Maximum likelihood estimation for Cox's regression model under nested case-control sampling

Nested case-control sampling is designed to reduce the costs of large cohort studies. It is important to estimate the parameters of interest as efficiently as possible. We present a new maximum likelihood estimator (MLE) for nested case-control sampling in the context of Cox's proportional haza...

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Bibliographic Details
Published in:Biostatistics (Oxford, England) England), 2004-04, Vol.5 (2), p.193-206
Main Authors: Scheike, Thomas H, Juul, Anders
Format: Article
Language:English
Subjects:
Adult
Algorithms
Case-Control Studies
Computer Simulation
Denmark
Female
Humans
Insulin-Like Growth Factor Binding Protein 3 - metabolism
Insulin-Like Growth Factor I - metabolism
Likelihood Functions
Male
Middle Aged
Myocardial Ischemia - etiology
Proportional Hazards Models
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Summary:Nested case-control sampling is designed to reduce the costs of large cohort studies. It is important to estimate the parameters of interest as efficiently as possible. We present a new maximum likelihood estimator (MLE) for nested case-control sampling in the context of Cox's proportional hazards model. The MLE is computed by the EM-algorithm, which is easy to implement in the proportional hazards setting. Standard errors are estimated by a numerical profile likelihood approach based on EM aided differentiation. The work was motivated by a nested case-control study that hypothesized that insulin-like growth factor I was associated with ischemic heart disease. The study was based on a population of 3784 Danes and 231 cases of ischemic heart disease where controls were matched on age and gender. We illustrate the use of the MLE for these data and show how the maximum likelihood framework can be used to obtain information additional to the relative risk estimates of covariates.
ISSN:1465-4644
1468-4357
DOI:10.1093/biostatistics/5.2.193