Potential for Conflict of Interest in the Evaluation of Suspected Adverse Drug Reactions: A Counterpoint

Department of Biostatistics and Epidemiology, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, Philadelphia 19104-6021, USA.
JAMA The Journal of the American Medical Association (Impact Factor: 35.29). 01/2005; 292(21):2643-6. DOI: 10.1001/jama.292.21.2643
Source: PubMed
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    • "A recent Australian medical journal editorial called for the development of computerised systems that can deal with the reality that only limited numbers of highly selected patients are studied before a drug is approved for marketing [2]. One such system could be an improved pharmacovigilance system to detect ADE patterns associated with a newly prescribed drug; however, identifying the offending drug is not easy in a multi-morbidity/multi-drug context, and pharmacovigilance systems also have an inbuilt epistemological problem, as the reporting process is subject to Type I and Type II errors [6]. "
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    ABSTRACT: The study was undertaken to evaluate the contribution of a process which uses clinical trial data plus linked de-identified administrative health data to forecast potential risk of adverse events associated with the use of newly released drugs by older Australian patients. The study uses publicly available data from the clinical trials of a newly released drug to ascertain which patient age groups, gender, comorbidities and co-medications were excluded in the trials. It then uses linked de-identified hospital morbidity and medications dispensing data to investigate the comorbidities and co-medications of patients who suffer from the target morbidity of the new drug and who are the likely target population for the drug. The clinical trial information and the linked morbidity and medication data are compared to assess which patient groups could potentially be at risk of an adverse event associated with use of the new drug. Applying the model in a retrospective real-world scenario identified that the majority of the sample group of Australian patients aged 65 years and over with the target morbidity of the newly released COX-2-selective NSAID rofecoxib also suffered from a major morbidity excluded in the trials of that drug, indicating a substantial potential risk of adverse events amongst those patients. This risk was borne out in post-release morbidity and mortality associated with use of that drug. Clinical trial data and linked administrative health data can together support a prospective assessment of patient groups who could be at risk of an adverse event if they are prescribed a newly released drug in the context of their age, gender, comorbidities and/or co-medications. Communication of this independent risk information to prescribers has the potential to reduce adverse events in the period after the release of the new drug, which is when the risk is greatest.Note: The terms 'adverse drug reaction' and 'adverse drug event' have come to be used interchangeably in the current literature. For consistency, the authors have chosen to use the wider term 'adverse drug event' (ADE).
    Full-text · Article · May 2011 · BMC Public Health
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    • "However, case reports are usually considered hypothesis generating because calculating information from them about the frequency or comparative risk of adverse events is difficult. In the US, the FDA receives about 280,000 reports of postmarketing adverse events annually, collects them into a database [42], and issues information about adverse drug events on its MedWatch website ( watch/). "
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    ABSTRACT: Comparative effectiveness reviews (CERs) are systematic reviews that evaluate evidence on alternative interventions to help clinicians, policy makers, and patients make informed treatment choices. Reviews should assess harms and benefits to provide balanced assessments of alternative interventions. Identifying important harms of treatment and quantifying the magnitude of any risks require CER authors to consider a broad range of data sources, including randomized controlled trials (RCTs) and observational studies. This may require evaluation of unpublished data in addition to published reports. Appropriate synthesis of harms data must also consider issues related to evaluation of rare or uncommon events, assessments of equivalence or noninferiority, and use of indirect comparisons. This article presents guidance for evaluating harms when conducting and reporting CERs. We include suggestions for prioritizing harms to be evaluated, use of terminology related to reporting of harms, selection of sources of evidence on harms, assessment of risk of bias (quality) of harms reporting, synthesis of evidence on harms, and reporting of evidence on harms.
    Full-text · Article · Oct 2008 · Journal of clinical epidemiology
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