Abstract：
:When a true survival endpoint cannot be assessed for some subjects, an alternative endpoint that measures the true endpoint with error may be collected, which often occurs when obtaining the true endpoint is too invasive or costly. We develop an estimated likelihood function for the situation where we have both uncertain endpoints for all participants and true endpoints for only a subset of participants. We propose a nonparametric maximum estimated likelihood estimator of the discrete survival function of time to the true endpoint. We show that the proposed estimator is consistent and asymptotically normal. We demonstrate through extensive simulations that the proposed estimator has little bias compared to the naïve Kaplan-Meier survival function estimator, which uses only uncertain endpoints, and more efficient with moderate missingness compared to the complete-case Kaplan-Meier survival function estimator, which uses only available true endpoints. Finally, we apply the proposed method to a data set for estimating the risk of detecting Alzheimer's disease from the Alzheimer's Disease Neuroimaging Initiative.
journal_name
Biometricsjournal_title
Biometricsauthors
Zee J,Xie SXdoi
10.1111/biom.12316subject
Has Abstractpub_date
2015-09-01 00:00:00pages
772-81issue
3eissn
0006-341Xissn
1541-0420journal_volume
71pub_type
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