Nonparametric Estimation of the Survival Function When Cause of Death is Uncertain
| Autor: | Amy H. Racine-Poon, David G. Hoel |
|---|---|
| Rok vydání: | 1984 |
| Předmět: |
Statistics and Probability
Bayes estimator General Immunology and Microbiology Applied Mathematics Estimator General Medicine General Biochemistry Genetics and Molecular Biology Minimum-variance unbiased estimator Efficient estimator Survival function Nelson–Aalen estimator Statistics Consistent estimator Stein's unbiased risk estimate Econometrics General Agricultural and Biological Sciences Mathematics |
| Zdroj: | Biometrics. 40:1151 |
| ISSN: | 0006-341X |
| Popis: | SUMMARY A nonparametric estimator for the survival function, accommodating censored survival times and uncertainty in the assignment of cause of death, is proposed. For example, in a carcinogenicity experiment the data on each animal may consist of an observed age-at-death and some indication of the probability that the tumor type under study caused death. An estimator of the net survival function, for time-to-death due to the cause of interest, is developed. Under certain assumptions, the proposed estimator is consistent and asymptotically normally distributed. Monte Carlo simulations were used to compare this estimator with the Kaplan-Meier estimator. Forcing the cause of death to be specified with certainty, as required by the Kaplan-Meier estimator, may result in substantial biases. |
| Databáze: | OpenAIRE |
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