A comparison of methods for estimating the temporal change in a continuous variable: Example of HbA1c in patients...

To compare the more complex technique, functional principal component analysis (FPCA), to simpler methods of estimating values of sparse and irregularly spaced continuous variables at given time points in longitudinal data using a diabetic patient cohort from UK primary care.

The setting for this study is the Clinical Practice Research Datalink (CPRD), a UK general practice research database. For 16,034 diabetic patients identified in CPRD, with at least 2 measures in a 30-month period, HbA1c was estimated after temporarily omitting (i) the final and (ii) middle known values using linear interpolation, simple linear regression, arithmetic mean, random effects, and FPCA. Performance of each method was assessed using mean prediction error. The influence on predictive accuracy of (1) more homogeneous populations and (2) number and range of known HbA1c values was explored.

When estimating the last observation, the predictive accuracy of FPCA was highest with over half of predicted values within 0.4 units, equivalent to laboratory measurement error. Predictive accuracy improved when estimating the middle observation with almost 60% predicted values within 0.4 units for FPCA. These results were marginally better than that achieved by simpler approaches, such as last-occurrence-carried-forward linear interpolation. This pattern persisted with more homogeneous populations as well as when variability in HbA1c measures coupled with frequency of data points were considered.

When estimating change from baseline to prespecified time points in electronic medical records data, a marginal benefit to using the more complex modelling approach of FPCA exists over more traditional methods.