title: Estimate variable importance for recurrent event outcomes with an application to identify hypoglycemia risk factors.
abstract: Recurrent event data are an important data type for medical research. In particular, many safety endpoints are recurrent outcomes, such as hypoglycemic events. For such a situation, it is important to identify the factors causing these events and rank these factors by their importance. Traditional model selection methods are not able to provide variable importance in this context. Methods that are able to evaluate 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 that enables us to evaluate the variable importance for recurrent events data. We evaluated the performance of our proposed method by simulations and applied it to a data set from a diabetes study.
authors.affiliation: Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46285, U.S.A.
authors.forename: Ran
authors.initials: R
authors.lastname: Duan
authors.affiliation: Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46285, U.S.A.
authors.forename: Haoda
authors.initials: H
authors.lastname: Fu
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003924:Diabetes Mellitus, Type 2 / Q000150:complications* / Q000188:drug therapy; D006801:Humans; D007003:Hypoglycemia / Q000139:chemically induced / Q000209:etiology*; D007004:Hypoglycemic Agents / Q000009:adverse effects* / Q000627:therapeutic use; D016013:Likelihood Functions; D008875:Middle Aged; D016016:Proportional Hazards Models; D012008:Recurrence; D012307:Risk Factors
chemical_list: D007004:Hypoglycemic Agents
keywords: gradient boosting; martingale residual; random forest; recurrent event data; variable importance; variable selection