title: A comparison of different statistical methods analyzing hypoglycemia data using bootstrap simulations.
abstract: Hypoglycemia has long been recognized as a major barrier to achieving normoglycemia with intensive diabetic therapies. It is a common safety concern for the diabetes patients. Therefore, it is important to apply appropriate statistical methods when analyzing hypoglycemia data. Here, we carried out bootstrap simulations to investigate the performance of the four commonly used statistical models (Poisson, negative binomial, analysis of covariance [ANCOVA], and rank ANCOVA) based on the data from a diabetes clinical trial. Zero-inflated Poisson (ZIP) model and zero-inflated negative binomial (ZINB) model were also evaluated. Simulation results showed that Poisson model inflated type I error, while negative binomial model was overly conservative. However, after adjusting for dispersion, both Poisson and negative binomial models yielded slightly inflated type I errors, which were close to the nominal level and reasonable power. Reasonable control of type I error was associated with ANCOVA model. Rank ANCOVA model was associated with the greatest power and with reasonable control of type I error. Inflated type I error was observed with ZIP and ZINB models.
authors.affiliation: a Department of Global Statistical Sciences , Eli Lilly and Company , Indianapolis , Indiana , USA.
authors.forename: Honghua
authors.initials: H
authors.lastname: Jiang
authors.forename: Xiao
authors.initials: X
authors.lastname: Ni
authors.forename: William
authors.initials: W
authors.lastname: Huster
authors.forename: Cory
authors.initials: C
authors.lastname: Heilmann
mesh_terms: D000704:Analysis of Variance; D016010:Binomial Distribution; D015415:Biomarkers / Q000097:blood; D001786:Blood Glucose / Q000187:drug effects* / Q000378:metabolism; D003198:Computer Simulation; D003920:Diabetes Mellitus / Q000097:blood / Q000175:diagnosis / Q000188:drug therapy*; D006801:Humans; D007003:Hypoglycemia / Q000097:blood / Q000139:chemically induced* / Q000175:diagnosis; D007004:Hypoglycemic Agents / Q000009:adverse effects*; D000069036:Insulin Glargine; D061268:Insulin Lispro / Q000009:adverse effects; D049528:Insulin, Long-Acting / Q000009:adverse effects; D015233:Models, Statistical; D016012:Poisson Distribution; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D015203:Reproducibility of Results; D018570:Risk Assessment; D013997:Time Factors; D016896:Treatment Outcome
chemical_list: D015415:Biomarkers; D001786:Blood Glucose; D007004:Hypoglycemic Agents; D061268:Insulin Lispro; D049528:Insulin, Long-Acting; D000069036:Insulin Glargine
keywords: Bootstrap; Power; Type I error