Hypothesis testing in functional linear regression models with Neyman's truncation and wavelet thresholding for longitudinal data

Xiaowei Yang, Kun Nie

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Longitudinal data sets in biomedical research often consist of large numbers of repeated measures. In many cases, the trajectories do not look globally linear or polynomial, making it difficult to summarize the data or test hypotheses using standard longitudinal data analysis based on various linear models. An alternative approach is to apply the approaches of functional data analysis, which directly target the continuous nonlinear curves underlying discretely sampled repeated measures. For the purposes of data exploration, many functional data analysis strategies have been developed based on various schemes of smoothing, but fewer options are available for making causal inferences regarding predictor-outcome relationships, a common task seen in hypothesis-driven medical studies. To compare groups of curves, two testing strategies with good power have been proposed for high-dimensional analysis of variance: the Fourier-based adaptive Neyman test and the wavelet-based thresholding test. Using a smoking cessation clinical trial data set, this paper demonstrates how to extend the strategies for hypothesis testing into the framework of functional linear regression models (FLRMs) with continuous functional responses and categorical or continuous scalar predictors. The analysis procedure consists of three steps: first, apply the Fourier or wavelet transform to the original repeated measures; then fit a multivariate linear model in the transformed domain; and finally, test the regression coefficients using either adaptive Neyman or thresholding statistics. Since a FLRM can be viewed as a natural extension of the traditional multiple linear regression model, the development of this model and computational tools should enhance the capacity of medical statistics for longitudinal data.

Original languageEnglish (US)
Pages (from-to)845-863
Number of pages19
JournalStatistics in Medicine
Volume27
Issue number6
DOIs
StatePublished - Mar 15 2008

Fingerprint

Wavelet Thresholding
Repeated Measures
Longitudinal Data
Hypothesis Testing
Linear Regression Model
Truncation
Functional Data Analysis
Linear Models
Thresholding
Predictors
Multivariate Linear Model
Longitudinal Data Analysis
Statistics
Causal Inference
Curve
Multiple Linear Regression
Functional Response
Smoking
Dimensional Analysis
Hypothesis Test

Keywords

  • Adaptive Neyman test
  • Fourier transform
  • Functional linear regression model
  • Longitudinal data analysis
  • Thresholding test
  • Wavelet transform

ASJC Scopus subject areas

  • Epidemiology

Cite this

Hypothesis testing in functional linear regression models with Neyman's truncation and wavelet thresholding for longitudinal data. / Yang, Xiaowei; Nie, Kun.

In: Statistics in Medicine, Vol. 27, No. 6, 15.03.2008, p. 845-863.

Research output: Contribution to journalArticle

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