Pseudo-likelihood based logistic regression for estimating COVID-19 infection and case fatality rates by gender, race, and age in California
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SciScore for 10.1101/2020.06.29.20141978: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:The proposed methods are subject to three general limitations. First, the analysis is based on publicly available test-based infection rates and case fatality rates. It has been well documented that the lack of testing for COVID-19 in the U.S. has hindered efforts to estimate the true COVID-19 infection rate. Further compounding this …
SciScore for 10.1101/2020.06.29.20141978: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:The proposed methods are subject to three general limitations. First, the analysis is based on publicly available test-based infection rates and case fatality rates. It has been well documented that the lack of testing for COVID-19 in the U.S. has hindered efforts to estimate the true COVID-19 infection rate. Further compounding this issue is the high prevalence of asymptomatic COVID-19 cases. These two issues may lead to substantial underestimates of the infection rates and/or substantial overestimates of the case fatality rates from our analyses. Second, race/ethnicity is missing in 29% of the reported cases from CDPH, which may bias our estimates. Even though CDPH releases summary statistics for age-race covariates and our model can fit the finer data, we fit the marginal statistics for each risk factor in the analysis to minimize the impact of the potential sampling bias. Moreover, the case and fatality data released by CDPH provide marginal summary statistics for a subset of risk factors, and we do not have direct information on the joint distribution of all risk factors. Although the central goal of our proposed methods is to circumvent this limitation, the absence of direct multivariate information on the risk factors of COVID-19 infection and mortality as well as the sampling bias should be taken into account when interpreting the results of our models. Third, in this paper, we do not consider regularity conditions ensuring concavity associated with the pseudo-log-lik...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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