Abstract
Background: Previous research assessed the accuracy of disease-severity measurement in clinical studies as a mathematical relationship between the set of endpoints selected and the disease-severity scale (DSS), a surrogate for the theoretical Neutral list of indicators representing the disease phenotype. New DSSs are continually developed, so clinical studies’ operationalisation of the Neutral list and resulting relative neutrality may vary over time. We assessed variation in the neutrality of clinical studies over time and the probability of false positive and false negative classifications at different disease prevalence rates.Methods: We used search strings extracted from the Orphanet Register of Rare Diseases using a proprietary algorithm to conduct a systematic review of studies published until January 2021 per Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Overall, 483 studies and 12 rare diseases met inclusion criteria. We extracted all indicators from clinical studies and calculated neutrality and its components, sensitivity and specificity, as well as the probability of misclassifications at 20%, 50% and 80% disease prevalence rates at two time points, the times of publication of the first and last DSS. Surrogate Neutral lists were the first DSS and a composite of all later DSSs.Results: Over time, the neutrality of clinical studies increased for six diseases and decreased for five diseases, driven by sensitivity for all but Friedreich ataxia. The neutrality of clinical studies in encephalitis decreased, but sensitivity remained constant at zero. At both timepoints, the likely false negative rate increased and the likely false positive rate decreased with increasing disease prevalence. The probability that the least neutral clinical study for most diseases would yield a false positive result was equal to one at all disease prevalence rates. Conclusions: The potential for accurate clinical trial disease-severity measurement increases over time. Neutral theory showed that endpoint selection and DSSs may need improvement in Charcot Marie Tooth disease, Gaucher disease Type I, Huntington’s disease, Sjogren’s syndrome and Tourette syndrome. Using Neutral theory to benchmark disease-severity measurement in rare disease clinical trials may reduce the risk of misclassification, ensuring that recruitment and treatment effect assessment optimise medicine adoption and benefit patients.
Variation in the Accuracy of Endpoint Selection in Clinical Studies for Rare Diseases in Respect of Increasing Knowledge on Disease-Severity Measurement