Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on sleep in brain health

LIVE: Thursday 2nd May 2024, 18:00-19:30 (CEST)

Quality sleep is essential for health. But what happens to our brains when sleep patterns are disturbed? Join our experts to explore the interplay between sleep disruption and neurological diseases, and the questions that you need to be asking your patients to help you prevent the harmful effects of sleep deprivation.
ADVANCED SEARCH
Log in
Top
BMC Medical Research Methodology
Published in: BMC Medical Research Methodology 1/2013

Open Access 01-12-2013 | Research article

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

Authors: Noémie Resseguier, Hélène Verdoux, Roch Giorgi, Françoise Clavel-Chapelon, Xavier Paoletti

Published in: BMC Medical Research Methodology | Issue 1/2013

Login to get access

Abstract

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.
Appendix
Available only for authorised users
Literature
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. 10.1177/014662167700100306.CrossRef
2.
Lewinsohn PM, Hoberman HM, Rosenbaum M: A prospective study of risk factors for unipolar depression. J Abnorm Psychol. 1988, 97: 251-264.CrossRefPubMed
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. 10.2307/2136667.CrossRefPubMed
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.PubMed
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. 10.1016/j.sapharm.2006.04.001.CrossRefPubMed
6.
Little RJ, Rubin DB: Statistical analysis with missing data. 2002, New York: John Wiley & Sons, 2
7.
8.
Rubin DB: Multiple imputation for non response in surveys. 1987, New York: John Wiley & SonsCrossRef
9.
Diggle P, Kenward MG: Informative drop-out in longitudinal data analysis. Appl Stat. 1994, 43: 49-93. 10.2307/2986113.CrossRef
10.
Beekman AT, Copeland JR, Prince MJ: Review of community prevalence of depression in later life. Br J Psychiatry. 1999, 174: 307-311. 10.1192/bjp.174.4.307.CrossRefPubMed
11.
Djernes JK: Prevalence and predictors of depression in populations of elderly: a review. Acta Psychiatr Scand. 2006, 113: 372-387. 10.1111/j.1600-0447.2006.00770.x.CrossRefPubMed
12.
Lepine JP, Bouchez S: Epidemiology of depression in the elderly. Int Clin Psychopharmacol. 1998, 13 (Suppl 5): S7-S12.CrossRefPubMed
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. 10.1097/00001648-200207000-00012.CrossRefPubMed
14.
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.CrossRefPubMed
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. 10.1017/S0266462300002774.CrossRefPubMed
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. 10.1002/sim.4067.CrossRefPubMed
19.
Resseguier N, Giorgi R, Paoletti X: Sensitivity analysis when data are missing not-at-random. Epidemiology. 2011, 22: 282-10.1097/EDE.0b013e318209dec7.CrossRefPubMed
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. 10.1002/mpr.1348.CrossRefPubMedPubMedCentral
21.
R Development Core Team: R: A language and environment for statistical computing. 2009, Vienna, Austria: R Foundation for Statistical Computing
22.
van Buuren S, Groothuis-Oudshoorn K: R package version 2.3. Mice: Multivariate Imputation by Chained Equations library. 2010
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. 10.1097/00005650-199404000-00003.CrossRefPubMed
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. 10.1017/S0033291799001890.CrossRefPubMed
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. 10.1093/geronb/56.2.P111.CrossRefPubMed
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.CrossRefPubMed
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.CrossRefPubMed
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. 10.2190/LYKR-7VHP-YJEM-MKM2.CrossRefPubMed
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. 10.1177/089826439300500202.CrossRefPubMed
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. 10.2190/BR9Y-J1CL-LM6M-JACJ.CrossRefPubMed
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.PubMed
33.
Schwarz N, Knauper B, Park DC, Sudman S: Cognition, aging and self-reports. 1999, Philadelphia, PA: Psychology PressCrossRef
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. 10.1002/(SICI)1097-0258(20000215)19:3<405::AID-SIM325>3.0.CO;2-2.CrossRefPubMed
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. 10.1002/sim.3476.CrossRefPubMed
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. 10.1097/00001648-199901000-00006.CrossRefPubMed
37.
Molenberghs G, Burzykowski T, Michiels B, Kenward MG: Analysis of incomplete public health data. Rev Epidemiol Sante Publique. 1999, 47: 499-514.PubMed
38.
Schafer JL: Analyis of incomplete multivariate data. 1997, London: Chapman & HallCrossRef
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: 57-10.1186/1471-2288-6-57.CrossRefPubMedPubMedCentral
40.
Moons KG, Donders RA, Stijnen T, Harrel FE: Using the outcome for imputation of missing predictor values was preferred. J Clin Epidemiol. 2006, 59: 1092-1101. 10.1016/j.jclinepi.2006.01.009.CrossRefPubMed
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.CrossRefPubMed
Metadata
Title
Dealing with missing data in the Center for Epidemiologic Studies Depression self-report scale: a study based on the French E3N cohort
Authors
Noémie Resseguier
Hélène Verdoux
Roch Giorgi
Françoise Clavel-Chapelon
Xavier Paoletti
Publication date
01-12-2013
Publisher
BioMed Central
Published in
BMC Medical Research Methodology / Issue 1/2013
Electronic ISSN: 1471-2288
DOI
https://doi.org/10.1186/1471-2288-13-28

Other articles of this Issue 1/2013

BMC Medical Research Methodology 1/2013 Go to the issue

Research article

Reducing and meta-analysing estimates from distributed lag non-linear models

Research article

External validation of a Cox prognostic model: principles and methods

Research article

A systematic review and meta-analysis of acute stroke unit care: What’s beyond the statistical significance?

Research article

Using Bonferroni, BIC and AIC to assess evidence for alternative biological pathways: covariate selection for the multilevel Embryo-Uterus model

Research article

A semi-parametric approach to estimate risk functions associated with multi-dimensional exposure profiles: application to smoking and lung cancer

Research article

Shortening the Current Opioid Misuse Measure via computer-based testing: a retrospective proof-of-concept study