Nonparametric discrete survival function estimation with uncertain endpoints using an internal validation subsample

Jarcy Zee and Sharon X. Xie

Biometrics, 2015, vol. 71, issue 3, 772-781

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.

Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1111/biom.12316

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:biomet:v:71:y:2015:i:3:p:772-781

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0006-341X

Access Statistics for this article

More articles in Biometrics from The International Biometric Society
Bibliographic data for series maintained by Wiley Content Delivery ().