Nonparametric Growth Curve Model with Local Linear Approximation

The growth curve model has been developed for longitudinal data, and its time trend is usually described by polynomials. However, it is difficult to interpret each coefficient of the polynomials with higher degrees, even when the number of repetitions is sufficiently large. We propose herein an alternative growth curve model having time-varying coefficients.     

A NONPARAMETRIC MIXED‐EFFECTS MODEL FOR CANCER MORTALITY

There are several ways to handle within‐subject correlations with a longitudinal discrete outcome, such as mortality. The most frequently used models are either marginal or random‐effects types. This paper deals with a random‐effects‐based approach. We propose a nonparametric regression model having time‐varying mixed effects for longitudinal cancer mortality data. The time‐varying mixed effects in the proposed model are estimated by combining kernel‐smoothing techniques and a growth‐curve model. As an illustration based on real data, we apply the proposed method to a set of prefecture‐specific data on mortality from large‐bowel cancer in Japan.