quantitative methods of signal detection - parametric modelling of the time to onset of adverse drug...
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Parametric modelling of time to onset of adverse drug reactions using parametric survival distributions
F. MaignenPrincipal scientific administrator
European Medicines Agency2nd DIA Conference on Signal Detection and Data Mining
17-18 November 2009
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Plan of the presentation1. Conflicts of interests and disclaimers2. Background and rationale of the project3. Materials and methods4. Results5. Interpretation and
discussion6. Conclusions and
future directions
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DIA Disclaimer
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Conflicts of interest and more disclaimers
• P. Tsintis and M. Hauben have contributed to this study• Other external contributions received from other Experts• No external funding was received for this study• I do not have any financial interests with the Pharmaceutical
industry or any IT software provider (declaration available from the Agency)
• I thank the two Companies which have given their approval to publish these results
• Disclaimer on the views expressed in this presentation wrt European Medicines Agency
• No claim on a “better” safety profile on any medicinal product mentioned in this work should be made.
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Disclaimers (cont.)ACKNOWLEDGEMENTS• No external source of funding was used to perform this study. The
implementation of EudraVigilance was undertaken by the EudraVigilance team at the EMEA lead by Dr Sabine Brosch. The following authors: FM has no conflicts of interest with the pharmaceutical industry (declaration of interest available from EMEA). P. Tsintis contributed to the study when he was working for the EMEA. M. Hauben is also working in Department of Medicine, Risk Management Strategy, Pfizer Inc., New York, New York University School of Medicine, Departments of Community and Preventive Medicine and Pharmacology, New York Medical College, Valhalla, New York, USA and for the School of Information Systems, Computing and Mathematics, Brunel University, London, England . None of the authors have any conflict of interests with any statistical software provider. Valuable comments on this work were received from Nils Feltelius, Hans-Georg Eichler, Francesco Pignatti, Xavier Kurz, Jim Slattery and Anders Sundström.
DISCLAIMER• The views expressed in this presentation are the personal views of the author(s)
and may not be understood or quoted as being made on behalf of or reflecting the position of the European Medicines Agency or one of its committees or working parties.
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Background
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Background• The time to onset of adverse drug reactions is directly connected to the underlying
mechanism of the toxicity associated with a medicine (DoTS classification)• The current quantitative methods do not integrate any information concerning the
underlying toxic mechanism of the suspected medicinal product (some rare studies conducted by A. Bate and E. Van Puijenbroek).
• Current methods used to analyse the reported time to onset of adverse drug reactions in Pharmacovigilance
• Simple histograms (or LOESS)– Only provide a partial view of the evolution of the risk– The visualisation of the risk highly depends on the number of bins and
bandwidth– Difficult to find a “risk window”– Output can be awful (LOESS).
• Other non-parametric methods– Kaplan-Meier estimate of the survivor function: can be difficultto interpret and difficult to actually visualise the exactevolution of the risk.
• Find patterns of toxicity (true signals) via the hazards
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Rationale for the study: hazard and hazard functions
• The hazard expresses the risk that something happens at a certain time t (does not help a lot).
• The hazard function specifies the instantaneous rate at which events / failures occur for items which survived until time t.
• Some recent classifications of adverse drug reactions (DoTS) includes the time relatedness as one key elements the classification.
• Therefore (in theory) the hazard should be directly connected to the underlying mechanism of the toxic effect resulting in an adverse drug reaction.
• Parametric survival distributions have a hazard function which is specified by a function (in opposition to non-parametric methods such as CPH).
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Hazard fcts of parametric survival dist.
Kalbfleisch and Prentice. The statistical analysis of failure time data. Second ed. Wiley and sons.
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Reported hazard of occurrence: a phenomenon involving several mechanisms
• P(occur.)*P(diag./occur.)*P(rep./diag.)(1)• P = prob. failure conditional on survival
until time t.• Lim f(x)*g(x) = Lim f(x)*Lim g(x)• Then when we take Lim t -> 0 (1)
becomes.
h(occur.)*h(diag./occur.)*h(rep./diag.)
PDToxicology profile
Efficacy / duration tt
Monitoring and “RM” activities
Awareness
AwarenessReporting mechanisms
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Materials and methods
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Materials and methods• We have used parametric survival distributions to perform a
modelling of the reported time to onset to compute and plot the corresponding hazard functions for signal detection purposes (in a broad sense).
• The objective is to illustrate (and better understand the elements of interpretation of) the use of hazard functions for signal detection purposes using two real examples.
• Study conducted on a spontaneous reported database (EudraVigilance).
• Two examples have been used in the study:Liver injuries associated with BosentanInfections associated with the useof TNF alpha inhibitors
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Materials and methodsEudraVigilance reports
Computation of TtO(> 5)
KM Fit parametric distribution(Exp/Weibull/LogN/Normal)
Selection of best fit
Computation / plot haz fct
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Criteria used to interpret the results
• The idea is to use a convergence of available evidence together with the hazard functions of the reported time to onset to assess whether there is a signal:– Existing signal– Pharmacodynamic properties of the products – Bradford-Hill criteria which have been used to
interpret the results of data mining algorithms.
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Results
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Liver injuries reported with bosentan(descriptive stats)
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Liver injuries reported with bosentan (KM)
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Liver injuries reported with bosentan (result of the fit of parametric distributions)
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Liver injuries reported with bosentan(hazard functions)
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Bosentan – liver injuries• Logical course of events some occurrences need
careful interpretation (blood bilirubin inc. and [hyper]bilirubinemia)
• Pattern AST/ALT unusual for liver injuries (but not for mitochondrial injuries from hepatocytes) but consistent with clinical safety data
• Residual and constant risk of liver failure• Consistent with the putative mechanism of toxicity
(dose-dpt)• Consistent with the safety profile of bosentan (lack of
independence)• Influence of the risk minimisation activities
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Infections reported with the administration of TNF alpha inhibitors (KM)
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Infections reported with the administration of TNF alpha inhibitors (hazard functions)
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Reported risk of infection reported with the administration of etanercept (hazard functions)
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Infliximab and adalimumab (hazard functions)
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TNF inhibitors - infections• Striking similarities (early risk of UTI, sepsis,
pneumonia and herpes zoster) and differences between products (TB and cellulitis)
• Consistent with the PD properties of the products and results of clinical trials
• Differences could be explained by:– PD/PK differences (half life of adalimumab significantly
longer than for the other two products, etanercept also binds TNF beta, infliximab inhibits IFN gamma)
• Probable influence of RM activities / monitoring of the patients (provided that the side-effect can be detected / prevented - cf Bosentan).
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Reported risk of tuberculosis reported with the administration of TNF alphas
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Risk of tuberculosis reported with the administration of infliximab
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TNF inhibitors - TB• TNF alpha plays an important role in the control of
granulomatous infections• Main difference is observed between infliximab / etanercept on
the one hand and adalimumab on the other• Different PD properties between the products would implies
different profiles between infliximab and etanercept• Different PK profile between adalimumab and the
other two products• Awareness and risk minimisation: adalimumab
has been authorised after the first two productswhen the risk of TB was established and recommendations to monitor the patients had beenpublished (shift of risk of TB? Different reportingpattern?).
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Summary of the main results• Pattern consistent with the logical course of action of the toxicity
(bosentan)• Hazard consistent with the suspected mechanism of the toxicity
(bosentan – dose dependent)• Consistency of the reported hazard of occurrence of the infections
across the 3 TNFs• Consistency of the reported hazard of tuberculosis for infliximab• Differences between the TNFs products (TB) could be explained by:
– Different pharmacological properties– Different pharmacokinetic properties– Different monitoring of the patients / reporting mechanisms
• Patterns consistent with the known safety profile of the product (two analyses not completely independent)
• Hazards certainly influenced by awareness and risk management activities
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Discussion
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Factors influencing the modelling
• Nplicates– Method sensitive to duplication
like any other DMA– Consider the cases of extreme
duplication– Duplication vs clusters
• Data quality– Accuracy of the dates– Completeness and precision does not
mean accuracy• Good documentation and FUp of the cases
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Statistical issues and important limitations
• The work is still preliminary. Interpretation is still based on explanations which involve documented pharmacological or reporting behaviours which can be subjective
• Issue with censoring and competing risks• Absence of hypothesis testing +++• Great difficulty to choose a suitable
comparator to build the hypothesis testing.• Performances need to be tested
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Statistical issues and important limitations
• Approach limited by the number and quality of the reports• Influence of the reporting mechanisms +++
– since the modelling was performed on spontaneous reporting data, the hazard does not have the usual interpretation as an instantaneous probability of failure conditional on survival to time t.
– As far as the spontaneous reports are concerned, the hazard reflects a mixture of reporting behaviour and natural history which cannot be disentangled.
• Multi-state model?– A “complete model” would not be devoid of any limitations or would
rely on strong assumptions which may not be met. – Spontaneous reporting does not collect all the information needed
to build such model).– Situation dependent
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CONCLUSIONS
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Conclusions
• Encouraging work which illustrates the potential use of hazard functions in signal detection
• Inherent limitations of the spontaneous reporting
• A lot of data manipulation• Carefully consider the influence of reporting
mechanisms (biases) and data quality (cliché)• Some statistical issues to be addressed
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Future directions
• Better understand the reporting mechanisms• Test the approach to discriminate true signals from
confounding (find negative examples)• Build a test of hypothesis• Use it in specific situations where a “shift” of the
hazard function could reflect an underlying / intercurrent event
• Assess the performances on a larger scale of data• Potentially able to disantangle the reporting
mechanisms by comparing functions from different sources of collection of information
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Acknowledgements
• Thank you to the persons who have supported me in this work (list not limitative)– Jim Slattery– Xavier Kurz– JM Dogne– Anders Sundstrom– Eugene Van Puijenbroek– HG Eichler
