國立交通大學、清華大學
統計學研究所
專題演講
題 目：Modeling Zero-Inflated Count Data with B-Splines and Its Applications
主講人：李金上博士
(Dept. of Biostatistics, St. Jude Children's Research Hospital)
時 間：96年6月8日(星期五)上午10:40-11:30
(上午10:20-10:40茶會於交大統計所428室舉行)
地 點：交大綜合一館427室
Abstract
Analyzing counts of rare events often involves datasets that contain many
zeros. For example, patients with congestive heart failure who take their
medications regularly may not require hospital admissions or emergency
department visits; therefore, analyzing these events can be challenging.
Counts of rare events are often modeled by zero-inflated Poisson (ZIP)
distributions. In the regression analysis of ZIP data, the effect of an
independent variable of interest is usually modeled via a linear predictor,
which imposes a restrictive, thus potentially questionable, functional form
on the relation between the independent and dependent variables. I propose a
flexible parametric procedure to model the effect of the independent variable
as a linear combination of fixed knot cubic B-splines. I fit the proposed
semiparametric ZIP regression model by maximizing the penalized likelihood
through an expectation-maximization algorithm; hence this approach yields a
smooth estimate of the functional form of the independent variable effect.
I contend that this semiparametric regression model greatly enhances modeling
flexibility. By introducing a likelihood ratio test, my research also
provides a practical way to assess the functional adequacy of the independent
variable effect. This research is a useful addition to the existing ZIP
literature. Results from a simulation study show that the proposed test has
excellent power in detecting the lack-of-fit of a linear predictor. Finally,
I illustrate the practical use of this method by modeling the counts of
hospital or emergency department visits in a real clinical trial. In this
application, 74 of the 92 patients with congestive heart failure did not have
a hospital or emergency department visit during the 1-year follow-up period.
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