title: Weighted hurdle regression method for joint modeling of cardiovascular events likelihood and rate in the US dialysis population.
abstract: We propose a new weighted hurdle regression method for modeling count data, with particular interest in modeling cardiovascular events in patients on dialysis. Cardiovascular disease remains one of the leading causes of hospitalization and death in this population. Our aim is to jointly model the relationship/association between covariates and (i) the probability of cardiovascular events, a binary process, and (ii) the rate of events once the realization is positive-when the 'hurdle' is crossed-using a zero-truncated Poisson distribution. When the observation period or follow-up time, from the start of dialysis, varies among individuals, the estimated probability of positive cardiovascular events during the study period will be biased. Furthermore, when the model contains covariates, then the estimated relationship between the covariates and the probability of cardiovascular events will also be biased. These challenges are addressed with the proposed weighted hurdle regression method. Estimation for the weighted hurdle regression model is a weighted likelihood approach, where standard maximum likelihood estimation can be utilized. The method is illustrated with data from the United States Renal Data System. Simulation studies show the ability of proposed method to successfully adjust for differential follow-up times and incorporate the effects of covariates in the weighting.
authors.affiliation: Department of Biostatistics, University of California, Los Angeles, CA 90095, U.S.A.
authors.forename: Damla
authors.initials: D
authors.lastname: Sentürk
authors.forename: Lorien S
authors.initials: LS
authors.lastname: Dalrymple
authors.forename: Yi
authors.initials: Y
authors.lastname: Mu
authors.forename: Danh V
authors.initials: DV
authors.lastname: Nguyen
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015982:Bias; D002318:Cardiovascular Diseases / Q000209:etiology*; D015331:Cohort Studies; D003198:Computer Simulation; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D008875:Middle Aged; D006435:Renal Dialysis / Q000009:adverse effects*; D014481:United States
keywords: Poisson regression; United States Renal Data System; cardiovascular outcomes; dialysis; end-stage renal disease; hurdle model; infection