On Data Enriched Logistic Regression

Understand

Biomedical researchers usually study the effects of certain exposures on disease risks among a well-defined population.

• To achieve this goal, the gold standard is to design a trial with an appropriate sample from that population.
• Due to the high cost of such trials, usually the sample size collected is limited and is not enough to accurately estimate some exposures' effect.
• In this paper, we discuss how to leverage the information from external `big data' (data with much larger sample size) to improve the estimation accuracy at the risk of introducing small bias.