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Dealing with missing data in a multi-question depression scale: a comparison of imputation methods.
BMC Med Res Methodol. 2006 Dec 13; 6:57.BM

Abstract

BACKGROUND

Missing data present a challenge to many research projects. The problem is often pronounced in studies utilizing self-report scales, and literature addressing different strategies for dealing with missing data in such circumstances is scarce. The objective of this study was to compare six different imputation techniques for dealing with missing data in the Zung Self-reported Depression scale (SDS).

METHODS

1580 participants from a surgical outcomes study completed the SDS. The SDS is a 20 question scale that respondents complete by circling a value of 1 to 4 for each question. The sum of the responses is calculated and respondents are classified as exhibiting depressive symptoms when their total score is over 40. Missing values were simulated by randomly selecting questions whose values were then deleted (a missing completely at random simulation). Additionally, a missing at random and missing not at random simulation were completed. Six imputation methods were then considered; 1) multiple imputation, 2) single regression, 3) individual mean, 4) overall mean, 5) participant's preceding response, and 6) random selection of a value from 1 to 4. For each method, the imputed mean SDS score and standard deviation were compared to the population statistics. The Spearman correlation coefficient, percent misclassified and the Kappa statistic were also calculated.

RESULTS

When 10% of values are missing, all the imputation methods except random selection produce Kappa statistics greater than 0.80 indicating 'near perfect' agreement. MI produces the most valid imputed values with a high Kappa statistic (0.89), although both single regression and individual mean imputation also produced favorable results. As the percent of missing information increased to 30%, or when unbalanced missing data were introduced, MI maintained a high Kappa statistic. The individual mean and single regression method produced Kappas in the 'substantial agreement' range (0.76 and 0.74 respectively).

CONCLUSION

Multiple imputation is the most accurate method for dealing with missing data in most of the missind data scenarios we assessed for the SDS. Imputing the individual's mean is also an appropriate and simple method for dealing with missing data that may be more interpretable to the majority of medical readers. Researchers should consider conducting methodological assessments such as this one when confronted with missing data. The optimal method should balance validity, ease of interpretability for readers, and analysis expertise of the research team.

Authors+Show Affiliations

Department of Community Health Sciences, Faculty of Medicine, University of Calgary, Alberta, Canada. fmshrive@ucalgary.ca <fmshrive@ucalgary.ca>No affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

17166270

Citation

Shrive, Fiona M., et al. "Dealing With Missing Data in a Multi-question Depression Scale: a Comparison of Imputation Methods." BMC Medical Research Methodology, vol. 6, 2006, p. 57.
Shrive FM, Stuart H, Quan H, et al. Dealing with missing data in a multi-question depression scale: a comparison of imputation methods. BMC Med Res Methodol. 2006;6:57.
Shrive, F. M., Stuart, H., Quan, H., & Ghali, W. A. (2006). Dealing with missing data in a multi-question depression scale: a comparison of imputation methods. BMC Medical Research Methodology, 6, 57.
Shrive FM, et al. Dealing With Missing Data in a Multi-question Depression Scale: a Comparison of Imputation Methods. BMC Med Res Methodol. 2006 Dec 13;6:57. PubMed PMID: 17166270.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Dealing with missing data in a multi-question depression scale: a comparison of imputation methods. AU - Shrive,Fiona M, AU - Stuart,Heather, AU - Quan,Hude, AU - Ghali,William A, Y1 - 2006/12/13/ PY - 2006/08/01/received PY - 2006/12/13/accepted PY - 2006/12/15/pubmed PY - 2007/5/10/medline PY - 2006/12/15/entrez SP - 57 EP - 57 JF - BMC medical research methodology JO - BMC Med Res Methodol VL - 6 N2 - BACKGROUND: Missing data present a challenge to many research projects. The problem is often pronounced in studies utilizing self-report scales, and literature addressing different strategies for dealing with missing data in such circumstances is scarce. The objective of this study was to compare six different imputation techniques for dealing with missing data in the Zung Self-reported Depression scale (SDS). METHODS: 1580 participants from a surgical outcomes study completed the SDS. The SDS is a 20 question scale that respondents complete by circling a value of 1 to 4 for each question. The sum of the responses is calculated and respondents are classified as exhibiting depressive symptoms when their total score is over 40. Missing values were simulated by randomly selecting questions whose values were then deleted (a missing completely at random simulation). Additionally, a missing at random and missing not at random simulation were completed. Six imputation methods were then considered; 1) multiple imputation, 2) single regression, 3) individual mean, 4) overall mean, 5) participant's preceding response, and 6) random selection of a value from 1 to 4. For each method, the imputed mean SDS score and standard deviation were compared to the population statistics. The Spearman correlation coefficient, percent misclassified and the Kappa statistic were also calculated. RESULTS: When 10% of values are missing, all the imputation methods except random selection produce Kappa statistics greater than 0.80 indicating 'near perfect' agreement. MI produces the most valid imputed values with a high Kappa statistic (0.89), although both single regression and individual mean imputation also produced favorable results. As the percent of missing information increased to 30%, or when unbalanced missing data were introduced, MI maintained a high Kappa statistic. The individual mean and single regression method produced Kappas in the 'substantial agreement' range (0.76 and 0.74 respectively). CONCLUSION: Multiple imputation is the most accurate method for dealing with missing data in most of the missind data scenarios we assessed for the SDS. Imputing the individual's mean is also an appropriate and simple method for dealing with missing data that may be more interpretable to the majority of medical readers. Researchers should consider conducting methodological assessments such as this one when confronted with missing data. The optimal method should balance validity, ease of interpretability for readers, and analysis expertise of the research team. SN - 1471-2288 UR - https://www.unboundmedicine.com/medline/citation/17166270/Dealing_with_missing_data_in_a_multi_question_depression_scale:_a_comparison_of_imputation_methods_ DB - PRIME DP - Unbound Medicine ER -