Statistical data harmonization for neuropsychological test battery conversion

Abstract Background A current problem scientists and clinicians face when studying cognitive impairment is how to harmonize scores across neuropsychological tests evaluating the same cognitive domains in a longitudinal analysis. Prior point conversion methods typically do not allow quantification of uncertainty and may be based on strong distributional assumptions. Our framework was motivated by an interest in assessing the uncertainty when converting scores. Method We established new statistical methodology for converting C1 and C2 neuropsychological batteries of the National Alzheimer’s Coordinating Center Uniform Data Set collected by the NIA Alzheimer’s Disease Research Centers Program. We adapted methodology from the measurement error and latent variable statistical modeling techniques and proposed an algorithm to give point and interval conversions. We developed parametric and non‐parametric models to achieve this task, allowing one to control for important traits such as age, education level and sex at birth. Additionally, we developed variational inference and functional Expectation Maximization algorithms to fit these models and implemented them in R. This method was compared to existing methodology using Z‐score conversions or quantile matching. Result We assessed our model by converting C1 scores (e.g. Mini‐Mental State Examination) to C2 scores (e.g. Montreal Cognitive Assessment). We trained our method on 18092 cognitively normal individuals with either a C1 or C2 score observed. The method was validated on a set 420 individuals with both scores observed using the Hellinger and Total Variation probability metrics to compare conversion distributions. Both methods outperformed prior methods in describing intrinsic variability (Table 1), which is the difference of scores from subsequent visits. Additionally, the non‐parametric model outperformed the others in matching the extrinsic variability, which is the difference of scores for individuals with multiple same day tests used to verify a model is capturing the conversion uncertainty (Table 1; Figures 1,2,3). Conclusion Our framework provides a statistically grounded approach to convert scores with uncertainty, a significant step towards understanding cognitive progression in settings where different neuropsychological batteries are used over time. This work establishes groundwork for future study including detection of Mild Cognitive Impairment when multiple testing batteries are used during an individual’s lifetime.

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