Incorporating validation subsets into discrete proportional hazards models for mismeasured outcomes

Standard proportional hazards methods are inappropriate for mismeasured outcomes. Previous work has shown that outcome mismeasurement can bias estimation of hazard ratios for covariates. We previously developed an adjusted proportional hazards method that can produce accurate hazard ratio estimates when outcome measurement is either non-sensitive or non-specific. That method requires that mismeasurement rates (the sensitivity and specificity of the diagnostic test) are known. Here, we develop an approach to handle unknown mismeasurement rates. We consider the case where the true failure status is known for a subset of subjects (the validation set) until the time of observed failure or censoring. Five methods of handling these mismeasured outcomes are described and compared. The first method uses only subjects on whom complete data are available (validation subset), whereas the second method uses only mismeasured outcomes (naive method). Three other methods include available data from both validated and non-validated subjects. Through simulation, we show that inclusion of the non-validated subjects can improve efficiency relative to use of the complete case data only and that inclusion of some true outcomes (the validation subset) can reduce bias relative to use of mismeasured outcomes only. We also compare the performance of the validation methods proposed using an example data set.