Testing the trajectory difference in a semi-parametric longitudinal model

Motivated by a genetic investigation on the progressive decline in renal function in a clinical trial study of kidney disease, we develop a practical test for evaluating the group difference in trajectories under a semi-parametric modeling framework. For the temporal patterns or trajectories of long...

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Bibliographic Details
Published in:Statistical methods in medical research 2017-06, Vol.26 (3), p.1519-1531
Main Authors: Niu, Feiyang, Zhou, Jianhui, Le, Thu H, Ma, Jennie Z
Format: Article
Language:English
Subjects:
Adolescent
Adult
Aged
Approximation
Asymptotic properties
Black or African American - genetics
Cardiovascular Diseases - genetics
Clinical research
Clinical trials
Clinical Trials as Topic
Criteria
Disease Progression
Estimation
Female
Glutathione Transferase - genetics
Group dynamics
Humans
Kidney diseases
Kidney Diseases - genetics
Kidney Diseases - physiopathology
Knots
Longitudinal Studies
Male
Mathematical models
Middle Aged
Optimization
Regression Analysis
Regression coefficients
Renal function
Splines
Statistical methods
Statistics, Nonparametric
Temporal patterns
Trajectories
Trajectory analysis
Validity
Young Adult
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Summary:Motivated by a genetic investigation on the progressive decline in renal function in a clinical trial study of kidney disease, we develop a practical test for evaluating the group difference in trajectories under a semi-parametric modeling framework. For the temporal patterns or trajectories of longitudinal data, B-splines are used to approximate the function non-parametrically. Such approximation asymptotically converts the problem of testing trajectory difference into the significance test of regression coefficients that can be simply estimated by generalized estimating equations. To select the optimal number of inner knots for B-splines, a cross-validation procedure is performed using the criterion of the generalized residual sum of squares. The new proposed test successfully detects a significant difference of underlying genetic impact on the progression of renal disease, which is not captured by the parametric approach.
ISSN:0962-2802
1477-0334
DOI:10.1177/0962280215584109