New approach and new permutation tests with R programs for analyses of false-negative-contaminated data in medicine and biology

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F20%3A10412175" target="_blank" >RIV/00216208:11310/20:10412175 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00023752:_____/20:43920460

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=uMUbYJ8bzs" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=uMUbYJ8bzs</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1242/bio.045948" target="_blank" >10.1242/bio.045948</a>

Jazyk výsledku

angličtina

Název v původním jazyce

New approach and new permutation tests with R programs for analyses of false-negative-contaminated data in medicine and biology

Popis výsledku v původním jazyce

Statistically, the concentration of antibodies against parasites decreases with the duration of infection. This can result in false-negative outcomes of diagnostic tests for subjects with old infections. When a property of seronegative and seropositive subjects is compared under these circumstances, the statistical tests can detect no difference between these two groups of subjects, despite the fact that they differ. When the effect of the infection has a cumulative character and subjects with older infections are affected to a greater degree, we may even get paradoxical results of the comparison - the seropositive subjects have, on average, a higher value of certain traits despite the infection having a negative effect on those traits. A permutation test for the contaminated data implemented, e.g. in the program Treept or available as a comprehensibly commented R function at https://github.com/ costlysignalling/Permutation test for contaminated data, can be used to reveal and to eliminate the effect of false negatives. A Monte Carlo simulation in the program R showed that our permutation test is a conservative test - it could provide false negative, but not false positive, results if the studied population contains no false-negative subjects. A new R version of the test was expanded by skewness analysis, which helps to estimate the proportion of false-negative subjects based on the assumption of equal data skewness in groups of healthy and infected subjects. Based on the results of simulations and our experience with empirical studies we recommend the usage of a permutation test for contaminated data whenever seronegative and seropositive individuals are compared.

Název v anglickém jazyce

New approach and new permutation tests with R programs for analyses of false-negative-contaminated data in medicine and biology

Popis výsledku anglicky

Statistically, the concentration of antibodies against parasites decreases with the duration of infection. This can result in false-negative outcomes of diagnostic tests for subjects with old infections. When a property of seronegative and seropositive subjects is compared under these circumstances, the statistical tests can detect no difference between these two groups of subjects, despite the fact that they differ. When the effect of the infection has a cumulative character and subjects with older infections are affected to a greater degree, we may even get paradoxical results of the comparison - the seropositive subjects have, on average, a higher value of certain traits despite the infection having a negative effect on those traits. A permutation test for the contaminated data implemented, e.g. in the program Treept or available as a comprehensibly commented R function at https://github.com/ costlysignalling/Permutation test for contaminated data, can be used to reveal and to eliminate the effect of false negatives. A Monte Carlo simulation in the program R showed that our permutation test is a conservative test - it could provide false negative, but not false positive, results if the studied population contains no false-negative subjects. A new R version of the test was expanded by skewness analysis, which helps to estimate the proportion of false-negative subjects based on the assumption of equal data skewness in groups of healthy and infected subjects. Based on the results of simulations and our experience with empirical studies we recommend the usage of a permutation test for contaminated data whenever seronegative and seropositive individuals are compared.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10602 - Biology (theoretical, mathematical, thermal, cryobiology, biological rhythm), Evolutionary biology

Projekt

<a href="/cs/project/GA18-13692S" target="_blank" >GA18-13692S: Udržování Rh polymorfismu v populaci moderního člověka selekcí ve prospěch heterozygotů – vliv genotypu na fertilitu a viabilitu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Biology Open [online]

ISSN

2046-6390

e-ISSN

—

Svazek periodika

9

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

6

Strana od-do

bio045948

Kód UT WoS článku

000510860300004

EID výsledku v databázi Scopus

2-s2.0-85089875568