Analysis of Safety Data Is More Enough?

1. Analysis of Safety Data Is More Enough? Marc Andersen Senior Statistician

2. Outline / Conclusion Analysis - For what purpose • Different usages Approaches and issues • Best practice to be developed Is more enough? • It's needed • Consolidation of methods Some slides will be skipped, but present in handout *: examples will be shown separately

3. Safety Analysis - For what • Annual Report (AR) • Investigator’s Brochure (IB) • Integrated Safety Summary (ISS, eCTD) • For planning of next study (protocol rationale) • Data Monitoring Commitees (DMC, DSMB) • Assessing spontaneous reports • Publications

4. Annual Report (AR) and Investigators Brochure (IB) Used for different purposes: • IND's: • 21 CFR 312.33 • http://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/cfrsearch.cfm • IRB's: • ICH E6 section 8.3.19 (note wording: annual or interim) • http://www.ich.org • Investigator’s Brochure (IB) • ICH E6 section 7 • http://www.ich.org

5. Integrated Safety Summary (ISS) • M4E: The CTD - Efficacy. U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER), Center for Biologics Evaluation and Research (CBER), August 2001. • http://www.fda.gov/cder/guidance/4539E.pdf • Guideline for the Format and Content of the Clinical and Statistical Sections of an Application. Center for Drug Evaluation and Research, Food and Drug Administration, Department of Health and Human Services, July 1988. • http://www.fda.gov/cder/guidance/statnda.pdf • Conducting a Clinical Safety Review of a New Product Application and Preparing a Report on the Review. U.S. Department of Health and Human Services Food and Drug Administration Center for Drug Evaluation and Research (CDER) • http://www.fda.gov/cder/guidance/3580fnl.pdf • Common Terminology Criteria for Adverse Events v3.0 (CTCAE), Cancer Therapy Evaluation Program, Common Terminology Criteria for Adverse Events, Version 3.0, DCTD, NCI, NIH, DHHS. • http://ctep.cancer.gov/forms/CTCAEv3.pdf May claim that ISS corresponds to eCTD section 2.7.4

6. Tips • Get the timeslines • More difficult to identify that imagined (!) • There is no shortcut • by study and integrated output are both needed • Keep the perspective • analysis on demand; aim for just in time • Safety is looking for the unknown :-) • so they will probably ask for more

7. What is the purpose of analysis • Exploratory – is it safe … • Signal detection – is there something … • Confirmatory – there is nothing … • Regulatory request – somebody else had … Absense of Evidence is not Evidence of Absence Alderson, BMJ editorial, 2004, http://bmj.bmjjournals.com/cgi/reprint/328/7438/476 Altman, Bland, BMJ Letter, 2004, http://bmj.bmjjournals.com/cgi/content/full/328/7446 /1016-b, Altman, Bland, BMJ Statistics notes 1995, http://bmj.bmjjournals.com/cgi/reprint/311/7003/485 Attributed to Carl Sagan http://www.quotationspage.com/quote/37901.html

8. Regulatory approachSignal detection Different Approaches • Proportional Reporting Ratio (PRR) • Reporting Odds Ratio • Pharmacoepidemiology and Drug Safety Volume 13, Issue 8: • http://www3.interscience.wiley.com/cgi-bin/jissue/109593697 • Ronald D. Mann: Assessments of disproportionality • Kenneth J. Rothman, Stephan Lanes, Susan T. Sacks: The reporting odds ratio and its advantages over the proportional reporting ratio • Patrick Waller, Eugène van Puijenbroek, Antoine Egberts, Stephen Evans: The reporting odds ratio versus the proportional reporting ratio: deuce • Bayesian Confidence Propagation Neural Network (BCPNN) • Lindquist M, Ståhl M, Bate A, Edwards IR, Meyboom RHB. A retrospective evaluation of a data mining approach to aid finding new adverse drug reaction signals in the WHO international database. Drug Safety 2000;23:533-42 Lareb is the centre for knowledge about adverse drug reactions in the Netherlands. The government has instructed us to register and analyse the adverse drug reactions from drugs and vaccines: • E.P. van Puijenbroek: Quantitative Signal Detection in Pharmacovigilance (2001) • http://www.lareb.nl/documents/thesis_evp_1059.pdf • Hauben M, Madigan D, Gerrits CM, Walsh L, van Puijenbroek EP: The role of data mining in pharmacovigilance (2005) • http://www.lareb.nl/documents/eods2005_2057.pdf Google search: adverse drug reactions data mining

9. Before anything: Integration ... • Why: What is the purpose? • Several study versus one study • Subgroup analysis • Integrated data may be more heterogenous • Alternative • extract results from study reports and pool (using excel) • Claim: integration is always nescessary!

10. To integrate without trouble Consider implications for integration • At protocol stage, • At database design stage (e.g. lab identification in database) • View it as meta analysis problem • Which treatment groups can be pooled: • Context depending (e.g. by active, placebo; by dose, placebo)

11. Integration - ideal versus reality Ideal: SAS set statement combines the study datasets: data lab; set lab001 lab002 lab003 ...; run; • Be pragmatic * • no formats, all formatted values to characters • Make reporting programs general • simple to make a summary for any subpopulation, when safety asks • Tip: Data definition tables by study * • definition and automation in SAS http://www.lexjansen.com/pharmasug/2002/proceed/fdacomp/fda04.pdf

12. When integrating Get an overview early • Study overview in calendar time * Overview of study characteristics * • Study characteristics • Timing of treatment administration by study • Dose in single dose / multiple dose studies • Study Populations • Study Periods

13. Coding / Standardisation • AE coding: MEDRA • http://www.meddramsso.com • hierachy SOC, HLGT, HLT, PT and LLT • special searches categories (SSC) • Jürgen Kübler et all: "Adverse Event Analysis and MedDRA: Business as Usual or Challenge?" Drug Information Journal, Vol. 39, pp. 63-72, 2005 http://www.diahome.org/NR/rdonlyres/ECDCE5F2-B5D5-4313-B64E-8026AAB684F6/0/DIJ39_1_63.pdf • Note: WHO-ART: Adverse Reaction Terminology http://www.umc-products.com/DynPage.aspx?id=4918&mn=1107 • CM coding: WHO Drug Dictionary http://www.umc-products.com/DynPage.aspx?id=2829&mn=1107 • Issue: version of dictionaries

14. Structure: Domains FDA: Regulatory Submissions in Electronic Format; New Drug Applications (Issued 1/1999, Posted 1/27/1999) (IT3) http://www.fda.gov/cder/guidance/2353fnl.pdf (not recent, but still relevant) • For single study datasets: • Demographics, Inclusion criteria, Exclusion criteria, Concomitant medication, Medical history, Drug exposure, Disposition, Efficacy results, Human pharmacology and bioavailability/bioequivalence data, Microbiology data, Adverse Events, Lab - chemistry, Lab - hematology, Lab - urinalysis, ECG, Vital signs, Physical examination CDISC, CDISC Submissions Data Model Version 2.0, http://www.cdisc.org/publications/index.html • Domains for DEMO, AE, CONMEDS, DISPOSIT, ECG, EXPOSURE, LAB, PE, VITALS Question: when is domain = dataset?

15. Integration - AE Has the same conventions been used in all studies • Symptoms not recorded as AE same across studies? • Does out of range lab parameters lead to AE registration • Relevant of subpopulations may be domain specific • One measurement may only be present in a few studies • Only pregnancy test for ... • When AE intensity changes, how is that captured: new event or ... • Is intensity registered using the same approach? • mild/moderate/severe vs CTCAE Grade 1-5

16. AE data are standardised • Typical items • Investigator term • Start (date or date and time) • Stop (duration) • Intensity • Severity • Ongoing (ongoing as of when) • Action taken • Relatedness

17. AE and Serious adverse events SAE reconciliation • Clinical database must be consistent with Safety database • Safety has the final word Tip: usefull to list CIOMs numbers with AE’s *

18. Integration - lab Lab parameters • obtain documentation for each study: • parameter names, • lab analysis method, • units (conversion !), • reference ranges

19. Tips for analyses • Do the simple stuff • as it was a study, just with more treatment arms • QC approach: number of records by study • Agree on intervals for grouping continous variables: • exposure • Age • Identify the sub population

20. Summaries patient level • This is covered for eCTD • 2.7.4.1.2 Overall Extent of Exposure • 2.7.4.1.3 Demographic and Other Characteristics of Study Population • Treatments • Follow-up status / Vital status • Exposure • Discontinuation (treatment, study)

21. Presenting AE analysis • What is the question? • Number of patients with event relative to number of patients treated (frequency and proportion)? • AE’s where proportion is > 5%, 1% etc? In which group? • Point Prevalence * • Incidence • Duration • Tables or Graphs • (usefull with row x columns layout) * • Which AE’s occurs at the same time? Question: Cross over studies?

22. AE tables lessons • Ask! • Identify groups of AE's to tabulate • AE's in tables distinguish between N and E • Full MEDRA hierachy is relevant * • There will be lot of columns or two *

23. Relationships: continous variable and AE time to event • Table: grouped value before versus any AE (+/-) by time interval • Graphical - AE start or measurement date on x-axis, cont. var on x axis • Time to event analysis • Proportional hazards model (time dependent predictor straightforward) Issue: multiple events per patient; first event only?

24. Challenges / further analyses • Which level for analysis of AE's: • SOC, HLGT, HLT, PT, LLT, SSC, selected AE's? • Model occurrence of AE's • Pharmacodynamic analysis (PD) modelling for all lab parameters • Simple PD model may work

25. Questions