Issues in multiple imputation of missing data for large general practice clinical databases

Abstract

Purpose: Missing data are a substantial problem in clinical databases. This paper aims to examine patterns of missing data in a primary care database, compare this to nationally representative datasets and explore the use of multiple imputation (MI) for these data.

Methods: The patterns and extent of missing health indicators in a UK primary care database (THIN) were quantified using 488 384 patients aged 16 or over in their first year after registration with a GP from 354 General Practices. MI models were developed and the resulting data compared to that from nationally representative datasets (14 142 participants aged 16 or over from the Health Survey for England 2006 (HSE) and 4 252 men from the British Regional Heart Study (BRHS)).

Results: Between 22% (smoking) and 38% (height) of health indicator data were missing in newly registered patients, 2004-2006. Distributions of height, weight and blood pressure were comparable to HSE and BRHS, but alcohol and smoking were not. After MI the percentage of smokers and non-drinkers was higher in THIN than the comparison datasets, while the percentage of ex-smokers and heavy drinkers was lower. Height, weight and blood pressure remained similar to the comparison datasets.

Conclusions: Given available data, the results are consistent with smoking and alcohol data missing not at random whereas height, weight and blood pressure missing at random. Further research is required on suitable imputation methods for smoking and alcohol in such databases.