Dealing with missing data in the Center for Epidemiologic Studies Depression self-report scale: a study based on the French E3N cohort

BMC Medical Research Methodology

Xavier Paoletti³  Françoise Clavel-Chapelon²  Roch Giorgi¹  Hélène Verdoux⁴  Noémie Resseguier¹ 

[1] Inserm, UMR 912, Aix-Marseille Univ, UMR 912, SESSTIM, Marseille, France;Centre for Research in Epidemiology and Population Health, UMRS 1018, Team 9, Institut Gustave Roussy, Villejuif, France;Inserm U900 Institut Curie, Paris, France;Inserm U657, Bordeaux, France

关键词: Sensitivity analysis;    Non ignorable;    Multiple imputation;    Missing data;    Cohort;    CES-D;   

Others  :  1126108
DOI  :  10.1186/1471-2288-13-28

 received in 2012-09-10, accepted in 2013-02-15,  发布年份 2013

【 摘 要 】

Background

The Center for Epidemiologic Studies - Depression scale (CES-D) is a validated tool commonly used to screen depressive symptoms. As with any self-administered questionnaire, missing data are frequently observed and can strongly bias any inference. The objective of this study was to investigate the best approach for handling missing data in the CES-D scale.

Methods

Among the 71,412 women from the French E3N prospective cohort (Etude Epidémiologique auprès des femmes de la Mutuelle Générale de l’Education Nationale) who returned the questionnaire comprising the CES-D scale in 2005, 45% had missing values in the scale. The reasons for failure to complete certain items were investigated by semi-directive interviews on a random sample of 204 participants. The prevalence of high depressive symptoms (score ≥16, hDS) was estimated after applying various methods for ignorable missing data including multiple imputation using imputation models with CES-D items with or without covariates. The accuracy of imputation models was investigated. Various scenarios of nonignorable missing data mechanisms were investigated by a sensitivity analysis based on the mixture modelling approach.

Results

The interviews showed that participants were not reluctant to answer the CES-D scale. Possible reasons for nonresponse were identified. The prevalence of hDS among complete responders was 26.1%. After multiple imputation, the prevalence was 28.6%, 29.8% and 31.7% for women presenting up to 4, 10 and 20 missing values, respectively. The estimates were robust to the various imputation models investigated and to the scenarios of nonignorable missing data.

Conclusions

The CES-D scale can easily be used in large cohorts even in the presence of missing data. Based on the results from both a qualitative study and a sensitivity analysis under various scenarios of missing data mechanism in a population of women, missing data mechanism does not appear to be nonignorable and estimates are robust to departures from ignorability. Multiple imputation is recommended to reliably handle missing data in the CES-D scale.

【 授权许可】

   
2013 Resseguier et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

【 参考文献 】

• [1]Radloff LS: The CES-D scale: A self report depression scale for research in the general population. Appl Psychol Meas 1977, 1:385-401.
• [2]Lewinsohn PM, Hoberman HM, Rosenbaum M: A prospective study of risk factors for unipolar depression. J Abnorm Psychol 1988, 97:251-264.
• [3]Lin N, Ensel WM: Depression-mobility and its social etiology: the role of life events and social support. J Health Soc Behav 1984, 25:176-188.
• [4]Weissman MM, Sholomskas D, Pottenger M, Prusoff BA, Locke BZ: Assessing depressive symptoms in five psychiatric populations: a validation study. Am J Epidemiol 1977, 106:203-214.
• [5]Bono C, Ried LD, Kimberlin C, Vogel B: Missing data on the Center for Epidemiologic Studies Depression Scale: a comparison of 4 imputation techniques. Res Social Adm Pharm 2007, 3:1-27.
• [6]Little RJ, Rubin DB: Statistical analysis with missing data. 2nd edition. New York: John Wiley & Sons; 2002.
• [7]Klebanoff MA, Cole SR: Use of multiple imputation in the epidemiologic literature. Am J Epidemiol 2008, 168:355-357.
• [8]Rubin DB: Multiple imputation for non response in surveys. New York: John Wiley & Sons; 1987.
• [9]Diggle P, Kenward MG: Informative drop-out in longitudinal data analysis. Appl Stat 1994, 43:49-93.
• [10]Beekman AT, Copeland JR, Prince MJ: Review of community prevalence of depression in later life. Br J Psychiatry 1999, 174:307-311.
• [11]Djernes JK: Prevalence and predictors of depression in populations of elderly: a review. Acta Psychiatr Scand 2006, 113:372-387.
• [12]Lepine JP, Bouchez S: Epidemiology of depression in the elderly. Int Clin Psychopharmacol 1998, 13(Suppl 5):S7-S12.
• [13]Kmetic A, Joseph L, Berger C, Tenenhouse A: Multiple imputation to account for missing data in a survey: estimating the prevalence of osteoporosis. Epidemiology 2002, 13:437-444.
• [14]Nutrition, hormones et cancer: épidémiologie et prévention. ERI 20. L'étude E3N. http://www.e3n.fr/ webcite
• [15]van Liere MJ, Lucas F, Clavel F, Slimani N, Villeminot S: Relative validity and reproducibility of a French dietary history questionnaire. Int J Epidemiol 1997, 26(Suppl 1):S128-S136.
• [16]Brook RH, Chassin MR, Fink A, Solomon DH, Kosecoff J, Park RE: A method for the detailed assessment of the appropriateness of medical technologies. Int J Technol Assess Health Care 1986, 2:53-63.
• [17]Dalkey NC: An experimental study of group opinion. Futures 1969, 1:53-63.
• [18]White IR, Royston P, Wood AM: Multiple imputation using chained equations: Issues and guidance for practice. Stat Med 2011, 30:377-399.
• [19]Resseguier N, Giorgi R, Paoletti X: Sensitivity analysis when data are missing not-at-random. Epidemiology 2011, 22:282.
• [20]Olino TM, Yu L, Klein DN, Rohde P, Seeley JR, Pilkonis PA, Lewinsohn PM: Measuring depression using item response theory: an examination of three measures of depressive symptomatology. Int J Methods Psychiatr Res 2012, 21:76-85.
• [21]R Development Core Team: R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2009.
• [22]van Buuren S, Groothuis-Oudshoorn K: Mice: Multivariate Imputation by Chained Equations library. 2010. [R package version 2.3]
• [23]Callahan CM, Wolinsky FD: The effect of gender and race on the measurement properties of the CES-D in older adults. Med Care 1994, 32:341-356.
• [24]Geerlings SW, Beekman AT, Deeg DJ, Van Tilburg W: Physical health and the onset and persistence of depression in older adults: an eight-wave prospective community-based study. Psychol Med 2000, 30:369-380.
• [25]Haynie DA, Berg S, Johansson B, Gatz M, Zarit SH: Symptoms of depression in the oldest old: a longitudinal study. J Gerontol B Psychol Sci Soc Sci 2001, 56:P111-P118.
• [26]Hays JC, Landerman LR, George LK, Flint EP, Koenig HG, Land KC, Blazer DG: Social correlates of the dimensions of depression in the elderly. J Gerontol B Psychol Sci Soc Sci 1998, 53:P31-P39.
• [27]Kimberlin CL, Pendergast JF, Berardo DH, McKenzie LC: Issues related to using a short-form of the Center for Epidemiological Studies-Depression Scale. Psychol Rep 1998, 83:411-421.
• [28]Zich JM, Attkisson CC, Greenfield TK: Screening for depression in primary care clinics: the CES-D and the BDI. Int J Psychiatry Med 1990, 20:259-277.
• [29]Kohout FJ, Berkman LF, Evans DA, Cornoni-Huntley J: Two shorter forms of the CES-D (Center for Epidemiological Studies Depression) depression symptoms index. J Aging Health 1993, 5:179-193.
• [30]Sapinho D, Chan-Chee C, Briffault X, Guignard R, Beck F: Issues and limits in the measurement of major depressive episodes in the general population. Bulletin épidémiologique hebdomadaire 2008, 35–36:314-317.
• [31]Powers JR, Young AF, Russell A, Pachana NA: Implications of non-response of older women to a short form of the Center for Epidemiologic Studies Depression Scale. Int J Aging Hum Dev 2003, 57:37-54.
• [32]Andresen EM, Malmgren JA, Carter WB, Patrick DL: Screening for depression in well older adults: evaluation of a short form of the CES-D (Center for Epidemiologic Studies Depression Scale). Am J Prev Med 1994, 10:77-84.
• [33]Schwarz N, Knauper B, Park DC, Sudman S: Cognition, aging and self-reports. Philadelphia, PA: Psychology Press; 1999.
• [34]Bebchuk JD, Betensky RA: Multiple imputation for simple estimation of the hazard function based on interval censored data. Stat Med 2000, 19:405-419.
• [35]Giorgi R, Belot A, Gaudart J, Launoy G: The performance of multiple imputation for missing covariate data within the context of regression relative survival analysis. Stat Med 2008, 27:6310-6331.
• [36]Sheppard L, Levy D, Norris G, Larson TV, Koenig JQ: Effects of ambient air pollution on nonelderly asthma hospital admissions in Seattle, Washington, 1987–1994. Epidemiology 1999, 10:23-30.
• [37]Molenberghs G, Burzykowski T, Michiels B, Kenward MG: Analysis of incomplete public health data. Rev Epidemiol Sante Publique 1999, 47:499-514.
• [38]Schafer JL: Analyis of incomplete multivariate data. London: Chapman & Hall; 1997.
• [39]Shrive FM, Stuart H, Quan H, Ghali WA: Dealing with missing data in a multi-question depression scale: a comparison of imputation methods. BMC Med Res Methodol 2006, 6:57. BioMed Central Full Text
• [40]Moons KG, Donders RA, Stijnen T, Harrel FE Jr: Using the outcome for imputation of missing predictor values was preferred. J Clin Epidemiol 2006, 59:1092-1101.
• [41]Fuhrer R, Rouillon F: The French version of the Center for Epidemiologic Studies-Depression Scale. Psychiatr Psychobiol 1989, 4:163-166.
• [42]Lepine JP, Gasquet I, Kovess V, Arbabzadeh-Bouchez S, Negre-Pages L, Nachbaur G, Gaudin AF: Prevalence and comorbidity of psychiatric disorders in the French general population. Encéphale 2005, 31:182-194.