Identifying risk factors associate with hypoglycemic events

Episodes of hypoglycemia occurred over the study period and is one of the most noticeable adverse events in diabetes care. It is important to identify the factors causing hypoglycemic events and rank these factors by their importance. Most research works only use the time of first hypoglycemia onset and treat it as time to event endpoint due to the limitation of methodology. Traditional model selection methods are not able to provide variable importance in this context. Methods that are able to provide the variable importance, such as gradient boosting and random forest algorithms, cannot directly be applied to recurrent events data. In this paper, we propose a two-step method to identify risk factors that are associate with hypoglycemia. In general, this method allows us to evaluate the variable importance for recurrent events data. The performance of our proposed method are evaluated through intensive simulation studies.