Estimating episode lengths when some observations are probably censored.
This paper analyses a case in censored failure time data problems where some observations are potentially censored. The traditional models for failure time data implicitly assume that the censoring status for each observation is deterministic. Therefore, they cannot be applied directly to the potentially censored data. We propose an estimator that uses resampling techniques to approximate censoring probabilities for individual observations. A Monte Carlo simulation study shows that the proposed estimator properly corrects biases that would otherwise be present had it been assumed that either all potentially censored observations are censored or that no censoring has occurred. Finally, we apply the estimator to a health insurance claims database.
Goodman
Allen C
AC
Department of Economics, Wayne State University, 2074 FAB, Detroit, MI 48202, USA. allen.goodman@wayne.edu
Peng
Yingwei
Y
Hankin
Janet R
JR
Kalist
David E
DE
Spurr
Stephen J
SJ
D017050:Episode of Care; D006801:Humans; D008605:Mental Health Services / Q000706:statistics & numerical data*; D019966:Substance-Related Disorders / Q000534:rehabilitation*; D015996:Survival Rate; D014481:United States