Statistical considerations for harmonization of the global multicenter study on reference values

Abstract

The global multicenter study on reference values coordinated by the Committee on Reference Intervals and Decision Limits (C-RIDL) of the IFCC was launched in December 2011, targeting 45 commonly tested analytes with the following objectives: 1) to derive reference intervals (RIs) country by country using a common protocol, and 2) to explore regionality/ethnicity of reference values by aligning test results among the countries. To achieve these objectives, it is crucial to harmonize 1) the protocol for recruitment and sampling, 2) statistical procedures for deriving the RI, and 3) test results through measurement of a panel of sera in common. For harmonized recruitment, very lenient inclusion/exclusion criteria were adopted in view of differences in interpretation of what constitutes healthiness by different cultures and investigators. This policy may require secondary exclusion of individuals according to the standard of each country at the time of deriving RIs. An iterative optimization procedure, called the latent abnormal values exclusion (LAVE) method, can be applied to automate the process of refining the choice of reference individuals. For global comparison of reference values, test results must be harmonized, based on the among-country, pair-wise linear relationships of test values for the panel. Traceability of reference values can be ensured based on values assigned indirectly to the panel through collaborative measurement of certified reference materials. The validity of the adopted strategies is discussed in this article, based on interim results obtained to date from five countries. Special considerations are made for dissociation of RIs by parametric and nonparametric methods and between-country difference in the effect of body mass index on reference values.

Keywords: Latent abnormal values exclusion method; Modified Box–Cox transformation formula; Multicenter study; Panel of sera; Reduced major-axis regression; Reference interval.