Performance of a Bayesian Approach for Imputing Missing Data on the SF-12 Health-Related Quality-of-Life Measure

Missing data in health-related quality-of-life outcomes are an ongoing problem. The 12-item short form health survey (SF-12) scores are no exception. Data imputation is complicated, because missingness may be partially predicted by the missing data themselves.

To compare the performance of a Bayesian method for imputing SF-12 data with previously described frequentist imputation methods.

SF-12 data were extracted from a trial assessing continence promotion on health-related quality of life in older women (n = 1052); the data set was split into a model development cohort for creating predictive models and a validation cohort to validate these models. Algorithms were constructed using data from the model development cohort to compute SF-12-related scores (physical health composite scale, the mental health composite scale, and the six-dimensional health state short form utilities). The Bayesian models used missing at random and missing not at random algorithms to impute missing SF-12 answers as categorical data. Comparative models replaced missing data with 0, used the mean weight of the sample, and regressed parameters from sociodemographic predictors. Data randomly deleted from the validation cohort were imputed with each algorithm, and the mean absolute error was used to gauge goodness of fit.

Each cohort included 526 persons; mean age was 78.1 ± 7.8 years. In the model development cohort, 15.6% of the participants had missing data. For the physical health composite scale, the mental health composite scale, and the six-dimensional health state short form utilities, the Bayesian model with missing at random data significantly outperformed all five comparison models, including the Bayesian models with missing not at random data.

Bayesian imputation was superior to other previously described methods for computing missing SF-12 data.