Safety Data Mining: Background and Current Issues

1. Safety Data Mining: Background and Current Issues Ramin Arani, PhD Safety Data Mining Global Biometric Science Bristol-Myers Squibb Company SAMSI: July, 2006

2. Outline • Rationale for Pharmacovigilance • AERS Data Base • Data base issues • Methodologies • BCNN (WHO) • MGPS (FDA) • Summary • Challenges and Opportunities

3. Pharmacovigilance - Rationale Information obtained prior to first marketing is inadequate to cover all aspects of drug safety: • tests in animals are insufficiently predictive of human safety, • in clinical trials patients are selected and limited in number, • conditions of use in trials differ from those in clinical practice, • duration of trials is limited • information about rare but serious adverse reactions, chronic toxicity, use in special groups or drug interactions is often not available.

4. Pharmacovigilance - Rationale Pre Approval Data- Controlled- Limited # Pts- Safety data not mature Post Approval Data- Real life ; uncontrolled- Off label use -Generic • Solicited Safety Data- Unsolicited Safety Data Population Subjects for approval

5. Spontaneous AE Reports • Safety information from clinical trials is incomplete • Few patients -- rare events likely to be missed • Not necessarily ‘real world’ • Need info from post-marketing surveillance & spontaneous reports • Pharmacovigilance by reg. agencies & mfrs carried out. • Long history of research on issue • Finney (MIMed1974, SM1982) Royall (Bcs1971) • Inman (BMedBull1970) Napke (CanPhJ1970)

6. Issues • Incomplete reports of events, not necessarily reactions • How to compute effect magnitude • Many events reported, many drugs reported • Bias & noise in system • Difficult to estimate incidence because no. of pats at risk, duration of exposure seldom reliable • Appropriate use of computerized methods, e.g., supplementing standard pharmacovigilance to identify possible signals sooner -- early warning signal

7. Pharmacovigilance - Definition Safety Signal: Reported information on a possible causal relationship between an adverse event and a drug. PhamacovigilanceSet of methods that aim at identifying and quantitatively assess the risks related to the use of drugs in the entire population, or in specific population subgroups Adverse Drug Reaction A response to a drug which is harmful and unintended, and which occurs at doses normally used.

8. AERS Database • Database Origin 1969 • SRS until 11/1/97; changed to AERS • 3.0 million reports in database • All SRS data migrated into AERS • Contains Drug and "Therapeutic" Biologic Reports • exception = vaccines (VAERS)

10. Source of AERS Reports • Health Professionals, Consumers / Patients • Voluntary : Direct to FDA and/or to Manufacturer • Manufacturers: Regulations for Postmarketing Reporting

11. AERS Limitations • Different populations, Co-morbidities, Co-prescribing, Off-label use, Rare events • Report volume for a drug is affected by, volume of use, publicity, type and severity of the event and other factors, therefore the reporting rate is not a true measure of the rate or the risk • An observed event may be due to the indication for therapy rather than the therapy itself; therefore observed associations should be viewed as signal, and causal conclusions drawn with caution

12. Examples Claritin and arrhythmias (channeling and need for detailed data not in data base) Increased number of reports due to preexisting condition. Selection of high risk patients for the drug deemed safest for them. Prozac and suicide (confounding by indication) Large increase in reports following publicity and stimulated reporting

13. The Pharmacovigilance Process Traditional Methods Data Mining Detect Signals Generate Hypotheses Insight from Outliers Public Health Impact, Benefit/Risk Refute/Verify Type A (Mechanism-based) Estimate Incidence Act Inform Type B (Idiosyncratic) Restrict use/ withdraw Change Label

14. Methodologies

15. Finding “Interestingly Large” Cell Counts in a Massive Frequency Table • Rows and Columns May Have Thousands of Categories • Most Cells Are Empty, even though N++ Is very Large • Only 386K out of 1331K Cells Have Nij > 0 • 174 Drug-Event Combinations Have Nij > 1000

16. Method - Basics Basic idea: Flag when R = a/E(a) is “large” • Endpoint: No of AEs • Most use variations of 2-way table statistics • Some possibilities • Reporting Ratio: E(a) = (a+b)  (a+c)/n • Proportional Reporting Ratio: E(a) = (a+b) c / (c+d) • Odds Ratio: E(a) = b  c / d • OR > PRR > RR when a > E(a)

17. Bayesian Approaches • Two current approaches: DuMouchel & WHO • Both use ratio nij / Eij where nij = no. of reports mentioning both drug i & event j Eij = expected no. of reports of drug i & event j • Both report features of posterior dist’n of ‘information criterion’ ICij = log2 nij / Eij = PRRij • Eij usually computed assuming drug i & event j are mentioned independently • Ratio > 1 (IC > 0)  combination mentioned more often than expected if independent

18. WHO (Bate et al, EurJClPhrm1998) • ‘Bayesian Confidence Neural Network’ (BCNN) Model: • nij = no. reports mentioning both drug i & event j • ni+ = no. reports mentioning drug i • n+j = no. reports mentioning event j Usual Bayesian inferential setup: • Binomial likelihoods for nij, ni+ , n+j • Beta priors for the rate parameters (rij, pi, qj)

19. WHO, cont’d • Uses ‘delta method’ to approximate variance of Qij = ln rij / piqj = ln 2  ICij • However, can calculate exact mean and variance of Qij • WHO measure of importance = E(ICij) - 2 SD(ICij) • Test of signal detection predictive value by analysis of signals 1993-2000: Drug Safety 2000; 23:533-542 • 84% Negative Pred Val, 44% Positive Pred Val • Good filtering strategy for clinical assessment

20. WHO, cont’d • WHO. (Orre et al 2000)

21. WHO, cont’d Let A denote adverse events and D denote the drug. Mutual information I(A,D) is a measure of association

22. DuMouchel (AmStat1999) • Eij known, computed using stratification of database -- ni+(k) = no. reports of drug i in stratum k n+j(k) = no. reports of event j in stratum k N(k) = total reports in stratum k Eij = k ni+(k)n+j(k) / N(k) (E (nij) under independence) • nij ~ Poisson(ij) -- interested in ij = ij/Eij • Prior dist’n for  = mixture of gamma dist’ns: f(; a1, b1, a2, b2, ) =  g(; a1, b1) + (1 – ) g(; a2, b2) where g(; a, b) = b (b)a – 1e-b/(a)

23. DuMouchel, cont’d • Estimate , a1, b1, a2, b2 using Empirical Bayes -- marginal dist’n of nij is mixture of negative binomials • Posterior density of ij also is mixture of gammas • ln2 ij = ICij • Easy to get 5% lower bound (i.e. E(ICij) - 2 SD(ICij) )

24. The control group and the issue of ‘compared to what?’ • Signal strategies, compare • a drug with itself from prior time periods • with other drugs and events • with external data sources of relative drug usage and exposure • Total frequency count for a drug is used as a relative surrogate for external denominator of exposure; for ease of use, quick and efficient; • Analogy to case-control design where cases are specific AE term, controls are other terms, and outcomes are presence or absence of exposure to a specific drug.

25. Other useful metrics and methods • Chi-square statistics • P-value type metric- overly influenced by sample size • Modeling association through directly Multivariate Poisson dist • Incorporation of a prior distribution on some drugs and/or events for which previous information is available - e.g. Liver events or pre-market signals

26. Interpreting the Signal Throughthe Role of Visual Graphics • Four examples of spatial maps that reduce the scores to patterns and user friendly graphs and help to interpret many signals collectively

27. Example 1A spatial map showing the “signal scores” for the most frequently reported events (rows) and drugs (columns) in the database by the intensity of the empirical Bayes signal score (blue color is a stronger signal than purple)

28. Example 2Spatial map showing ‘fingerprints’ of signal scores allowing one to visually compare the complexity of patterns for different drugs and events and to identify positive or negative co-occurrences

29. Example 3Cumulative scores and numbers of reports according to the year when the signal was first detected for selected drugs

30. Example 4Differences in paired male-female signal scores for a specific adverse event across drugs with events reported (red means females greater, green means males greater)

31. Summary • There is NO Golden Standard method for signal detection. • The signals become more stable over time, however there is a limited time window of opportunity for signal detection. • Use Time-slice evolution of signal.-Fluctuation might reveal external risk factors. -Robustness can be assessed. • Consider other endpoint such as time to onset, duration of event, etc. • For spontaneous case reports, the means to improve content is to standardize and improve intake • Data mining likely will generate many false positives and affirmations of what was previously known • Causality assessments should largely be reserved refining important signals

32. Challenges in the future • More real time data analysis • More interactivity ( Visual Data mining, e.g. ggobi ) • Linkage with other data bases to control the bias inherent in data base • Quality control strategies (e.g. Identifying duplicates • Methods to reduce the false positive and negative?