Robust Wald‐type tests under random censoring

Randomly censored survival data are frequently encountered in biomedical or reliability applications and clinical trial analyses. Testing the significance of statistical hypotheses is crucial in such analyses to get conclusive inference but the existing likelihood‐based tests, under a fully parametr...

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Bibliographic Details
Published in:	Statistics in medicine Vol. 40; no. 5; pp. 1285 - 1305
Main Authors:	Ghosh, Abhik, Basu, Ayanendranath, Pardo, Leandro
Format:	Journal Article
Language:	English
Published:	England Wiley Subscription Services, Inc 28.02.2021
Subjects:	clinical trial analysis informative censoring minimum density power divergence estimator M‐estimator random censored data robust hypothesis testing robust hypothesis testing minimum density power divergence estimator informative censoring clinical trial analysis random censored data M-estimator
ISSN:	0277-6715, 1097-0258, 1097-0258
Online Access:	Get full text
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Summary:	Randomly censored survival data are frequently encountered in biomedical or reliability applications and clinical trial analyses. Testing the significance of statistical hypotheses is crucial in such analyses to get conclusive inference but the existing likelihood‐based tests, under a fully parametric model, are extremely nonrobust against outliers in the data. Although there exists a few robust estimators given randomly censored data, there is hardly any robust testing procedure available in the literature in this context. One of the major difficulties here is the construction of a suitable consistent estimator of the asymptotic variance of robust estimators, since the latter is a function of the unknown censoring distribution. In this article, we take the first step in this direction by proposing a consistent estimator of asymptotic variance of the M‐estimators based on randomly censored data without any assumption on the censoring scheme. We then describe and study a class of robust Wald‐type tests for parametric statistical hypothesis, both simple as well as composite, under such a set‐up. Robust tests for comparing two independent randomly censored samples and robust tests against one sided alternatives are also discussed. Their advantages and usefulness are demonstrated for the tests based on the minimum density power divergence estimators and illustrated with clinical trials and other medical data.
Bibliography:	Funding information Department of Science and Technology, Government of India, INSPIRE Faculty Research Grant; Ministerio de Ciencia, Innovacion y Universidades, Spain, PGC2018‐ 095194‐B‐I00 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	0277-6715 1097-0258 1097-0258
DOI:	10.1002/sim.8841