About: Non‐parametric estimation of the case fatality ratio with competing risks data: an application to Severe Acute Respiratory Syndrome (SARS)

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About: Non‐parametric estimation of the case fatality ratio with competing risks data: an application to Severe Acute Respiratory Syndrome (SARS) Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Non‐parametric estimation of the case fatality ratio with competing risks data: an application to Severe Acute Respiratory Syndrome (SARS)
Creator	Ho, Lai-Ming Donnelly, Christl Leung, Gabriel Lei, Xiudong Cowling, Benjamin Ghani, Azra Hedley, Anthony Jewell, Nicholas
Source	Medline; PMC
abstract	For diseases with some level of associated mortality, the case fatality ratio measures the proportion of diseased individuals who die from the disease. In principle, it is straightforward to estimate this quantity from individual follow‐up data that provides times from onset to death or recovery. In particular, in a competing risks context, the case fatality ratio is defined by the limiting value of the sub‐distribution function, F (1)(t) = Pr(T ⩽t and J = 1), associated with death, as t → ∞, where T denotes the time from onset to death (J = 1) or recovery (J = 2). When censoring is present, however, estimation of F (1)(∞) is complicated by the possibility of little information regarding the right tail of F (1), requiring use of estimators of F (1)(t()) or F (1)(t ())/(F (1)(t ())+F (2)(t ())) where t () is large, with F (2)(t) = Pr(T ⩽t and J = 2) being the analogous sub‐distribution function associated with recovery. With right censored data, the variability of such estimators increases as t () increases, suggesting the possibility of using estimators at lower values of t (*) where bias may be increased but overall mean squared error be smaller. These issues are investigated here for non‐parametric estimators of F (1) and F (2). The ideas are illustrated on case fatality data for individuals infected with Severe Acute Respiratory Syndrome (SARS) in Hong Kong in 2003. Copyright © 2006 John Wiley & Sons, Ltd.
has issue date	2006-09-15(xsd:dateTime)
bibo:doi	10.1002/sim.2691
bibo:pmid	16981181
has license	no-cc
sha1sum (hex)	a254824499b7ef93a353942450620bdd4b8847b2
schema:url	https://doi.org/10.1002/sim.2691
resource representing a document's title	Non‐parametric estimation of the case fatality ratio with competing risks data: an application to Severe Acute Respiratory Syndrome (SARS)
has PubMed Central identifier	PMC7169492
has PubMed identifier	16981181
schema:publication	Stat Med
resource representing a document's body	covid:a254824499b7ef93a353942450620bdd4b8847b2#body_text
is schema:about of	named entity 'For' named entity 'recovery' named entity 'risks' named entity 'case' named entity 'BUT' named entity 'ASSOCIATED' named entity 'PRESENT' named entity 'RIGHT CENSORED' named entity 'DISTRIBUTION FUNCTION' named entity 'estimators' named entity 'limiting' named entity 'onset' named entity 'application' named entity 'case fatality' named entity 'follow-up' named entity 'SARS' named entity 'case fatality ratio' named entity 'estimators' named entity 'case fatality ratio' named entity 'cofactors' named entity 'Hong Kong' named entity 'non-parametric' named entity 'andF' named entity 'SARS' named entity 'case fatality ratio' named entity 'bootstrap samples' named entity 'multinomial distribution' named entity 'bootstrap samples' named entity 'estimator' named entity 'variance' named entity 'distribution function' named entity 'CFR' named entity 'asymptotic variance' named entity 'CFR' named entity 'asymptotic variance' named entity 'case fatality' named entity 'general population' named entity 'bootstrap' named entity 'bootstrap' named entity 'estimator' named entity 'CFR' named entity 'censoring' named entity 'CFR' named entity 'clinical case definition' named entity 'distribution functions' named entity 'variance' named entity 'epidemic' named entity 'variance' named entity 'asymptotic approximation' named entity 'estimator' named entity 'sample sizes' named entity 'estimator' named entity 'sample size' named entity 'non-parametric' named entity 'estimator' named entity 'CFR' named entity 'estimator' named entity 'variance' named entity 'asymptotic limit' named entity 'probability' named entity 'CFR' named entity 'non-parametric' named entity 'CFR' named entity 'CFR' named entity 'epidemic' named entity 'variance' named entity 'observed data' named entity 'Guangzhou' named entity 'non-parametric' named entity 'variance' named entity 'CFR' named entity 'andF' named entity 'CFR' named entity 'variance'

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