About: Identifiability and estimation under the test-negative design with population controls with the goal of identifying risk and preventive factors for SARS-CoV-2 infection

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Identifiability and estimation under the test-negative design with population controls with the goal of identifying risk and preventive factors for SARS-CoV-2 infection Goto Sponge NotDistinct Permalink

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Identifiability and estimation under the test-negative design with population controls with the goal of identifying risk and preventive factors for SARS-CoV-2 infection
Creator	Merckx, Joanna Harel, Daphna Ho, Vikki Koushik, Anita Schnitzer, Mireille
source	ArXiv
abstract	Due to the rapidly evolving COVID-19 pandemic caused by the SARS-CoV-2 virus, quick public health investigations of the relationships between behaviours and infection risk are essential. Recently the test-negative design was proposed to recruit and survey participants who are being tested for SARS-CoV-2 infection in order to evaluate associations between their characteristics and testing positive on the test. It was also proposed to recruit additional untested controls who are part of the general public in order to have a baseline comparison group. This study design involves two major challenges for statistical risk factor analysis: 1) the selection bias invoked by selecting on people being tested and 2) imperfect sensitivity and specificity of the SARS-CoV-2 test. In this study, we investigate the nonparametric identifiability of potential statistical parameters of interest under a hypothetical data structure, expressed through missing data directed acyclic graphs. We clarify the types of data that must be collected in order to correctly estimate the parameter of interest. We then propose a novel inverse probability weighting estimator that can consistently estimate the parameter of interest under correctly specified nuisance models.
has issue date	2020-06-04(xsd:dateTime)
has license	arxiv
sha1sum (hex)	77d79db743161f776ec3db337bde603f9dbb1aab
resource representing a document's title	Identifiability and estimation under the test-negative design with population controls with the goal of identifying risk and preventive factors for SARS-CoV-2 infection
resource representing a document's body	covid:77d79db743161f776ec3db337bde603f9dbb1aab#body_text
is schema:about of	named entity 'STATISTICAL' named entity 'FACTOR ANALYSIS' named entity 'CONTROLS' named entity 'EXPRESSED' named entity 'SARS-CoV-2 virus' »more»

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2025 OpenLink Software