Multiversal Methods in Observational Studies: The Case of COVID-19
50th Scientific Meeting of the Italian Statistical Society, SIS 2021
; 406:369-392, 2022.
Article
in English
| Scopus | ID: covidwho-2284273
ABSTRACT
In the present study, 13 covariates have been selected as potentially associated with 3 metrics of the spread of COVID-19 in 20 European countries. Robustness of the linear correlations between 10 of the 13 covariates as main regressors and the 3 COVID-19 metrics as dependent variables have been tested through a methodology for sensitivity analysis that falls under the name of "Multiverse”. Under this methodology, thousands of alternative estimates are generated by a single hypothesis of regression. The capacity of identification of a robust causal claim for the 10 variables has been measured through 3 indicators over a Janus Confusion Matrix, which is a confusion matrix that assumes the likelihood to observe a True claim as the ratio between the absolute difference of estimates with a different sign and the total of estimates. This methodology provides the opportunity to evaluate the outcomes of a shift from the common level of significance to the alternative. According to the results of the study, in the dataset the benefits of the shifts come at a very high cost in terms of false negatives. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Observational study
/
Prognostic study
Language:
English
Journal:
50th Scientific Meeting of the Italian Statistical Society, SIS 2021
Year:
2022
Document Type:
Article
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