Abstract

With an adaptive partition procedure, we can partition a “time course” into consecutive non-overlapped intervals such that the population means/proportions of the observations in two adjacent intervals are significantly different at a given level . However, the widely used recursive combination or partition procedures do not guarantee a global optimization. We propose a modified dynamic programming algorithm to achieve a global optimization. Our method can provide consistent estimation results. In a comprehensive simulation study, our method shows an improved performance when it is compared to the recursive combination/partition procedures. In practice, can be determined based on a cross-validation procedure. As an application, we consider the well-known Pima Indian Diabetes data. We explore the relationship among the diabetes risk and several important variables including the plasma glucose concentration, body mass index and age.

Highlights

  • Time-course data analysis can be common in biomedical studies

  • We explore the relationship among the diabetes risk and several important variables including the plasma glucose concentration, body mass index (BMI) and age

  • Since we know the true parameters for simulations, we choose the aC value that minimizes the overall mean squared error (MSE)

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Summary

Introduction

Time-course data analysis can be common in biomedical studies. A ‘‘time course’’ is not necessarily only a certain period of time in the study. It can be patients’ age records or biomarkers’ chromosomal locations. A general ‘‘time’’ variable can be a predictor with continuous or ordinal values. When we analyze a response variable (binary, continuous, etc.), it is usually necessary to incorporate the information from this predictor. We focus on a nonparametric type of analysis of time-course data: the whole time course is partitioned into consecutive non-overlapped intervals such that the response observations are similar in the same block but different in adjacent blocks. The partition of time course is the detection of change-points. For the detection of multiple change-points, since there are many unknown parameters like the number of change-points, the locations of change-points and the population means/ proportions in each block, it still remains a difficult problem [2]

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