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Inference in a survival cure model with mismeasured covariates using a simulation-extrapolation approach
- Source :
- Biometrika, Biometrika, Vol. 104, no. 1, p. 31-50 (2017)
- Publication Year :
- 2017
- Publisher :
- Oxford University Press (OUP), 2017.
-
Abstract
- In many situations in survival analysis, it may happen that a fraction of individuals will never experience the event of interest: they are considered to be cured. The promotion time cure model takes this into account. We consider the case where one or more explanatory variables in the model are subject to measurement error, which should be taken into account to avoid biased estimators. A general approach is the simulation-extrapolation algorithm, a method based on simulations which allows one to estimate the effect of measurement error on the bias of the estimators and to reduce this bias. We extend this approach to the promotion time cure model. We explain how the algorithm works, we show that the proposed estimator is approximately consistent and asymptotically normally distributed, and that it performs well in finite samples. Finally, we analyze a database in cardiology: among the explanatory variables of interest is the ejection fraction, which is known to be measured with error. ispartof: Biometrika vol:104 issue:1 pages:31-50 ispartof: location:United States status: published
- Subjects :
- Statistics and Probability
General Mathematics
media_common.quotation_subject
Political Science & Public Administration
Inference
030204 cardiovascular system & hematology
01 natural sciences
Semiparametric method
010104 statistics & probability
03 medical and health sciences
Cure fraction
Measurement error
0302 clinical medicine
Promotion (rank)
Statistics
Covariate
Econometrics
Fraction (mathematics)
0101 mathematics
Survival analysis
Mathematics
media_common
Event (probability theory)
Observational error
Applied Mathematics
Promotion time cure model
Estimator
Articles
Agricultural and Biological Sciences (miscellaneous)
3. Good health
Bias correction
Statistics, Probability and Uncertainty
General Agricultural and Biological Sciences
Subjects
Details
- ISSN :
- 14643510 and 00063444
- Database :
- OpenAIRE
- Journal :
- Biometrika
- Accession number :
- edsair.doi.dedup.....0a1b03370dc7d9a97af26eb1cbd4b499