TABLE 2.

Simulation Study Results

	Truth	Mean Est	MC SE	% Cov cRR	% CovmRR
Scenario 1: Basic setting
Estimands
1 − ψcRR	0.96
1 − ψmRR	0.96
1 − mRR for SARS-CoV-2	0.84
Estimators
Logistic regression		0.97	0.01	89	-
IPTW
πcontrols		0.96	0.02	-	92
πall		0.44	0.06	-	0
Scenario 2: Effect modification by C for SARS-CoV-2 infection
Estimands
1 − ψcRR	0.77
1 − ψmRR	0.75
1 − mRR for SARS-CoV-2	0.44
Estimators
Logistic regression		0.91	0.06	39	-
IPTW
πcontrols		0.74	0.15	-	93
πall		0.18	0.05	-	0
Scenario 3: Partial interference by block vaccination prevalence, f
Estimands
1 − ψcRR	0.86
1 − ψmRR	0.85
1 − mRR for SARS-CoV-2	0.48
1 − ψcRR,75	0.86
1 − ψcRR,50	0.80
1 − ψcRR,25	0.76
Estimators
Logistic regression		0.88	0.04	89	-
IPTW
πcontrols		0.85	0.06	-	92
πall		0.14	0.04	-	0

Aggregate results of the application of each method to 1,000 simulated datasets of n hospitalized patients where n = 500 for Scenarios 1 and 2 and n =1000 for Scenario 3. The results are given with respect to one minus the risk ratios, often referred to as “vaccine effectiveness.” ${\psi }_{cRR}$: the conditional risk ratio for hospitalization with COVID-19 in Equation (3); ${\psi }_{mRR}$: the marginal risk ratio for hospitalization with COVID-19 in Equation (5).

% Cov indicates % of 95% confidence intervals that contain the true vaccine effectiveness (optimal is 95%); Mean est, mean estimate; MC SE, Monte-Carlo standard error of the estimate; mRR marginal risk ratio.