Population-calibrated inference for incomplete continuous variables via weighted multiple imputation
- Principal Investigator: Tra My Pham
- 1 October 2017 to 30 September 2018
- Project No: 379
- Funding round: FR 13
Primary care electronic health records (EHR) are valuable resources in healthcare research, because they provide a large amount of health data that would be difficult or expensive to get through other research methods. In primary care, patient data are collected during routine consultations with healthcare professionals from when they register with a GP practice to when they leave, providing a record of health data over time.
During a patient’s first year at a practice, their past and current medical history is typically recorded; some will have a record of key health indicators such as their height, body weight, blood pressure, smoking and alcohol consumption. Thereafter, this information is primarily recorded if it is directly relevant to the patient's care. Incomplete data in EHR is an obstacle to their use in medical research, which requires complete and accurate data of key health indicators over time.
It is standard in medical research to overcome the problem of incomplete data by using a statistical method called multiple imputation (MI). This method involves using the data collected to estimate the data that are missing, so that analysis can proceed as though complete data had been collected. However, since data in primary care can be incomplete for many reasons, the standard MI method is not suitable for all situations. For example, standard MI is not appropriate for handling missing values in body weight when weight is measured more frequently for underweight or overweight individuals compared to other healthy individuals.
By bringing in population-level information about the incomplete data from external data sources, such as the Health Survey for England, this project aims to develop a more accurate MI method for dealing with incomplete data in EHR, and should therefore improve medical research that relies on such information; better research has the potential to benefit patients.
Amount awarded: £46,029