Analysis of clustered and interval censored data from a community-based study in asthma.
Many authors in recent years have proposed extensions of familiar survival analysis methodologies to apply in dependent data settings, for example, when data are clustered or subject to repeated measures. However, these extensions have been considered largely in the context of right censored data. In this paper, we discuss a parametric frailty model for the analysis of clustered and interval censored failure time data. Details are presented for the specific case where the underlying time to event data follow a Weibull distribution. Maximum likelihood estimates will be obtained using commercially available software and the empirical efficiency of these estimators will be explored via a simulation study. We also discuss a score test to make inferences about the magnitude and significance of over-dispersion in clustered data settings. These methods will be illustrated using data from the East Boston Asthma Study.
Bellamy
Scarlett L
SL
Department of Biostatistics and Epidemiology, Center for Clinical Epidemiology and Biostatistics, 629 Blockley Hall/423 Guardian Drive, Philadelphia, PA 19103, USA. sbellamy@cceb.upenn.edu
Li
Yi
Y
Ryan
Louise M
LM
Lipsitz
Stuart
S
Canner
Marina J
MJ
Wright
Rosalind
R
D001249:Asthma / Q000453:epidemiology / Q000209:etiology*; D001699:Biometry / Q000379:methods*; D001900:Boston / Q000453:epidemiology; D002648:Child; D002675:Child, Preschool; D016000:Cluster Analysis; D003625:Data Collection; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D007223:Infant; D016013:Likelihood Functions; D008137:Longitudinal Studies; D008297:Male; D015233:Models, Statistical; D012141:Respiratory Tract Infections / Q000150:complications / Q000453:epidemiology; D012307:Risk Factors; D016019:Survival Analysis