Identifying and Alleviating Bias Due to Differential Depletion of Susceptible People in Postmarketing Evaluations of COVID-19 Vaccines
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Abstract
Recent studies have provided key information about SARS-CoV-2 vaccines’ efficacy and effectiveness (VE). One important question that remains is whether the protection conferred by vaccines wanes over time. However, estimates over time are subject to bias from differential depletion of susceptible individuals between vaccinated and unvaccinated groups. We examined the extent to which biases occur under different scenarios and assessed whether serological testing has the potential to correct this bias. By identifying nonvaccine antibodies, these tests could identify individuals with prior infection. We found that in scenarios with high baseline VE, differential depletion of susceptible individuals created minimal bias in VE estimates, suggesting that any observed declines are likely not due to spurious waning alone. However, if baseline VE was lower, the bias for leaky vaccines (which reduce individual probability of infection given contact) was larger and should be corrected for by excluding individuals with past infection if the mechanism is known to be leaky. Conducting analyses both unadjusted and adjusted for past infection could give lower and upper bounds for the true VE. Studies of VE should therefore enroll individuals regardless of prior infection history but also collect information, ideally through serological testing, on this critical variable.
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SciScore for 10.1101/2021.07.15.21260595: (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: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:This study has several limitations. First, we make many simplifying assumptions in the model. For example, we assume all individuals are grouped into one large community and do not examine the potential impact of geographic heterogeneity. Other studies have shown epidemic dynamics due to differences in geography are important to control for in vaccine (20) and serologic (3) studies. We also assume perfect sensitivity and specificity of …
SciScore for 10.1101/2021.07.15.21260595: (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: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:This study has several limitations. First, we make many simplifying assumptions in the model. For example, we assume all individuals are grouped into one large community and do not examine the potential impact of geographic heterogeneity. Other studies have shown epidemic dynamics due to differences in geography are important to control for in vaccine (20) and serologic (3) studies. We also assume perfect sensitivity and specificity of virologic tests, as implications of these parameters have been explored in detail previously (21,22). While we incorporate heterogeneity in risk of acquiring infection, we do not model differences in risk of transmitting infection (e.g. due to host factors). Additionally, as described above, the combined effects of immunity from prior infection and vaccination are uncertain; we model two simplified assumptions of this relationship but do not incorporate further complexity such as boosting of immunity from repeated exposure. Second, using serologic tests to identify prior infection is subject to error from imperfect test characteristics and waning of antibodies over time. However, we find only small biases in VE estimates from imperfect sensitivity, and information on past infection can also be obtained through self-report or medical records. Third, as described above, we assume random vaccination and no unmeasured confounding; the strategies discussed here alone do not address most other sources of potential confounding, which are important to ...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).
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.
Results from scite Reference Check: We found no unreliable references.
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