Correction

‘Multiple imputation for correcting verification bias’ by Harel O, Zhou XH ( Statistics in Medicine 2006; 25 :3769–3786) and ‘Rejoinder to Multiple imputation for correcting verification bias’ ( Statistics in Medicine 2007; 26 :3047–3050). A calculation error in the breast cancer example using diaphanography data presented in our paper was recently brought to our attention. In this example, the code calculating the sensitivity and specificity for the multiple imputation (MI) techniques had a typo resulting in column summation instead of row summation. As a result, the reported sensitivity and specificity were actually positive predictive and negative predictive values. The reported values for the methods not requiring MI (Naive 1 and B & G 2) were correct. The corrected table is reported in Table I. We want to emphasize that the conclusions in Harel and Zhou 1 are still valid. The conclusions were based on a simulation study for which their results are valid. We apologize for any inconvenience caused to Statistics in Medicine readers. I Results comparing five MI methods, the B & G as existing method and naive method of sensitivity and specificity—diaphanography data for breast cancer.Sensitivity SpecificityProcedure Est SE CI Est SE CI Naive 0.788 0.071 (0.649, 0.927) 0.800 0.054 (0.694, 0.906) B&G 0.292 0.080 (0.134, 0.449) 0.973 0.008 (0.958, 0.988) Logit B&G 0.292 0.388 (0.161, 0.469) 0.973 0.288 (0.954, 0.985) A&C 0.304 0.075 (0.145, 0.462) 0.972 0.007 (0.959, 0.985) Rubin 0.297 0.079 (0.165, 0.475) 0.974 0.007 (0.957, 0.984) Wilson 0.292 0.035 (0.223, 0.361) 0.972 0.006 (0.960, 0.983) Jeffrey 0.299 — (0.222, 0.359) 0.974 — (0.961, 0.984) Z&L 0.301 — (0.235, 0.373) 0.973 — (0.960, 0.983)