Abstract
If information on single items in the Short Form–12 health survey (SF-12) is missing, the analysis of only complete cases causes a loss of statistical power and, in case of nonrandom missing data (MD), systematic bias. This study aimed at evaluating the concordance of real patient data and data estimated by different MD imputation procedures in the items of the SF-12 assessment. For this ends, MD were examined in a sample of 1,137 orthopedic patients. Additionally, MD were simulated (a) in the subsample of orthopedic patients exhibiting no MD (n = 810; 71%) as well as (b) in a sample of 6,970 respondents representing the German general population (95.8% participants with complete data) using logistic regression modelling. Simulated MD were replaced by mean values as well as regression-, expectation-maximization- (EM-), and multiple imputation estimates. Higher age and lower education were associated with enhanced probabilities of MD. In terms of accuracy in both data sets, the EM-procedure (ICC2,1 = .33-.72) outperformed alternative estimation approaches substantially (e.g., regression imputation: ICC2,1 = .18-.48). The EM-algorithm can be recommended to estimate MD in the items of the SF-12, because it reproduces the actual patient data most accurately.
Highlights
Measures of patient reported health-related quality of life (HRQoL) are key indicators of patient’s health condition
Because the normative study sample ensured an uncommon elaborate data assessment, the missing data (MD) rates were far below typical rates (Liu et al, 2005; Morfeld et al, 2003; Perneger & Burnand, 2005): 6,943 (99.6%) of the 6,970 participants responded to at least 9 of the 12 items, and less than 1% of the data were missing for each item
As potential limitation of this study, it should be considered that we focused on imputation of single missing item responses, because MD diagnostics revealed no salient MD patterns
Summary
Measures of patient reported health-related quality of life (HRQoL) are key indicators of patient’s health condition. According to World Health Organization (WHO) standards, HRQoL must be considered as a third outcome parameter, in addition to mortality and morbidity, to ensure adequate and comprehensive measurement of patient’s health (WHO, 1995; Fayers & Machin, 2007). Assessment scales measuring patient reported HRQoL, like the Short Form–12 (Ware et al, 2001; Wirtz et al, 2018a), consist of several items. If respondents refuse to provide information on at least one of the scale items, the validity of the findings may be compromised by these missing data (MD) systematically. Biasing effects have to be regarded if MD result from systematic, nonrandom causes (Allison, 2001; Schafer & Graham, 2002)
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