Errors in variables regression with value-censored data

Samples with analyte concentrations outside a method's dynamic range are a reality of clinical chemistry and are particularly of interest in method comparison studies. The most obvious remedy-to ignore any such values-introduces bias and loses the information that censored data might add to the analysis. Extending conventional errors-in-variables methods to incorporate value-censored data recovers this information. The formulation presented uses a variance model more flexible than either the constant variance or the constant coefficient of variation models.