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Methodological issues of clinical prediction models for shoulder pain in general practice

Objectives: When designing prediction models by complete case analysis (CCA), missing information in either baseline (predictors) or outcomes may lead to biased results. Multiple imputation (MI) has been shown to be suitable for obtaining unbiased results. This study provides researchers with an empirical illustration of the use of MI in a dataset on low back pain (LBP), by comparing MI with the more commonly used CCA. Effects will be shown of imputing missing information on the composition and performance of prognostic models, distinguishing imputation of missing values in baseline characteristics and outcome data. Methods: Data came from the Beliefs about Backpain (BeBack) cohort, a study of psychological obstacles to recovery in primary care back pain patients in the United Kingdom (UK). Candidate predictors included demographics, back pain characteristics and psychological variables. CCA was compared with MI within patients with complete outcome but missing baseline data (N=809) and patients with missing baseline or outcome data (N=1591). MI was performed by a Multiple Imputation by Chained Equations (MICE) procedure. Results: Cases with missing outcome data (n=782, 49.1%) or with missing baseline data (n=116, 8%) both differed from complete cases regarding the distribution of some predictors and more often had a poor outcome. When comparing CCA with MI, model composition showed to be affected. Conclusion: CCA can give biased results, even when only small amounts of data are missing. Now that MI is available in standard statistical software, we recommend it be used to handle missing data.