Practical pharmacovigilance analysis strategies.

PURPOSE To compare two recently proposed Bayesian methods for quantitative pharmacovigilance with respect to assumptions and results, and to describe some practical strategies for their use. METHODS The two methods were expressed in common terms to simplify identifying similarities and differences, some extensions to both methods were provided, and the empirical Bayes method was applied to accumulated experience on a new antihypertensive drug to elucidate the pattern of adverse-event reporting. Both methods use the logarithm of the proportional risk ratio as the basic metric for association. RESULTS The two methods provide similar numerical results for frequently reported events, but not necessarily when few events are reported. Using a lower 5% quantile of the posterior distribution gives some assurance that potential signals are unlikely to be noise. The calculations indicated that most potential adverse event-drug associations that were well-recognized after 6 years of use could be identified within the first year, that most of the associations identified in the first year persisted over time. Other insights into the pattern of event reporting were also noted. CONCLUSION Both methods can provide useful early signals of potential drug-event associations that subsequently can be the focus of detailed evaluation by skilled clinicians and epidemiologists.

[1]  M. Hippius,et al.  Adverse drug reaction monitoring--digitoxin overdosage in the elderly. , 2001, International journal of clinical pharmacology and therapeutics.

[2]  Robert T. O'Neill,et al.  Some US Food and Drug Administration Perspectives on Data Mining for Pediatric Safety Assessment , 2001 .

[3]  S. Z. Zhao,et al.  A comparison of renal-related adverse drug reactions between rofecoxib and celecoxib, based on the World Health Organization/Uppsala Monitoring Centre safety database. , 2001, Clinical therapeutics.

[4]  M. Braun,et al.  Data mining in the US Vaccine Adverse Event Reporting System (VAERS): early detection of intussusception and other events after rotavirus vaccination. , 2001, Vaccine.

[5]  A. Egberts,et al.  Detecting drug–drug interactions using a database for spontaneous adverse drug reactions: an example with diuretics and non-steroidal anti-inflammatory drugs , 2000, European Journal of Clinical Pharmacology.

[6]  A. Bate,et al.  A Bayesian neural network method for adverse drug reaction signal generation , 1998, European Journal of Clinical Pharmacology.

[7]  W. Amery.  Assessment of signals generated from spontaneously reported adverse events , 1999, Pharmacoepidemiology and Drug Safety.

[8]  William DuMouchel,et al.  Empirical bayes screening for multi-item associations , 2001, KDD '01.

[9]  D. Murphy,et al.  Adverse Drug Events in Children: The US Food and Drug Administration Perspective , 2001 .

[10]  M. Lindquist,et al.  A comparison of measures of disproportionality for signal detection in spontaneous reporting systems for adverse drug reactions , 2002, Pharmacoepidemiology and drug safety.

[11]  Andrew Bate,et al.  From association to alert—a revised approach to international signal analysis , 1999, Pharmacoepidemiology and drug safety.

[12]  D. J. Finney,et al.  The detection of adverse reactions to therapeutic drugs. , 1982, Statistics in medicine.

[13]  D. J. Finney,et al.  stematic Signalling of Adverse Reactions to Drugs , 1974, Methods of Information in Medicine.

[14]  P. Donnan,et al.  The multiple propensity score for analysis of dose–response relationships in drug safety studies , 2001, Pharmacoepidemiology and drug safety.

[15]  Thomas P. Gross,et al.  Proposed Statistical Methods for Signal Detection of Adverse Medical Device Events , 1998 .

[16]  M. Lapeyre-Mestre,et al.  [Drug vigilance: opinion survey among residents of a university hospital]. , 1994, Therapie.

[17]  R. Meyboom,et al.  Pharmacovigilance in Perspective , 1999, Drug safety.

[18]  C. Waller,et al.  Safety of human albumin based on spontaneously reported serious adverse events , 2001, Critical care medicine.

[19]  B. Bégaud,et al.  Under-reporting of adverse drug reactions Estimate based on a spontaneous reporting scheme and a sentinel system , 1998, European Journal of Clinical Pharmacology.

[20]  William DuMouchel,et al.  Bayesian Data Mining in Large Frequency Tables, with an Application to the FDA Spontaneous Reporting System , 1999 .

[21]  J. Evans,et al.  Do H2‐receptor antagonists cause acute pancreatitis? , 1998, Pharmacoepidemiology and drug safety.

[22]  A. Lansner,et al.  Bayesian neural networks with confidence estimations applied to data mining , 2000 .

[23]  Peter G. M. van der Heijden,et al.  On the assessment of adverse drug reactions from spontaneous reporting systems: the influence of under-reporting on odds ratios , 2002 .

[24]  T M Smith,et al.  Testing for adverse reactions using prescription event monitoring. , 1996, Statistics in medicine.

[25]  E. H. Simpson,et al.  The Interpretation of Interaction in Contingency Tables , 1951 .

[26]  S J Evans,et al.  Pharmacovigilance: a science or fielding emergencies? , 2000, Statistics in medicine.

[27]  P. Waller.  Pharmacoepidemiology – a tool for public health * , 2001, Pharmacoepidemiology and drug safety.

[28]  W. Amery.  Signal generation from spontaneous adverse event reports , 1999, Pharmacoepidemiology and drug safety.

[29]  F. Haramburu,et al.  Under-reporting of adverse drug reactions in general practice. , 2003, British journal of clinical pharmacology.

[30]  M. Lindquist,et al.  A Retrospective Evaluation of a Data Mining Approach to Aid Finding New Adverse Drug Reaction Signals in the WHO International Database , 2000, Drug safety.

[31]  M. Lindquist,et al.  Antipsychotic drugs and heart muscle disorder in international pharmacovigilance: data mining study , 2001, BMJ : British Medical Journal.

[32]  H. Tilson,et al.  Proactive safety surveillance , 2001, Pharmacoepidemiology and drug safety.