Using Exploratory Visualization in the Analysis of Medical Product Safety in Observational Healthcare Data

  • Patrick Ryan


Increasing the knowledge about the safety of medical products remains a top priority throughout the pharmaceutical development life cycle and particularly after regulatory approval when the products are used in real-world populations. The increasing availability and use of observational healthcare data, such as administrative claims and electronic health records, provides opportunity for generating ­better information to support therapeutic decision-making. Analysis of observational databases is quite different from randomized clinical trials, and offers unique opportunities for exploratory visualization to complement traditional epidemiologic investigations. The Observational Medical Outcomes Partnership was established to conduct methodological research on the appropriate use of observational data to identify and evaluate the effects of medical products in the real world. Several visualization tools were developed throughout the process, which demonstrate the value in combining standardized analytics with interactive graphics. These tools include a patient profile to study longitudinal patterns within clinical observations for a given person; a cohort profile to evaluate collections of patients; a treemap to assess ­disease prevalence across a database; a trellis scatterplot to investigate subgroup differences in drug utilization; a heatmap to support evaluation of high-dimensional confounding adjustment; and a multi-method forest plot to enable sensitivity analyses of estimated effects of drug and outcomes. This chapter illustrates these visualizations through a case study exploring the relationship between ACE inhibitor exposure and the subsequent health event angioedema.


Angiotensin Convert Enzyme Angiotensin Convert Enzyme Inhibitor Electronic Health Record Patient Profile Electronic Health Record System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Agbabiaka TB, Savovic J, Ernst E (2008) Methods for causality assessment of adverse drug reactions: a systematic review. Drug Saf 31(1):21–37PubMedCrossRefGoogle Scholar
  2. ALLHAT (2002) Major outcomes in high-risk hypertensive patients randomized to angiotensin-converting enzyme inhibitor or calcium channel blocker vs diuretic: The Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT). JAMA 288(23):2981–2997CrossRefGoogle Scholar
  3. Almenoff J, Tonning JM, Gould AL et al (2005) Perspectives on the use of data mining in pharmacovigilance. Drug Saf 28(11):981–1007PubMedCrossRefGoogle Scholar
  4. Arimone Y, Miremont-Salame G, Haramburu F et al (2007) Inter-expert agreement of seven criteria in causality assessment of adverse drug reactions. Br J Clin Pharmacol 64(4):482–488PubMedCrossRefGoogle Scholar
  5. Bandekar MS, Anwikar SR, Kshirsagar NA (2010) Quality check of spontaneous adverse drug reaction reporting forms of different countries. Pharmacoepidemiol Drug Saf 19(11):1181–1185PubMedCrossRefGoogle Scholar
  6. Bennett CL, Nebeker JR, Yarnold PR et al (2007) Evaluation of serious adverse drug reactions: a proactive pharmacovigilance program (RADAR) vs safety activities conducted by the Food and Drug Administration and pharmaceutical manufacturers. Arch Intern Med 167(10):1041–1049PubMedCrossRefGoogle Scholar
  7. Berlin JA, Glasser SC, Ellenberg SS (2008) Adverse event detection in drug development: recommendations and obligations beyond phase 3. Am J Public Health 98(8):1366–1371PubMedCrossRefGoogle Scholar
  8. Brown NJ, Ray WA, Snowden M, Griffin MR (1996) Black Americans have an increased rate of angiotensin converting enzyme inhibitor-associated angioedema. Clin Pharmacol Ther 60(1):8–13PubMedCrossRefGoogle Scholar
  9. Chalmers D, Whitehead A, Lawson DH (1992) Postmarketing surveillance of captopril for hypertension. Br J Clin Pharmacol 34(3):215–223PubMedCrossRefGoogle Scholar
  10. Chobanian A (2009) The joint national committee on prevention detection and evaluation of high blood pressure. US Department of Health and Human Services NHLBI, Bethesda, MDGoogle Scholar
  11. Chou R, Helfand M, Carson S (1995) Drug class review on angiotensin converting enzyme inhibitors. Final report. Portland, OR: Oregon Health & Science UniversityGoogle Scholar
  12. DuMouchel W (1999) Bayesian data mining in large frequency tables, with an application to the FDA spontaneous reporting system. Am Statist 53(3):177–189Google Scholar
  13. Foody JM, Mendys PM, Liu LZ, Simpson RJ Jr (2010) The utility of observational studies in clinical decision making: lessons learned from statin trials. Postgrad Med 122(3):222–229PubMedCrossRefGoogle Scholar
  14. Hennessy S (2006) Use of health care databases in pharmacoepidemiology. Basic Clin Pharmacol Toxicol 98(3):311–313PubMedCrossRefGoogle Scholar
  15. Hennessy S, Leonard CE, Palumbo CM, Newcomb C, Bilker WB (2007) Quality of Medicaid and Medicare data obtained through Centers for Medicare and Medicaid Services (CMS). Med Care 45(12):1216–1220PubMedCrossRefGoogle Scholar
  16. Hill AB (1965) The environment and disease: association or causation? Proc R Soc Med 58:295–300PubMedGoogle Scholar
  17. Knapp P, Raynor DK, Berry DC (2004) Comparison of two methods of presenting risk information to patients about the side effects of medicines. Qual Saf Health Care 13(3):176–180PubMedCrossRefGoogle Scholar
  18. Kostis JB, Shelton B, Gosselin G et al (1996) Adverse effects of enalapril in the Studies of Left Ventricular Dysfunction (SOLVD). SOLVD Investigators. Am Heart J 131(2):350–355PubMedCrossRefGoogle Scholar
  19. Lazarou J, Pomeranz BH, Corey PN (1998) Incidence of adverse drug reactions in hospitalized patients: a meta-analysis of prospective studies. JAMA 279(15):1200–1205PubMedCrossRefGoogle Scholar
  20. Lewis JD, Brensinger C (2004) Agreement between GPRD smoking data: a survey of general practitioners and a population-based survey. Pharmacoepidemiol Drug Saf 13(7):437–441PubMedCrossRefGoogle Scholar
  21. Madigan D, Ryan P (2011) What can we really learn from observational studies?: the need for empirical assessment of methodology for active drug safety surveillance and comparative effectiveness research. Epidemiology 22(5):629–631PubMedCrossRefGoogle Scholar
  22. Miller DR, Oliveria SA, Berlowitz DR, Fincke BG, Stang P, Lillienfeld DE (2008) Angioedema incidence in US veterans initiating angiotensin-converting enzyme inhibitors. Hypertension 51(6):1624–1630PubMedCrossRefGoogle Scholar
  23. Norén G, Bate A, Hopstadius J, Star K, Edwards I (2008) Temporal pattern discovery for trends and transient effects: its application to patient records. Paper presented at: Proceeding of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, Las Vegas, Nevada, USAGoogle Scholar
  24. Norris S, Weinstein J, Peterson K, Thakurta S (2010) Drug class review: direct renin inhibitors, angiotensin converting enzyme inhibitors, and angiotensin II receptor blockers. Accessed on Oct 8, 2012, nal-document-display.cfm Accessed Oct 8, 2012.
  25. Overhage JM, Ryan PB, Reich CG, Hartzema AG, Stang PE (2011) Validation of a common data model for active safety surveillance research. J Am Med Inform Assoc 19(1):54–60PubMedCrossRefGoogle Scholar
  26. Public Law 110–85 (2007) Food and Drug Administration Amendments Act of 2007Google Scholar
  27. Papay JI, Clines D, Rafi R et al (2009) Drug-induced liver injury following positive drug rechallenge. Regul Toxicol Pharmacol 54(1):84–90PubMedCrossRefGoogle Scholar
  28. Perrio M, Voss S, Shakir SA (2007) Application of the bradford hill criteria to assess the causality of cisapride-induced arrhythmia: a model for assessing causal association in pharmacovigilance. Drug Saf 30(4):333–346PubMedCrossRefGoogle Scholar
  29. Polinski JM, Schneeweiss S, Levin R, Shrank WH (2009) Completeness of retail pharmacy claims data: implications for pharmacoepidemiologic studies and pharmacy practice in elderly patients. Clin Ther 31(9):2048–2059PubMedCrossRefGoogle Scholar
  30. Racoosin J (2009) FDA’s sentinel initiative—a national strategy for monitoring medical product safety. 2nd Drug Information Association (DIA) Conference on Signal Detection and Data Mining. New York, NYGoogle Scholar
  31. Rockhill B, Newman B, Weinberg C (1998) Use and misuse of population attributable fractions. Am J Public Health 88(1):15–19PubMedCrossRefGoogle Scholar
  32. Rodriguez EM, Staffa JA, Graham DJ (2001) The role of databases in drug postmarketing surveillance. Pharmacoepidemiol Drug Saf 10(5):407–410PubMedCrossRefGoogle Scholar
  33. Rosenbaum P (2002) Observational studies. Springer, New YorkGoogle Scholar
  34. Schneeweiss S (2009) On guidelines for comparative effectiveness research using nonrandomized studies in secondary data sources. Value Health 10 Sep 2009Google Scholar
  35. Schneeweiss S, Rassen JA, Glynn RJ, Avorn J, Mogun H, Brookhart MA (2009) High-dimensional propensity score adjustment in studies of treatment effects using health care claims data. Epidemiology 20(4):512–522PubMedCrossRefGoogle Scholar
  36. Schneeweiss S, Avorn J (2005) A review of uses of health care utilization databases for epidemiologic research on therapeutics. J Clin Epidemiol 58(4):323–337PubMedCrossRefGoogle Scholar
  37. Schneeweiss S, Sturmer T, Maclure M (1997) Case-crossover and case-time-control designs as alternatives in pharmacoepidemiologic research. Pharmacoepidemiol Drug Saf 6(Suppl 3):S51–S59PubMedCrossRefGoogle Scholar
  38. Speirs C, Wagniart F, Poggi L (1998) Perindopril postmarketing surveillance: a 12 month study in 47,351 hypertensive patients. Br J Clin Pharmacol 46(1):63–70PubMedCrossRefGoogle Scholar
  39. Stang PE, Ryan PB, Racoosin JA et al (2010) Advancing the science for active surveillance: rationale and design for the Observational Medical Outcomes Partnership. Ann Intern Med 153(9):600–606PubMedGoogle Scholar
  40. Strom B (2005) Pharmacoepidemiology, 4th edn. Wiley, ChichesterGoogle Scholar
  41. Suissa S, Garbe E (2007) Primer: administrative health databases in observational studies of drug effects—advantages and disadvantages. Nat Clin Pract Rheumatol 3(12):725–732PubMedCrossRefGoogle Scholar
  42. Szarfman A, Machado SG, O’Neill RT (2002) Use of screening algorithms and computer systems to efficiently signal higher-than-expected combinations of drugs and events in the US FDA’s spontaneous reports database. Drug Saf 25(6):381–392PubMedCrossRefGoogle Scholar
  43. US Department of Health and Human Services, Food and Drug Administration (1999) Managing the risks from medical product use: creating a risk management framework US Department of Health and Human Services, Food and Drug Administration, May 1999Google Scholar
  44. Waller PC, Evans SJ (2003) A model for the future conduct of pharmacovigilance. Pharmacoepi­demiol Drug Saf 12(1):17–29PubMedCrossRefGoogle Scholar
  45. Weatherby LB, Nordstrom BL, Fife D, Walker AM (2002) The impact of wording in “Dear doctor” letters and in black box labels. Clin Pharmacol Ther 72(6):735–742PubMedCrossRefGoogle Scholar
  46. Whitaker HJ, Farrington CP, Spiessens B, Musonda P (2006) Tutorial in biostatistics: the self-controlled case series method. Stat Med 25(10):1768–1797PubMedCrossRefGoogle Scholar
  47. Yusuf S, Sleight P, Pogue J, Bosch J, Davies R, Dagenais G (2000) Effects of an angiotensin-converting-enzyme inhibitor, ramipril, on cardiovascular events in high-risk patients. The Heart Outcomes Prevention Evaluation Study Investigators. N Engl J Med 342(3):145–153PubMedCrossRefGoogle Scholar
  48. Zorych I, Madigan D, Ryan P, Bate A (2011) Disproportionality methods for pharmacovigilance in longitudinal observational databases. Stat Methods Med Res (in print)Google Scholar

Copyright information

© Springer Science+Business Media, New York 2012

Authors and Affiliations

  1. 1.Epidemiology AnalyticsJanssen Research and DevelopmentTitusvilleUSA

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