The conditional score approach is proposed to the analysis of errors-in-variable current status data under the proportional odds model. Distinct from the conditional scores in other applications, the proposed conditional score involves a high-dimensional nuisance parameter, causing challenges in both asymptotic theory and computation. We propose a composite algorithm combining the Newton–Raphson and self-consistency algorithms for computation and develop an efficient conditional score, analogous to the efficient score from a typical semiparametric likelihood, for building an asymptotic linear expression and hence the asymptotic distribution of the conditional-score estimator for the regression parameter. Our proposal is shown to perform well in simulation studies and is applied to a zebrafish basal cell carcinoma data involving measurement errors in gene expression levels.
Scandinavian Journal of Statistics 39(4), pp.635-644