Analyzing excessive no changes in clinical trials with clustered data.
This article considers clinical trials in which the efficacy measure is taken from several sites within each patient, such as the alveolar bone height of the tooth sites, or bone mineral densities of the lumbar spine sites. Since usually only a small portion of these sites will exhibit changes, the conventional method using per patient average gives a diluted result due to excessive no changes in the data. Different methods have been proposed for this type of data in the case where the observations are mutually independent. This includes the popular "two-part model" (Lachenbruch, 2001, Statistics in Medicine 20, 1215-1234; 2002, Statistical Methods in Medical Research 11, 297-302), which is related to the "composite approach" for discrete and continuous data in Shih and Quan (1997, Statistics in Medicine16, 1225-1239; 2001, Statistica Sinica 11, 53-62). In this article, we model the data with excessive zeros (no changes) in clustered data using a mixture of distributions, and taking into account possible measurement errors. This mixture model includes the two-part model as a special case when one component of the mixture degenerates.
Lu
Shou-En
SE
Division of Biometrics, University of Medicine and Dentistry of New Jersey, School of Public Health, New Brunswick, New Jersey 08903, USA.
Lin
Yong
Y
Shih
Wei-Chung Joe
WC
D000465:Algorithms; D016301:Alveolar Bone Loss / Q000378:metabolism / Q000517:prevention & control; D001699:Biometry; D015519:Bone Density; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D006801:Humans; D016014:Linear Models; D015233:Models, Statistical; D009010:Monte Carlo Method; D010510:Periodontal Diseases / Q000378:metabolism / Q000628:therapy