Detecting Multi-Item Associations and Temporal Trends Using the WebVDME/MGPS Application

1. Detecting Multi-Item Associations and Temporal Trends Using the WebVDME/MGPS Application DIMACS Tutorial on Statistical and Other Analytic Health Surveillance Methods 18 June 2003 Richard Ferris

2. Pharmaceutical post-marketing surveillance • Companies and regulatory agencies collect databases of spontaneous adverse reaction reports • Relevant exposure data not readily available (the “denominator problem”) • Can drug-event combinations of potential interest be identified from internal evidence alone? • Approach: • Use an internally defined “denominator” • Construct set of “expected” counts using a stratified independence model

3. Computation of Expected Counts • The expected count for a given drug-event combination is determined by the overall count for the particular drug (across all events) and the overall count of the particular event (across all drugs) • For example, if 2% of all reports have PROZAC as a drug, and 3% of all reports have RASH as an event, then one would expect that 0.06% (0.02*0.03) of the reports will include this combination (PROZAC in combination with RASH) • (MGPS carries out this computation separately for each distinct “stratum” and sums the strata-specific expected counts to obtain an overall expected count)

4. Comparing Observed and Expected Counts:Relative Reporting Rate • Relative Report Rate (RR):RRij = Nij / Eij • Easy to interpret, easy to compute • Statistically unstable if N is small or E is very small • The following all have RR = 100: • N = 1000, E = 10 • N = 100, E = 1 • N = 10, E = 0.1 • N = 1, E = 0.01

5. Comparing Observed and Expected Counts:Statistical Significance • What is the probability that Nij would be observed by chance (“sampling error”) when expected value is Eij ? (p-value for testing a null hypothesis) • Harder to interpret (not expressed in same units as RR) • Results in computation of absurdly small probabilities that have no meaning • N=100, E=1 produces 10-158 ! • Small RR can be very significant (small p-value) when sample size is very large: • N = 2000, E = 1000, RR = 2 is more “significant” than • N = 10, E = 0.1, RR =100

6. Comparing Observed and Expected Counts:Empirical Bayes Multi-Item Gamma Poisson Shrinker • Try for best of both previous approaches • interpretability of relative rate • adjust properly for sampling variation • Focus on the distribution across the set of drug-event combinations of the ratios: • Estimate lij = mij /Eij , where Nij ~ Poisson(mij ) • Fit a parameterized “prior distribution” function (mixture of two gamma functions) to the empirical distribution of the l’s • Find posterior distribution of l after observing N = some value n • Use this to obtain posterior estimate of expectation value of l given observation of Nij • This posterior estimate is what we call EBGM (Empirical Bayes Geometric Mean); also get lower and upper 95% confidence bounds (EB05, EB95). • EBGM is termed the “shrinkage estimate” for RR

7. Multi-Item Associationsvs. Pairwise Associations • Consider the case of an item triplet; e.g. 2 drugs and an event • RRijk = Nijk/Eijk where Eijkis based on independence model • EBGMijk = shrinkage estimate of RRijk • Suppose a particular itemset (drug A, drug B, event C = kidney failure) is unusually frequent (EBGM for the triplet is >> 2) • Important to ask: • Is this merely the result of one or more of the pairs (AB, AC, BC) being unusually frequent? OR • Is this a drug-drug interaction • Compare Empirical Bayes estimate of the frequency count of the triplet to the prediction from the all-2-factor log-linear model • EXCESS2 = (EBGM * E ) – EAll2F • E is the expected count from independence • Computation of EAll2F uses shrinkage estimates of pairwise counts • EXCESS2 is an estimate of how many “extra” cases were observed over what was expected using the all-2-factor model • Alternate approach: Define Eijkfrom predictions of all-2-factor model in which case resulting EBGM directly measures divergence of observed count from all-2-factor prediction

8. Health Authority Adoption of Signal Detection Technologies • FDA • CDER: • Experimented in Office of Biostatistics with GPS for several years • Validated GPS • Moving to production • Have published data mining results on internal web for almost all products • CBER: • initial GPS implementation (VAERS) • CRADA between Lincoln and FDA to further develop methodology and tools • CDC • Collaborative GPS methodology development with FDA • Includes simulation capability • WHO Uppsala Monitoring Centre • Production safety signal generation mechanism using BCPNN

9. FDA/GPS Validation Activities • Positive controls • Examine data mining results for drug-event combinations corresponding to known “labeled” adverse reactions • Negative controls • Examine data mining results for several drugs (with differing safety profiles) given for the same indication • “Roll back” database in time to determine when method would have provided first signal

10. Databases of Spontaneous AE Reports • FDA Spontaneous Report System (SRS) • Post-Marketing Surveillance of all Drugs since 1969 • Dates from mid-60’s thru 1997 • 1.5 Million Reports • Encoded in COSTART • FDA Adverse Event Reporting System (AERS) • US cases, serious unlabeled events from all manufacturers. • All products sold in the US ~5000 Rx’s • Replaced SRS in 1997 • Reactions coded as MedDRA PTs • Quarterly Updates, 4-6 month delay • Drugs are Verbatim • Includes initial and some follow-up reports • Includes Demographics, Reactions, Drugs, Outcomes, etc. • FDA/CDC Vaccine Adverse Events (VAERS) • Stricter Laws for Vaccine Adverse Event Reporting

11. Signal Detection DemonstrationUsing VAERS Data

12. “Significant” EBGM and even extremely conservative EB05 with small N

13. Simple Rankings by Signal Strength

14. Evolution of Signals Over Time

15. Multi-Symptom Syndromes (Higher Order Associations)

16. The “Serotonin Syndrome” • Could MGPS be used to identify unknown syndromes? • Try mining the AERS data for “significant” event triples using a known syndrome. • "The symptoms of the serotonin syndrome are: euphoria, drowsiness, sustained rapid eye movement, overreaction of the reflexes, rapid muscle contraction and relaxation in the ankle causing abnormal movements of the foot, clumsiness, restlessness, feeling drunk and dizzy, muscle contraction and relaxation in the jaw, sweating, intoxication, muscle twitching, rigidity, high body temperature, mental status changes were frequent (including confusion and hypomania - a "happy drunk" state), shivering, diarrhea, loss of consciousness and death. (The Serotonin Syndrome, AM J PSYCHIATRY, June 1991)

18. Using Simulation to Testthe Signal Detection Process

19. Interpreting Simulation Parameters Outcome • As R  P and (Q-R)  (1-P)  => “No Signal” • As R  P and (Q-R) << (1-P) => “Strong Signal” • When R << P and (Q-R)(1-P) => “No Signal” • When R << P and (Q-R) << (1-P) => “Rare event” Yes No Yes R P-R P Exposure No Q-R 1-P-Q+R 1-P 1 Q 1-Q

20. Using Simulation to Create a Receiver Operating Characteristic (ROC) Curve for EBGM • An ROC curve displays the true-positive rate (sensitivity) versus the false-positive rate(1 – specificity) for a statistic • Ran a 20 iteration simulation using P = 0.003Q = 0.001 and R = 0.00003 (RR = 10) to check the true-positive rate • Ran a 20 iteration simulation using P = 0.003,Q = 0.001 and R = 0.0003 (RR = 1) to check the false-positive rate

21. ROC Curve Based on Simulated Injection of Signals

22. Simulating a Rare Event • Sample 100,000 records from VAERS data • Set P = 0.003, Q = 0.001, R = 0.00003 • Iterate 20 Monte Carlo simulations • Expect (on average): • 0.003 x 100,000 = 300 “Rare Exposures” • 0.001 x 100,000 = 100 “Rare Outcomes” • 0.00003 x 100,000 = 3 “Rare Exposure + Rare Outcome” combinations • E = (300 x 100) / 100,000 = 0.3 • RR = 3/ 0.3 = 10

23. Base Simulation on VAERS Data

24. Sample Cases From VAERS

25. Sample 100,000 Cases

26. P = 0.003 Q = 0.001 R = 0.00003

27. 20 Monte Carlo Iterations

28. RareExposure Expected N = 300

29. RareOutcome Expected N = 100

30. RareExposure + RareOutcome Expected N = 3Expected RR = 10

31. Technical Details • William DuMouchel. Bayesian Data Mining in Large Frequency Tables (with Discussion). The American Statistician (1999) pp 177-190. • William Dumouchel and Daryl Pregibon. Empirical Bayes Screening for Multi-Item Associations. Proceedings of KDD 2001.

32. Methodology History and Key Contributors • Stephan Evans • MCA, UK • Proportional reporting ratio (PRR) with Chi 2 analyses • Simple, highly intuitive, can be calculated by hand • Bate, Lindquist, Edwards et. al. • WHO Uppsala Monitoring Centre • Bayesian neural network method for adverse drug reaction signal generation • Ana Szarfman, FDA (CDER) and Bill DuMouchel (ATT) • Empiric Bayes, more robust than PRR for small n • MGPS method: statistical parameter is EGBM • William DuMouchel. Bayesian Data Mining in Large Frequency Tables (with Discussion). The American Statistician (1999) pp 177-190. • William Dumouchel and Daryl Pregibon. Empirical Bayes Screening for Multi-Item Associations. Proceedings of KDD 2001. • Multidimensional analyses possible • Interactions, gender and other demographic associates, syndrome identification • Can directly compare EBGM values of different drugs, as well as for a specific drug

33. Key Contributors (continued) • WHO Collaborating Center for Internat’l Drug Monitoring: M Lindquist, M Stahl, A. Bate, R. Edwards, RH Meyboom. • Bayesian confidence propagation neural network (BCPNN) . Information Component (IC) statistic is the measure of the strength of D:E relationship • Iterative approach • L. Gould . Comparison and refinement of Bayesian approaches for evaluating spontaneous reports of ADRs. DIA Annual meeting, July 2001, (Denver) • EB vs BCPNN = similar results • Thakrar, BT, Blesch, KS, Sacks, ST, Wilcock, K (2001) • (ISPE, Pharmacoepid. & Drug Safety 10), • PRR vs. EB= similar sensitivity, EB better at ranking events based on small N.