Key result
High-quality normative data acquisition requires standardized objective determination of freedom from disease, large stratified samples, and appropriate statistical methods for accurate electrodiagnosis.
High-quality, standardized normative data is essential for accurate electrodiagnosis of neuromuscular diseases, and many existing studies do not meet proposed standards.
Caution advised with existing normative data in electrodiagnosis; leaves open the need for standardized large-scale reference studies.
This article reviews, without mathematics, the important principles governing the acquisition and use of normative data in electrodiagnostic medicine. Common flaws in neurophysiological normative data include vague clinical criteria for establishing freedom from disease, samples that are too small and inadequately stratified, and application of Gaussian statistics to non-Gaussian variables. Other problematic issues concern the trade-off between permissible false-positivity and false-negativity in defining the limits of normative from sample data, test-retest variability, and the use of multiple independent test measurements in each electrodiagnostic examination. The following standards for normative data are proposed: (1) standardized objective determination of freedom from disease; (2) appropriately large sample of normal subjects; (3) proportional statification of normal subjects for known relevant variables; (4) test of Gaussian fit for application of Gaussian statistics; and (5) data presentation by percentiles when Gaussian fit is in doubt. Many existing normative studies in clinical neurophysiology do not meet these standards. High-quality normative data, readily accessible, is essential for the accurate electrodiagnosis of neuromuscular diseases.
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Dorfman et al. (1997) conducted a review in Neuromuscular diseases. Normative data acquisition was evaluated. High-quality normative data acquisition requires standardized objective determination of freedom from disease, large stratified samples, and appropriate statistical methods for accurate electrodiagnosis.
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