Table 2.

Simulation results based on 2, 000 Monte Carlo data sets for estimation of the log-hazard ratio β in (3.1) based on the time-to-event endpoint and of the log-odds ratio β in a logistic regression model analogous to (3.1) based on the binary endpoint, as at the end of Section 4. Methods are as in Table 1. True is the true value of β, Mean Est. is the Monte Carlo average of estimates, MC SD is the Monte Carlo standard deviation of estimates, Ave. SE is the Monte Carlo average of estimated standard errors, and Cov. Prob. is Monte Carlo coverage probability of nominal 95% Wald confidence intervals. Estimated standard errors were obtained by treating the weights as fixed as discussed in the Supplementary Material available at Biostatistics online

	Log-hazard ratio					Log-odds ratio
Method	True	Mean Est.	MC SD	Ave. SE	Cov. Prob	True	Mean Est.	MC SD	Ave. SE	Cov. Prob
Intent-to-treat	− 0.500	− 0.334	0.055	0.055	0.141	− 0.546	− 0.427	0.115	0.116	0.824
Censor, optional	− 0.500	− 0.389	0.065	0.065	0.602
Inverse weighting
w(u, Z) ≡ 1	− 0.500	− 0.492	0.074	0.077	0.960	− 0.546	− 0.546	0.143	0.146	0.956
w(u, Z) depends on Z	− 0.500	− 0.502	0.091	0.092	0.956