TY - JOUR
T1 - BAYESIAN ADJUSTMENT FOR PREFERENTIAL TESTING IN ESTIMATING INFECTION FATALITY RATES, AS MOTIVATED BY THE COVID-19 PANDEMIC
AU - Campbell, Harlan
AU - De Valpine, Perry
AU - Maxwell, Lauren
AU - De Jong, Valentijn M.T.
AU - Debray, Thomas P.A.
AU - Jaenisch, Thomas
AU - Gustafson, Paul
N1 - Funding Information:
Funding. This work was supported by the European Union’s Horizon 2020 research and innovation programme under ReCoDID grant agreement No. 825746 and by the Canadian Institutes of Health Research, Institute of Genetics (CIHR-IG) under Grant Agreement No. 01886-000.
Publisher Copyright:
© Institute of Mathematical Statistics, 2022.
PY - 2022/3
Y1 - 2022/3
N2 - A key challenge in estimating the infection fatality rate (IFR), along with its relation with various factors of interest, is determining the total number of cases. The total number of cases is not known not only because not everyone is tested but also, more importantly, because tested individuals are not representative of the population at large. We refer to the phenomenon whereby infected individuals are more likely to be tested than noninfected individuals as “preferential testing.” An open question is whether or not it is possible to reliably estimate the IFR without any specific knowledge about the degree to which the data are biased by preferential testing. In this paper we take a partial identifiability approach, formulating clearly where deliberate prior assumptions can be made and presenting a Bayesian model which pools information from different samples. When the model is fit to European data obtained from seroprevalence studies and national official COVID-19 statistics, we estimate the overall COVID-19 IFR for Europe to be 0.53%, 95% C.I. =[0.38%, 0.70%].
AB - A key challenge in estimating the infection fatality rate (IFR), along with its relation with various factors of interest, is determining the total number of cases. The total number of cases is not known not only because not everyone is tested but also, more importantly, because tested individuals are not representative of the population at large. We refer to the phenomenon whereby infected individuals are more likely to be tested than noninfected individuals as “preferential testing.” An open question is whether or not it is possible to reliably estimate the IFR without any specific knowledge about the degree to which the data are biased by preferential testing. In this paper we take a partial identifiability approach, formulating clearly where deliberate prior assumptions can be made and presenting a Bayesian model which pools information from different samples. When the model is fit to European data obtained from seroprevalence studies and national official COVID-19 statistics, we estimate the overall COVID-19 IFR for Europe to be 0.53%, 95% C.I. =[0.38%, 0.70%].
KW - evidence synthesis
KW - partial identification
KW - Selection bias
UR - http://www.scopus.com/inward/record.url?scp=85127757543&partnerID=8YFLogxK
U2 - 10.1214/21-AOAS1499
DO - 10.1214/21-AOAS1499
M3 - Article
AN - SCOPUS:85127757543
SN - 1932-6157
VL - 16
SP - 436
EP - 459
JO - Annals of Applied Statistics
JF - Annals of Applied Statistics
IS - 1
ER -