Methods and Issues to Consider for Detection of Safety Signals From Spontaneous Reporting Databases: A Report of the DIA Bayesian Safety Signal Detection Working Group

Spontaneous reporting (SR) adverse event system databases, large observational databases, large clinical trials, and large health records databases comprise repositories of information that may be useful for early detection of potential harms associated with drugs, devices, and vaccines. All of the data sources include many different adverse events and many medical products, so that any approach designed to detect “important” signals of potential harm must have adequate specificity to protect against false alarms yet provide satisfactory sensitivity for detecting issues that really need further investigation. Algorithms for evaluating potential risks using information from these sources, especially SR databases, have been described in the literature. The algorithms may seek to identify potential product-event associations without any prior specifications, to identify events associated with a particular product or set of products, or to identify products associated with a particular event or set of events. This article provides recommendations for using information from postmarketing spontaneous adverse event reporting databases to provide insight into risks of potential harm expressed by safety signals and offers guidance regarding appropriate methods, both frequentist and Bayesian, to use in various situations as a function of the objective of the analysis.

[1]  Vincent Bauchau,et al.  Optimization of a quantitative signal detection algorithm for spontaneous reports of adverse events post immunization , 2013, Pharmacoepidemiology and drug safety.

[2]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[3]  Health Canada has released its Draft Guidance Document for Industry, Reporting Adverse Reactions to Marketed Health Products , 2008 .

[4]  M. Lindquist,et al.  A Data Mining Approach for Signal Detection and Analysis , 2002, Drug safety.

[5]  R. Tiwari,et al.  A Likelihood Ratio Test Based Method for Signal Detection With Application to FDA’s Drug Safety Data , 2011 .

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

[7]  Robert Ball,et al.  Effects of Stratification on Data Mining in the US Vaccine Adverse Event Reporting System (VAERS) , 2008, Drug safety.

[8]  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.

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

[10]  Brian Sauer,et al.  Guidelines for good database selection and use in pharmacoepidemiology research , 2012, Pharmacoepidemiology and drug safety.

[11]  D. Madigan,et al.  The role of data mining in pharmacovigilance , 2005, Expert opinion on drug safety.

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

[13]  D Madigan,et al.  Logistic Regression in Signal Detection: Another Piece Added to the Puzzle , 2013, Clinical pharmacology and therapeutics.

[14]  Carol Friedman,et al.  Mining multi-item drug adverse effect associations in spontaneous reporting systems , 2010, BMC Bioinformatics.

[15]  Johan Hopstadius,et al.  Impact of Stratification on Adverse Drug Reaction Surveillance , 2008, Drug safety.

[16]  John A. Clark,et al.  Spontaneous adverse event signaling methods: classification and use with health care treatment products. , 2001, Epidemiologic reviews.

[17]  Gaurav Deshpande,et al.  Data Mining in Drug Safety , 2010, Pharmaceutical Medicine.

[18]  Ismaïl Ahmed,et al.  Bayesian pharmacovigilance signal detection methods revisited in a multiple comparison setting , 2009, Statistics in medicine.

[19]  Andrea J Cook,et al.  Statistical approaches to group sequential monitoring of postmarket safety surveillance data: current state of the art for use in the Mini‐Sentinel pilot , 2012, Pharmacoepidemiology and drug safety.

[20]  David Madigan,et al.  Large-scale regression-based pattern discovery: The example of screening the WHO global drug safety database , 2010 .

[21]  Pankdeep Chhabra,et al.  Adverse Event Reporting Patterns of Newly Approved Drugs in the USA in 2006: An Analysis of FDA Adverse Event Reporting System Data , 2013, Drug Safety.

[22]  A. Fourrier-Réglat,et al.  The EU-ADR project: preliminary results and perspective. , 2009, Studies in health technology and informatics.

[23]  Richard Platt,et al.  The U.S. Food and Drug Administration's Mini‐Sentinel program: status and direction , 2012, Pharmacoepidemiology and drug safety.

[24]  R. Altman,et al.  Data-Driven Prediction of Drug Effects and Interactions , 2012, Science Translational Medicine.

[25]  M. Kulldorff,et al.  A conditional maximized sequential probability ratio test for Pharmacovigilance , 2010, Statistics in medicine.

[26]  James G. Scott,et al.  Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem , 2010, 1011.2333.

[27]  A Lawrence Gould,et al.  Practical pharmacovigilance analysis strategies. , 2003, Pharmacoepidemiology and drug safety.

[28]  F Thiessard,et al.  Pharmacovigilance Data Mining With Methods Based on False Discovery Rates: A Comparative Simulation Study , 2010, Clinical pharmacology and therapeutics.

[29]  Johan Hopstadius,et al.  Stratification for Spontaneous Report Databases , 2008, Drug safety.

[30]  Harry Seifert,et al.  Quantitative signal detection for vaccines: Effects of stratification, background and masking on GlaxoSmithKline’s spontaneous reports database , 2009, Human vaccines.

[31]  Kenneth J Rothman,et al.  The reporting odds ratio and its advantages over the proportional reporting ratio , 2004, Pharmacoepidemiology and drug safety.

[32]  Reader Aids,et al.  Estimating the Reliability , 1991 .

[33]  G. D. Dal Pan,et al.  The Future of Population‐Based Postmarket Drug Risk Assessment: A Regulator's Perspective , 2013, Clinical pharmacology and therapeutics.

[34]  C S Lao.  Application of CUSUM technique and beta-binomial model in monitoring adverse drug reactions. , 1997, Journal of biopharmaceutical statistics.

[35]  Chieko Ishiguro,et al.  Analysis of the Factors Influencing the Spontaneous Reporting Frequency of Drug Safety Issues Addressed in the FDA’s Drug Safety Communications, Using FAERS Data , 2014, Pharmaceutical Medicine.

[36]  D. Rubin,et al.  The central role of the propensity score in observational studies for causal effects , 1983 .

[37]  Edsel A. Peña,et al.  POWER-ENHANCED MULTIPLE DECISION FUNCTIONS CONTROLLING FAMILY-WISE ERROR AND FALSE DISCOVERY RATES. , 2009, Annals of statistics.

[38]  David Madigan,et al.  Disproportionality methods for pharmacovigilance in longitudinal observational databases , 2013, Statistical methods in medical research.

[39]  Yanqing Ji,et al.  A Potential Causal Association Mining Algorithm for Screening Adverse Drug Reactions in Postmarketing Surveillance , 2011, IEEE Transactions on Information Technology in Biomedicine.

[40]  M. Kulldorff,et al.  A Maximized Sequential Probability Ratio Test for Drug and Vaccine Safety Surveillance , 2011 .

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

[42]  F Thiessard,et al.  False Discovery Rate Estimation for Frequentist Pharmacovigilance Signal Detection Methods , 2010, Biometrics.

[43]  A Lawrence Gould,et al.  Accounting for Multiplicity in the Evaluation of “Signals” Obtained by Data Mining from Spontaneous Report Adverse Event Databases , 2007, Biometrical journal. Biometrische Zeitschrift.

[44]  William DuMouchel,et al.  Empirical bayes model to combine signals of adverse drug reactions , 2013, KDD.

[45]  A. Bate,et al.  Quantitative signal detection using spontaneous ADR reporting , 2009, Pharmacoepidemiology and drug safety.

[46]  Joseph M. Tonning,et al.  Perspectives on the Use of Data Mining in Pharmacovigilance , 2005, Drug safety.

[47]  Patrick B. Ryan,et al.  Evaluation of alternative standardized terminologies for medical conditions within a network of observational healthcare databases , 2012, J. Biomed. Informatics.

[48]  P. Rothwell,et al.  External validity of randomised controlled trials: “To whom do the results of this trial apply?” , 2005, The Lancet.

[49]  A. Bate,et al.  Extending the methods used to screen the WHO drug safety database towards analysis of complex associations and improved accuracy for rare events , 2006, Statistics in medicine.

[50]  K. Sonawane,et al.  Serious adverse Drug events reported to the Food and Drug Administration (FDA): analysis of the FDA adverse event reporting system (FAERS) 2006-2011 database , 2015 .

[51]  S. Evans,et al.  Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports , 2001, Pharmacoepidemiology and drug safety.