for questionnaire design: a taxonomy linking procedures to test goals

Health utility and health-related quality of life of Japanese prostate cancer patients according to progression status measured using EQ-5D-5L and FACT-P