Matches in SemOpenAlex for { <https://semopenalex.org/work/W4317567029> ?p ?o ?g. }
- W4317567029 endingPage "794" @default.
- W4317567029 startingPage "777" @default.
- W4317567029 abstract "This tutorial shows how to perform a meta-analysis of diagnostic test accuracy studies (DTA) based on a 2 × 2 table available for each included primary study. First, univariate methods for meta-analysis of sensitivity and specificity are presented. Then the use of univariate logistic regression models with and without random effects for e.g. sensitivity is described. Diagnostic odds ratios (DOR) are then introduced to combine sensitivity and specificity into one single measure and to assess publication bias. Finally, bivariate random effects models using the exact binomial likelihood to describe within-study variability and a normal distribution to describe between-study variability are presented as the method of choice. Based on this model summary receiver operating characteristic (sROC) curves are constructed using a regression model logit-true positive rate (TPR) over logit-false positive rate (FPR). Also it is demonstrated how to perform the necessary calculations with the freely available software R. As an example a meta-analysis of DTA studies using Procalcitonin as a diagnostic marker for sepsis is presented." @default.
- W4317567029 created "2023-01-21" @default.
- W4317567029 creator A5085406499 @default.
- W4317567029 date "2023-01-19" @default.
- W4317567029 modified "2023-09-28" @default.
- W4317567029 title "Tutorial: statistical methods for the meta-analysis of diagnostic test accuracy studies" @default.
- W4317567029 cites W1511524925 @default.
- W4317567029 cites W1644997609 @default.
- W4317567029 cites W1909101599 @default.
- W4317567029 cites W1963657009 @default.
- W4317567029 cites W1968038365 @default.
- W4317567029 cites W1973914781 @default.
- W4317567029 cites W2001855995 @default.
- W4317567029 cites W2017988559 @default.
- W4317567029 cites W2020488794 @default.
- W4317567029 cites W2032077324 @default.
- W4317567029 cites W2036270909 @default.
- W4317567029 cites W2041290881 @default.
- W4317567029 cites W2046205453 @default.
- W4317567029 cites W2066569461 @default.
- W4317567029 cites W2077165978 @default.
- W4317567029 cites W2087501593 @default.
- W4317567029 cites W2089969052 @default.
- W4317567029 cites W2092926517 @default.
- W4317567029 cites W2096014593 @default.
- W4317567029 cites W2102187963 @default.
- W4317567029 cites W2103961250 @default.
- W4317567029 cites W2110093215 @default.
- W4317567029 cites W2124210282 @default.
- W4317567029 cites W2126602143 @default.
- W4317567029 cites W2126727011 @default.
- W4317567029 cites W2128076015 @default.
- W4317567029 cites W2129343425 @default.
- W4317567029 cites W2144589352 @default.
- W4317567029 cites W2155132414 @default.
- W4317567029 cites W2162445884 @default.
- W4317567029 cites W2170952317 @default.
- W4317567029 cites W2226415896 @default.
- W4317567029 cites W2947339359 @default.
- W4317567029 cites W2949333168 @default.
- W4317567029 cites W2975424845 @default.
- W4317567029 cites W3049565965 @default.
- W4317567029 cites W3089342391 @default.
- W4317567029 cites W4210662951 @default.
- W4317567029 cites W4220899089 @default.
- W4317567029 cites W4220917875 @default.
- W4317567029 cites W4225773359 @default.
- W4317567029 cites W4251211582 @default.
- W4317567029 cites W4280513075 @default.
- W4317567029 cites W4280535913 @default.
- W4317567029 doi "https://doi.org/10.1515/cclm-2022-1256" @default.
- W4317567029 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36656998" @default.
- W4317567029 hasPublicationYear "2023" @default.
- W4317567029 type Work @default.
- W4317567029 citedByCount "2" @default.
- W4317567029 countsByYear W43175670292023 @default.
- W4317567029 crossrefType "journal-article" @default.
- W4317567029 hasAuthorship W4317567029A5085406499 @default.
- W4317567029 hasBestOaLocation W43175670291 @default.
- W4317567029 hasConcept C105795698 @default.
- W4317567029 hasConcept C126322002 @default.
- W4317567029 hasConcept C127413603 @default.
- W4317567029 hasConcept C140331021 @default.
- W4317567029 hasConcept C149782125 @default.
- W4317567029 hasConcept C151956035 @default.
- W4317567029 hasConcept C154606282 @default.
- W4317567029 hasConcept C157481446 @default.
- W4317567029 hasConcept C161584116 @default.
- W4317567029 hasConcept C199163554 @default.
- W4317567029 hasConcept C21200559 @default.
- W4317567029 hasConcept C24326235 @default.
- W4317567029 hasConcept C33923547 @default.
- W4317567029 hasConcept C41008148 @default.
- W4317567029 hasConcept C44249647 @default.
- W4317567029 hasConcept C58471807 @default.
- W4317567029 hasConcept C64341305 @default.
- W4317567029 hasConcept C71924100 @default.
- W4317567029 hasConcept C95190672 @default.
- W4317567029 hasConcept C95922358 @default.
- W4317567029 hasConceptScore W4317567029C105795698 @default.
- W4317567029 hasConceptScore W4317567029C126322002 @default.
- W4317567029 hasConceptScore W4317567029C127413603 @default.
- W4317567029 hasConceptScore W4317567029C140331021 @default.
- W4317567029 hasConceptScore W4317567029C149782125 @default.
- W4317567029 hasConceptScore W4317567029C151956035 @default.
- W4317567029 hasConceptScore W4317567029C154606282 @default.
- W4317567029 hasConceptScore W4317567029C157481446 @default.
- W4317567029 hasConceptScore W4317567029C161584116 @default.
- W4317567029 hasConceptScore W4317567029C199163554 @default.
- W4317567029 hasConceptScore W4317567029C21200559 @default.
- W4317567029 hasConceptScore W4317567029C24326235 @default.
- W4317567029 hasConceptScore W4317567029C33923547 @default.
- W4317567029 hasConceptScore W4317567029C41008148 @default.
- W4317567029 hasConceptScore W4317567029C44249647 @default.
- W4317567029 hasConceptScore W4317567029C58471807 @default.
- W4317567029 hasConceptScore W4317567029C64341305 @default.
- W4317567029 hasConceptScore W4317567029C71924100 @default.
- W4317567029 hasConceptScore W4317567029C95190672 @default.