. 2013 Dec 18;179(5):641–647. doi: 10.1093/aje/kwt309

Table 3.

Results Accounting for Outcome Misclassification Using Modified Maximum Likelihood in Poisson Regression in 10,000 Simulated Cohorts^a

Scenario	Measure		Method	Rate Ratio	Bias^b	95% CI Coverage^c	Mean Squared Error^d
Scenario	Specificity	Sensitivity	Method	Rate Ratio	Bias^b	95% CI Coverage^c	Mean Squared Error^d
1	1	1	Truth	2.00	0	95	0.77
2	0.95	0.9	Standard ML	1.89	−5	91	1.15
			Modified ML	2.00	0	95	0.95
3	0.95	0.6	Standard ML	1.84	−8	89	1.96
			Modified ML	2.01	0	95	1.30
4	0.9	0.9	Standard ML	1.80	−10	79	1.93
			Modified ML	2.01	0	95	0.96
5	0.9	0.6	Standard ML	1.72	−15	72	3.55
			Modified ML	2.01	1	95	1.46

Abbreviations: CI, confidence interval; ML, maximum likelihood.

^a The models accounting for imperfect sensitivity and specificity did not converge in 6, 7, 9, and 5 simulated cohorts for scenarios 2, 3, 4, and 5, respectively.

^b Bias was defined as 100 times the difference between the true ln(rate ratio) and the estimated ln(rate ratio).

^c The 95% confidence interval coverage was the proportion of simulations in which the estimated 95% confidence interval contained the true value.

^d Mean squared error was the sum of the square of the bias and the square of the standard deviation of the bias.