Top

Gepubliceerd in:

01-02-2012

Measures of relative importance for health-related quality of life

Auteurs: Tolulope T. Sajobi, Lisa M. Lix, Ian Clara, John Walker, Lesley A. Graff, Patricia Rawsthorne, Norine Miller, Linda Rogala, Rachel Carr, Charles N. Bernstein

Gepubliceerd in: Quality of Life Research | Uitgave 1/2012

Abstract

Purpose

In health-related quality of life (HRQOL) studies, data are often collected on multiple domains for two or more groups of study participants. Quantitative measures of relative importance, which are used to rank order the domains based on their ability to discriminate between groups, are an alternative to multiple tests of significance on the group differences. This study describes relative importance measures based on logistic regression (LR) and multivariate analysis of variance (MANOVA) models.

Methods

Relative importance measures are illustrated using data from the Manitoba Inflammatory Bowel Disease (IBD) Cohort Study. Study participants with self-reported active (n = 244) and inactive (n = 105) disease were compared on 12 HRQOL domains from the Inflammatory Bowel Disease Questionnaire (IBDQ) and Medical Outcomes Study 36-item Short-Form (SF-36) Questionnaire.

Results

All but two relative importance measures ranked the IBDQ bowel symptoms and emotional health domains as most important.

Conclusions

MANOVA-based importance measures are recommended for multivariate normal data and when group covariances are equal, while LR measures are recommended for non-normal data and when the correlations among the domains are small. Relative importance measures can be used in exploratory studies to identify a small set of domains for further research.

volgende artikel Rasch analysis of the Patient and Observer Scar Assessment Scale (POSAS) in burn scars

Alleen toegankelijk voor geautoriseerde gebruikers

Fairclough, D. L. (2002). Design and analysis of quality of life studies in clinical trials. New York: Chapman & Hall.

Sheehan-Holt, J. K. (1998). MANOVA simultaneous test procedures: The power and robustness of restricted multivariate contrasts. Journal of Educational and Psychological Measurement, 58, 861–881.CrossRef

Wu, Y., Hu, W., Xia, Y., Ma, J., Liu, M., & Cui, N. (2006). Quality of life of nasopharyngeal carcinoma survivors in Mainland China. Quality of Life Research, 16, 65–74.PubMedCrossRef

Laaksonen, C., Aromaa, M., Heinonen, O. J., Koivusilta, L., Koski, P., Suominen, S., et al. (2008). Health related quality of life in 10-year-old school children. Quality of Life Research, 17, 1049–1054.PubMedCrossRef

Dunn, W. S., Mount, M. K., Barrick, M. R., & Ones, D. S. (1995). Relative importance of personality and general mentality ability in managers’ judgments of applicant qualifications. Journal of Applied Psychology, 80, 500–509.PubMedCrossRef

Hobson, C. J., & Gibson, F. W. (1983). Policy capturing as an approach to understanding and improving performance appraisal: A review of the literature. Academy of Management Review, 8, 640–649.PubMed

Healy, M. J. R. (1990). Measuring importance. Statistics in Medicine, 9, 633–637.PubMedCrossRef

Curran, D., Molenberghs, G., Fayers, P. M., & Machin, D. (1998). Incomplete quality of life data in randomized trials: Missing forms. Statistics in Medicine, 17, 697–709.PubMedCrossRef

Rose, M. S., & Koshman, M. L. (1999). Statistical issues encountered in the comparison of health-related quality of life in diseased patients to published general population norms: Problems and solutions. Journal of Clinical Epidemiology, 52, 405–412.PubMedCrossRef

10.

Beaumont, J. L., Lix, L. M., Yost, K. J., & Hahn, E. A. (2006). Application of robust statistical methods for sensitivity analysis of health-related quality of life outcomes. Quality of Life Research, 15, 349–356.PubMedCrossRef

11.

Menard, S. (1995). Applied logistic regression analysis. Thousand Oaks, CA: Sage.

12.

Thomas, D. R., Zumbo, B. D., Zhu, P., & Dutta, S. (2008). On measuring the relative importance of explanatory variables in a logistic regression. Journal of Modern Applied Statistical Methods, 7, 21–38.

13.

Traxel, N. M., & Azen, R. (2006). Predictor importance in logistic regression: An extension of dominance analysis. Poster presented at the 114th annual convention of the American psychological association, New Orleans, LA.

14.

Tonidandale, S., & LeBreton, J. M. (2010). Determining the relative importance of predictors in logistic regression: An extension of relative weight analysis. Organizational Research Methods, 13, 767–781.CrossRef

15.

Huberty, C. J., & Wisenbaker, J. M. (1992). Variable importance in multivariate group comparisons. Journal of Educational Statistics, 17, 75–91.CrossRef

16.

Thomas, D. R. (1992). Interpreting discriminant functions: A data analytic approach. Multivariate Behavioral Research, 27, 335–362.CrossRef

17.

Cohen, J. (1962). The statistical power of abnormal-social psychological research: A review. Journal of Abnormal and Social Psychology, 65, 145–153.PubMedCrossRef

18.

Olejnik, S., & Algina, J. (2000). Measures of effect size for comparative studies: Applications, interpretations, and limitations. Educational Psychology, 25, 241–286.

19.

Agresti, A. (1996). An introduction to categorical data analysis. New York: Wiley.

20.

Menard, S. (2004). Six approaches to calculating standardized logistic regression coefficients. The American Statistician, 58, 218–223.CrossRef

21.

Agresti, A., & Finlay, B. (1986). Statistical methods for the social sciences (2nd ed.). San Francisco: Dellen/Macmillan.

22.

Kaufman, R. L. (1996). Comparing effects in dichotomous logistic regression: A variety of standardized coefficients. Social Science Quarterly, 77, 90–110.

23.

Nagelkerke, N. J. D. (1991). A note on a general definition of coefficient of determination. Biometrika, 78, 691–692.CrossRef

24.

Cox, D. R., & Snell, E. J. (1989). The analysis of binary data (2nd ed.). London: Chapman and Hall.

25.

Estrella, A. (1998). A new measure of fit for equations with dichotomous dependent variables. Journal of Business and Economic Statistics, 16, 198–205.CrossRef

26.

Pratt, J. W. (1987). Dividing the indivisible: Using simple symmetry to partition variance explained. In T. Pukkila & S. Puntanen (Eds.), Proceedings of the second international conference in statistics (pp. 245–260). Tampere, Finland: University of Tampere.

27.

Thomas, D. R., Hughes, E., & Zumbo, B. D. (1998). On variable importance in linear regression. Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, 45, 253–275.

28.

Ochieng, C. O., & Zumbo, B. D. (2001). Examination of a variable ordering index in linear regression models: An assessment of the relative Pratt index in Likert data. Paper presented at the Bob Conry conference on measurement evaluation and research and methodology, Vancouver, Canada.

29.

Witherill, G. G. (1986). Regression analysis with applications. London: Chapman & Hall.

30.

Azen, R., & Budescu, D. V. (2003). Comparing predictors in multivariate regression models: An extension of dominance analysis. Journal of Educational and Behavioral Statistics, 31, 157–180.CrossRef

31.

Johnson, J. W. (2000). A heuristic method for estimating the relative weight of predictor variables in multiple regression. Multivariate Behavioral Research, 35, 1–19.CrossRef

32.

Thomas, D. R., & Zumbo, B. D. (1996). Using a measure of variable importance to investigate the standardization of discriminant coefficients. Journal of Educational and Behavioral Statistics, 21, 110–130.

33.

Huberty, C. J., & Olejnik, S. (2006). Applied MANOVA and discriminant analysis. Hoboken, New Jersey: Wiley.CrossRef

34.

Pohar, M., Blas, M., & Turk, S. (2004). Comparison of logistic regression and linear discriminant analysis: A simulation study. Metodoloski zveski, 1, 143–161.

35.

Jennrich, R. I. (1977). Stepwise discriminant analysis. In K. Enslein, A. Ralston, & H. S. Wilf (Eds.), Mathematical methods for digital computers (Vol. 3). New York: Wiley.

36.

Rencher, A. C. (1992). Interpretation of canonical discriminant functions, canonical variates, and principal components. The American Statistician, 46, 217–225.CrossRef

37.

Finch, W. H., & Laking, T. (2008). Evaluation of the use of standardized weights for interpreting results from a descriptive discriminant analysis. Multiple Linear Regression Viewpoints, 34, 19–34.

38.

Gibbons, L. B., & Hosmer, D. W. (1991). Conditional logistic regression with missing data. Communications in Statistics, Part B, 20, 109–120.

39.

Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing data (2nd ed.). New Jersey: Wiley.

40.

SAS Institute Inc. (2008). SAS/STAT user’s guide, version 9.2. Cary, NC: SAS Institute Inc.

41.

Lix, L. M., Graff, L. A., Walker, J. R. L., et al. (2008). Longitudinal study of quality of life and psychological functioning for active, fluctuating, and inactive disease patterns. Inflammatory Bowel Diseases, 14, 1575–1584.PubMedCrossRef

42.

Graff, L. A., Walker, J. R., Lix, L. M., et al. (2006). The relationship of inflammatory bowel disease type and activity to psychological functioning and quality of life. Clinical Gastoroenterology and Hepatology, 4, 1491–1501.CrossRef

43.

Clara, I., Lix, L. M., Walker, J. R., et al. (2009). The Manitoba IBD index: Evidence for a new and simple indicator of IBD activity. American Journal of Gastroenterology, 104, 1754–1763.PubMedCrossRef

44.

Guyatt, G. H., Mitchell, A., Irvine, E. J., et al. (1989). A new measure of health status for clinical trials in inflammatory bowel disease. Gastroenterology, 96, 804–810.PubMed

45.

Ware, J. E., & Sherbourne, C. D. (1992). The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Medical Care, 30, 473–483.PubMedCrossRef

46.

Irvine, E. J. (1996). Effects of budesonide therapy on quality of life in active Crohn′s disease. Respiratory Clinical Forums, 18, 81–89.

47.

Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52, 591–611.

48.

LeBreton, J. M., & Tonidandel, S. (2008). Multivariate relative importance: Extending relative weight analysis to ultivariate criterion spaces. Journal of Applied Psychology, 93, 329–345.PubMedCrossRef

49.

Lievens, F., Van Hoye, G., & Schreurs, B. (2005). Examining the relationship between employer knowledge dimensions and organizational attractiveness: An application in a military context. Journal of Occupational and Organizational Psychology, 78, 553–572.CrossRef

50.

Glomb, T. M., Kammeyer-Mueller, J. D., & Rotundo, M. (2004). Emotional labor demands and compensating wage differentials. Journal of Applied Psychology, 84, 700–714.CrossRef

51.

Hawkins, D. M. (1981). A new test for multivariate normality and homoscedasticity. Technometrics, 23, 105–110.CrossRef

52.

Box, G. E. P. (1949). A general distribution theory for a class of likelihood criteria. Biometrika, 36, 317–346.PubMed

53.

Mardia, K. V. (1980). Tests of univariate and multivariate normality. In P. R. Krishnaiah (Ed.), Handbook of statistics (Vol. 1, pp. 279–320). Amsterdam: North-Holland.

54.

Shapiro, S. S., Wilk, M. B., & Chen, H. J. (1968). A comparative study of various tests for normality. Journal of the American Statistical Association, 63, 1343–1372.CrossRef

55.

Huberty, C. J. (1975). The stability of three indices of relative variable contribution in discriminant analysis. Journal of Experimental Education, 44, 59–64.

56.

Dalgleish, L. I. (1994). Discriminant analysis: Statistical inference using the jackknife and bootstrap procedures. Psychological Bulletin, 116, 498–508.CrossRef

57.

Guyatt, G. H., Osoba, D., Wu, A. W., Wyrwich, K. W., Norman, G. R., & the Clinical Significance Consensus Meeting Group. (2002). Methods to explain the clinical significance of health status measures. Mayo Clinic Proceedings, 77, 371–383.PubMedCrossRef

58.

Wyrwich, K. W., Nienaber, N. A., Tierney, W. M., & Wolinsky, F. D. (1999). Linking clinical relevance and statistical significance in evaluating intra-individual changes in health-related quality of life. Medical Care, 37, 469–478.PubMedCrossRef

59.

Kim, K., & Timm, N. (2007). Univariate and multivariate general linear models: Theory and applications with SAS (2nd ed.). New York: Chapman & Hall/CRC.

60.

Benjamini, Y., & Hochberg, Y. (1997). Multiple hypotheses testing with weights. Scandinavian Journal of Statistics, 24, 407–418.CrossRef

61.

Maurer, W., Hothorn, L. A., & Lechmacher, W. (1995). Multiple comparisons in drug clinical trials and preclinical assays: A priori ordered hypotheses. In J. Vollman (Ed.), Biometrie in der chemische-pharmazeutichen Industrie (Vol. 6). Stuttgart: Fischer Verlag.

Titel: Measures of relative importance for health-related quality of life
Auteurs: Tolulope T. Sajobi
Lisa M. Lix
Ian Clara
John Walker
Lesley A. Graff
Patricia Rawsthorne
Norine Miller
Linda Rogala
Rachel Carr
Charles N. Bernstein
Publicatiedatum: 01-02-2012
Uitgeverij: Springer Netherlands
Gepubliceerd in: Quality of Life Research / Uitgave 1/2012
Print ISSN: 0962-9343
Elektronisch ISSN: 1573-2649
DOI: https://doi.org/10.1007/s11136-011-9914-7

Bohn Stafleu van Loghum

Deel dit onderdeel of sectie (kopieer de link)

Abstract

Purpose

Methods

Results

Conclusions

Log in om toegang te krijgen

Andere artikelen Uitgave 1/2012

Validation of the EQ-5D in a general population sample in urban China

Overweight and its impact on the health-related quality of life in children and adolescents: results from the European KIDSCREEN survey

Psychometric properties and gender invariance of the Chinese version of the self-report pediatric quality of life inventory version 4.0: short form is acceptable

Evaluation of the quality of life of children with celiac disease and their parents: a case–control study

Rasch analysis of the Patient and Observer Scar Assessment Scale (POSAS) in burn scars

Mothers of infants with congenital heart defects: well-being from pregnancy through the child’s first six months