Causality of Adverse Drug Reactions: The Upper-Bound of Arbitrated Expert Agreement for Ratings Obtained by WHO and Naranjo Algorithms

Paul R. Yarnold

Optimal Data Analysis, LLC

As a high-ranking cause of human mortality, adverse drug reactions (ADRs) are the focus of an enormous literature, and optimal statistical methods have proven undaunted by the analysis-challenging geometry of multi-site longitudinal medical data sets. Two broadly-used causality assessment algorithms for identifying ADRs are the Naranjo and World Health Organization (WHO) ADR algorithms. Ratings made using these algorithms haven’t been validated, so the extent to which arbitrated ratings made by independent experts using these algorithms agree is important in assessing the expected upper-bound of inter-rater, inter-method reliability. Using data from India on this issue, UniODA identified a strong to very strong relationship between ratings obtained using WHO and Naranjo algorithms for a sample of N = 200 randomly selected patients. Inter-algorithm disagreement occurred for 15.2% of cases indicated as “Probable” by the Naranjo algorithm, but as “Possible” by the WHO algorithm.

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