Thumbnail
Access Restriction
Open

Author Hua, Lei ♦ Zhang, Ying
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Abstract We propose to analyze panel count data using a spline-based semiparametric projected generalized estimating equation method with the semiparametric proportional mean model E(N(t) Z) = Λ0 (t) e βT 0 Z. The natural logarithm of the baseline mean function, log Λ0 (t), is approximated by monotone cubic B-spline functions. The estimates of regression parameters and spline coefficients are obtained by projecting the generalized estimating equation estimates into the feasible domain using a weighted isotonic regression. The proposed method avoids assuming any parametric structure of the baseline mean function or the underlying counting process. Selection of the working-covariance matrix that represents the true corre-1 lation between the cumulative counts improves the estimating efficiency. Simulation studies are conducted to investigate finite sample performance of the proposed method and to compare the estimating efficiency using different working-covariance matrices in the generalized estimating equation. Finally, the proposed method is applied to a real dataset from a bladder tumor clinical trial.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study