Evaluating the Test-Negative Design for COVID-19 Vaccine Effectiveness Using Randomized Trial Data: A Secondary...

Area of Science:

• Epidemiology
• Vaccinology
• Biostatistics

Background:

• The test-negative design (TND) is frequently used for postmarketing COVID-19 vaccine effectiveness studies.
• Further validation of the TND for this application is necessary.

Purpose of the Study:

• To evaluate the reliability of the TND in assessing vaccine effectiveness against symptomatic COVID-19.
• To compare TND estimates with vaccine efficacy data from placebo-controlled randomized clinical trials (RCTs).

Main Methods:

• A cross-protocol analysis was conducted using data from 5 harmonized Phase 3 COVID-19 Prevention Network RCTs across 16 countries.
• TND datasets were constructed from participants with COVID-19-like symptoms who were tested for SARS-CoV-2.
• Vaccine effectiveness was estimated using targeted maximum likelihood estimation and ordinary logistic regression, with noncase exchangeability assessed against non-COVID-19 illness.

Main Results:

• TND vaccine effectiveness estimates demonstrated strong concordance with RCT vaccine efficacy estimates (concordance correlation coefficient: 0.86).
• A semiparametric method resulted in 48% smaller variance estimates compared to ordinary logistic regression.
• Noncase exchangeability was generally supported, with median vaccine efficacy against non-COVID-19 illness of 7.7%.

Conclusions:

• The TND provides reliable inferences on COVID-19 vaccine effectiveness in populations seeking healthcare.
• The TND is effective for multiple vaccines and symptom definitions when confounding and selection bias are controlled.
• A machine-learning approach was introduced for enhanced confounding control in postmarketing TND studies.