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Reporter’s causality assessment Example: One drug, 12 month period 2010-2011 • Events from study cases: 568/2155 (26%) `not related’ (2 re-assigned `related’ by the company) 944/2155 (44%) `related’ 643/2155 (30%) no reporter causality assessment • Events from spontaneous cases: 199/2681 (8%) `not related’ 1191/2681 (44%) `related’ 1291/2681 (48%) no reporter causality assessment Note: All spontaneous events are considered `related’ by the company for reporting purposes
Methods for the assessment of individual cases Guidance CIOMS VI (2005): • Survey of pharmaceutical companies: ‟ 12/21 companies use ìntrospection’, 2 home-grown algorithms, 3 published methods, 4 non- specific methods • Recommendations: ‟ Binary yes/no causality (i.e. related/not related) for study investigators „ Grades of causality (e.g. `possible’, `probable’, `definite’) offer little practical advantage. Only `related’ versus ùnrelated’ is needed for regulatory reporting requirements „ Poor inter-rater agreement using terms such as `possible’ or `probable’ ‟ Investigator should complete a checklist of potential causes ‟ Events should be considered related if there is “a reasonable possibility of a causal relationship” rather than if “a causal relationship cannot be ruled out”
Page 1 and 2: Causality Assessment in Practice Ph
Page 3 and 4: Overview of presentation • Causal
Page 5 and 6: Instructions for investigators Typi
Page 7 and 8: Case processing Study cases
Page 9: Adding èvents’ to the reporter
Page 13 and 14: Naranjo algorithm at Roche • Pre-
Page 16 and 17: Expressing causality in words `Prob
Page 18 and 19: Combining evidence from different s
Page 20 and 21: Single compelling case reports •
Page 22 and 23: Final thoughts… • Communicating
Page 24 and 25: Back-up slides, additional referenc
Page 26 and 27: Guidance • CIOMS VI ‟ A CRF is
Page 28: Capturing the reporter’s assessme

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