Dealing with missing data in a multi-question depression scale: a comparison of imputation methods

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...

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Vydáno v:BMC medical research methodology Ročník 6; číslo 1; s. 57
Hlavní autoři: Shrive, Fiona M, Stuart, Heather, Quan, Hude, Ghali, William A
Médium: Journal Article
Jazyk:angličtina
Vydáno: England BioMed Central 13.12.2006
BMC
Témata:
Alberta
Attitude to Health
Data Interpretation, Statistical
Depression - classification
Depression - diagnosis
Female
Hospitals, Teaching
Humans
Male
Outcome Assessment (Health Care) - methods
Outcome Assessment (Health Care) - statistics & numerical data
Preoperative Care
Psychiatric Status Rating Scales
Psychometrics - methods
Regression Analysis
Reproducibility of Results
Research Design
Self-Assessment
Surgical Procedures, Operative - psychology
Surveys and Questionnaires
Alberta
ISSN:1471-2288, 1471-2288
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Shrnutí: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). 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. 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). 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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:1471-2288
1471-2288
DOI:10.1186/1471-2288-6-57