Computer program for the proportional hazards measurement error model

The Cox-regression analysis based on the partial likelihood assumes that the covariates, or independent variables, are exactly measured without error. If the covariates are subject to measurement error and the error-prone observed values are used in the analysis by simply ignoring the measurement error, the results are generally biased and misleading; the bias does not diminish as the sample size is increased. The objective of the paper is to briefly describe a method searching for asymptotically unbiased estimates of the parameters correcting for the measurement error in the Cox-regression model and to present a FORTRAN program to perform the correction method; asymptotic standard errors of the corrected estimates are also obtained. The measurement error distribution, that is the conditional distribution of the observed values given the true value, must be specified. An advantage of the method described is that it does not require any assumption on the distribution of the true values; in other words, <--> values are treated as unknown fixed constants. It can accommodate tied failure times unless ties are very frequent, and any censorship or loss to follow-up are allowed as long as they are 'independent of survival'.