Using the hazard ratio to evaluate allowable total error in predictive measurands.

Abstract

Allowable total error is usually derived from data on biological variation or from state-of-the-art of measuring technology. Here we present a new principle for evaluating allowable total error when the concentration of the analyte (the measurand) is used for prediction: What are the predictive consequences of allowable total errors in terms of errors in the estimate of the hazard ratio (HR)? We explored the effect of analytical measurement errors on Cox regression estimates of HR. Published data on Cox regression coefficients were used to illustrate the effect of measurement errors on predicting cardiovascular events or death based on serum concentration of cholesterol and on progression of chronic kidney disease to kidney failure based on serum concentrations of albumin, bicarbonate, calcium and phosphate, and urine albumin/creatinine-ratio. If the acceptable error in the estimate of the HR is 10%, allowable total errors in serum cholesterol, bicarbonate and phosphate are approximately the same as allowable total error based on biological variation, while allowable total error in serum albumin and calcium are a little larger than estimates based on biological variation. Evaluating allowable total error from its effect on the estimate of HR is universally applicable to measurands used for prediction.