nach oben

Quality of Life Research

Erschienen in:

01.04.2015

Multiple imputation to deal with missing EQ-5D-3L data: Should we impute individual domains or the actual index?

verfasst von: Claire L. Simons, Oliver Rivero-Arias, Ly-Mee Yu, Judit Simon

Erschienen in: Quality of Life Research | Ausgabe 4/2015

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Purpose

Missing data are a well-known and widely documented problem in cost-effectiveness analyses alongside clinical trials using individual patient-level data. Current methodological research recommends multiple imputation (MI) to deal with missing health outcome data, but there is little guidance on whether MI for multi-attribute questionnaires, such as the EQ-5D-3L, should be carried out at domain or at summary score level. In this paper, we evaluated the impact of imputing individual domains versus imputing index values to deal with missing EQ-5D-3L data using a simulation study and developed recommendations for future practice.

Methods

We simulated missing data in a patient-level dataset with complete EQ-5D-3L data at one point in time from a large multinational clinical trial (n = 1,814). Different proportions of missing data were generated using a missing at random (MAR) mechanism and three different scenarios were studied. The performance of using each method was evaluated using root mean squared error and mean absolute error of the actual versus predicted EQ-5D-3L indices.

Results

In large sample sizes (n > 500) and a missing data pattern that follows mainly unit non-response, imputing domains or the index produced similar results. However, domain imputation became more accurate than index imputation with pattern of missingness following an item non-response. For smaller sample sizes (n < 100), index imputation was more accurate. When MI models were misspecified, both domain and index imputations were inaccurate for any proportion of missing data.

Conclusions

The decision between imputing the domains or the EQ-5D-3L index scores depends on the observed missing data pattern and the sample size available for analysis. Analysts conducting this type of exercises should also evaluate the sensitivity of the analysis to the MAR assumption and whether the imputation model is correctly specified.

Vorheriger Artikel Joint modeling of longitudinal health-related quality of life data and survival

Nächster Artikel Quality of life domains important and relevant to family caregivers of advanced cancer patients in an Asian population: a qualitative study

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

Missing data due to dropouts for informative reasons are cases when the participant fails to complete a questionnaire as a result of their severity of their illness, death or other known reason. Non-responders occur when the participant does not respond to a questionnaire at one or multiple time points, which creates different missing data patterns in a dataset. In cross-sectional studies, the main missing data patterns are unit non-response when the participant fails to complete all the items within a questionnaire, and item non-response when the participant fails to complete some of the items within a questionnaire. In longitudinal studies, the participant may drop out before the end of the study and do not return creating a monotone missing data pattern.

Briggs, A., Clark, T., Wolstenholme, J., & Clarke, P. (2003). Missing....presumed at random: Cost-analysis of incomplete data. Health Economics, 12(5), 377–392.CrossRefPubMed

Manca, A., & Palmer, S. (2005). Handling missing data in patient-level cost-effectiveness analysis alongside randomised clinical trials. Applied Health Economics and Health Policy, 4(2), 65–75.CrossRefPubMed

Burton, A., Billingham, L. J., & Bryan, S. (2007). Cost-effectiveness in clinical trials: Using multiple imputation to deal with incomplete cost data. Clinical Trials, 4(2), 154–161.CrossRefPubMed

Grieve, R., Cairns, J., & Thompson, S. G. (2010). Improving costing methods in multicentre economic evaluation: The use of multiple imputation for unit costs. Health Economics, 19(8), 939–954.CrossRefPubMed

Oostenbrink, J. B., & Al, M. J. (2005). The analysis of incomplete cost data due to dropout. Health Economics, 14(8), 763–776.CrossRefPubMed

Yu, L. M., Burton, A., & Rivero-Arias, O. (2007). Evaluation of software for multiple imputation of semi-continuous data. Statistical Methods in Medical Research, 16(3), 243–258.CrossRefPubMed

Faria, R., Gomes, M., Epstein, D., & White, I. R. (2014). A guide to handling missing data in cost-effectiveness analysis conducted within randomised controlled trials. PharmacoEconomics. doi:10.1007/s40273-014-0193-3.

Little, R. J. & D. B. Rubin. (2002). Statistical analysis with missing data. 2nd ed. Wiley Series in Probability and Statistics. Hoboken, NJ: Wiley.

Noble, S. M., Hollingworth, W., & Tilling, K. (2012). Missing data in trial-based cost-effectiveness analysis: The current state of play. Health Economics, 21(2), 187–200.CrossRefPubMed

10.

Wood, A. M., White, I. R., & Thompson, S. G. (2004). Are missing outcome data adequately handled? A review of published randomized controlled trials in major medical journals. Clinical Trials, 1(4), 368–376.CrossRefPubMed

11.

Eekhout, I., de Boer, R. M., Twisk, J. W. R., de Vet, H. C. W., & Heymans, M. W. (2012). Missing data: A systematic review of how they are reported and handled. Epidemiology, 23(5), 729–732.CrossRefPubMed

12.

EuroQol, G. (1990). EuroQol—a new facility for the measurement of health-related quality of life. Health Policy, 16, 199–208.CrossRef

13.

Brooks, R. (1996). EuroQol: The current state of play. Health Policy, 37(1), 53–72.CrossRefPubMed

14.

Horsman, J., Furlong, W., Feeny, D., & Torrance, G. (2003). The health utilities index (HUI): Concepts, measurement properties and applications. Health and Quality of Life Outcomes, 1, 54.CrossRefPubMedCentralPubMed

15.

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

16.

Ratcliffe, J., Young, T., Longworth, L., & Buxton, M. (2005). An assessment of the impact of informative dropout and nonresponse in measuring health-related quality of life using the EuroQol (EQ-5D) descriptive system. Value Health, 8(1), 53–58.CrossRefPubMed

17.

Blough, D. K., Ramsey, S., Sullivan, S. D., & Yusen, R. (2009). The impact of using different imputation methods for missing quality of life scores on the estimation of the cost-effectiveness of lung-volume-reduction surgery. Health Economics, 18(1), 91–101.CrossRefPubMed

18.

Szende, A., M. Oppe, & N. Devlin.(2007). EQ-5D value sets: Inventory, comparative review and user guide. A. Szende, M. Oppe, and N. Devlin. (Eds.) Dordrecht: Springer.

19.

StataCorp. Stata Statistical Software. (2011). Stata Press: College Station. TX: StataCorp LP.

20.

Molyneux, A., Kerr, R., Stratton, I., Sandercock, P., Clarke, M., Shrimpton, J., et al. (2002). International Subarachnoid Aneurysm Trial (ISAT) of neurosurgical clipping versus endovascular coiling in 2143 patients with ruptured intracranial aneurysms: A randomised trial. Lancet, 360(9342), 1267–1274.CrossRefPubMed

21.

Jennett, B., & Bond, M. (1975). Assessment of outcome after severe brain damage. Lancet, 1(7905), 480–484.CrossRefPubMed

22.

van Swieten, J. C., Koudstaal, P. J., Visser, M. C., Schouten, H. J., & van Gijn, J. (1988). Interobserver agreement for the assessment of handicap in stroke patients. Stroke, 19(5), 604–607.CrossRefPubMed

23.

EuroQol Research Foundation (2014). Available from http://www.euroqol.org/. [Accessed 14 September 2014].

24.

Van Buuren, S., Brand, J. P. L., Groothuis-Oudshoorn, C. G. M., & Rubin, D. B. (2006). Fully conditional specification in multivariate imputation. Journal of Statistical Computation and Simulation, 76(12), 1049–1064.CrossRef

25.

Fairbank, J., Frost, H., Wilson-MacDonald, J., Yu, L. M., Barker, K., & Collins, R. (2005). Spine stabilisation trial. Randomised controlled trial to compare surgical stabilisation of the lumbar spine with an intensive rehabilitation programme for patients with chronic low back pain: The MRC spine stabilisation trial. BMJ, 330(7502), 1233.CrossRefPubMedCentralPubMed

26.

Trial Group, K. A. T. (2009). The Knee Arthroplasty Trial (KAT) design features, baseline characteristics, and two-year functional outcomes after alternative approaches to knee replacement. The Journal of Bone and Joint Surgery, 91(1), 134–141.CrossRef

27.

Rivero-Arias, O., Gray, A., Frost, H., Lamb, S. E., & Stewart-Brown, S. (2006). Cost-utility analysis of physiotherapy treatment compared with physiotherapy advice in low back pain. Spine, 31(12), 1381–1387.CrossRefPubMed

28.

Kendrick, T., Simons, L., Mynors-Wallis, L., Gray, A., Lathlean, J., Pickering, R., et al. (2006). Cost-effectiveness of referral for generic care or problem-solving treatment from community mental health nurses, compared with usual general practitioner care for common mental disorders: Randomised controlled trial. British Journal of Psychiatry, 189, 50–59.CrossRefPubMed

29.

Horton, N. J., & Kleinman, K. P. (2007). Much ado about nothing: A comparison of missing data methods and software to fit incomplete data regression models. American Statistician, 61(1), 79–90.CrossRefPubMedCentralPubMed

30.

Harel, O., & Zhou, X. H. (2007). Multiple imputation: Review of theory, implementation and software. Statistics in Medicine, 26(16), 3057–3077.CrossRefPubMed

31.

White, I. R., Royston, P., & Wood, A. M. (2011). Multiple imputation using chained equations: Issues and guidance for practice. Statistics in Medicine, 30(4), 377–399.CrossRefPubMed

32.

Benjamini, Y., & Hochberg, Y. (1995). Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society. Series B (Methodological), 57(1), 289–300.

33.

Efron, B. (1979). The 1977 rietz lecture—bootstrap methods—another look at the Jackknife. The annals of Statistics, 7(1), 1–26.

34.

Kind, P., Hardman, G., & Macran, S. (1999). UK population norms for EQ-5D. UK: Centre for Health Economics, University of York.

35.

Janssen, M. F., Pickard, A. S., Golicki, D., Gudex, C., Niewada, M., Scalone, L., et al. (2013). Measurement properties of the EQ-5D-5L compared to the EQ-5D-3L across eight patient groups: A multi-country study. Quality of Life Research, 22(7), 1717–1727.CrossRefPubMedCentralPubMed

36.

Konig, H. H., Born, A., Gunther, O., Matschinger, H., Heinrich, S., Riedel-Heller, S. G., et al. (2010). Validity and responsiveness of the EQ-5D in assessing and valuing health status in patients with anxiety disorders. Health and Quality of Life Outcomes, 8, 47.CrossRefPubMedCentralPubMed

37.

Long, J. S. (1997). Regression models for categorical and limited dependent variables. London: Sage.

38.

Ramsey, J. B. (1969). Tests for specification errors in classical linear least-squares regression analysis. Journal of the Royal Statistical Society. Series B-Statistical Methodology, 31(2), 350–371.

39.

Carpenter, J. R., Kenward, M. G., & White, I. R. (2007). Sensitivity analysis after multiple imputation under missing at random: A weighting approach. Statistical Methods in Medical Research, 16(3), 259–275.CrossRefPubMed

40.

Herdman, M., Gudex, C., Lloyd, A., Janssen, M., Kind, P., Parkin, D., et al. (2011). Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L). Quality of Life Research, 20(10), 1727–1736.CrossRefPubMedCentralPubMed

Titel: Multiple imputation to deal with missing EQ-5D-3L data: Should we impute individual domains or the actual index?
verfasst von: Claire L. Simons
Oliver Rivero-Arias
Ly-Mee Yu
Judit Simon
Publikationsdatum: 01.04.2015
Verlag: Springer International Publishing
Erschienen in: Quality of Life Research / Ausgabe 4/2015
Print ISSN: 0962-9343
Elektronische ISSN: 1573-2649
DOI: https://doi.org/10.1007/s11136-014-0837-y

Springer Professional

Abstract

Purpose

Methods

Results

Conclusions

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 4/2015

Joint modeling of longitudinal health-related quality of life data and survival

Influence of different prosthodontic rehabilitation options on oral health-related quality of life, orofacial esthetics and chewing function based on patient-reported outcomes

Construct validity of SF-6D health state utility values in an employed population

A new computerized adaptive test advancing the measurement of health-related quality of life (HRQoL) in children: the Kids-CAT

Why visually impaired older adults often do not receive mental health services: the patient’s perspective

The development and validation of a disease-specific quality of life measure in hyperhidrosis: the Hyperhidrosis Quality of Life Index (HidroQOL©)