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Research Methods for Pharmacoepidemiology Studies

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Essentials of Clinical Research

Abstract

Pharmacoepidemiology (PE) is the discipline that studies the frequency and distribution of health and disease in human populations, as a result of the use and effects (beneficial and adverse) of drugs. PE uses methods similar to traditional epidemiologic investigation, but applies them to the area of clinical pharmacology. This chapter will review the factors involved in the selection of the type of pharmacoepidemiologic study design, and advantages and disadvantages of these designs. Since other chapters describe randomized clinical trials in detail, we will focus on observational studies.

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References

  1. Strom B, Kimmel S. Textbook of pharmacoepidemiology. Hoboken: Wiley; 2006.

    Book  Google Scholar 

  2. Miller JL. Troglitazone withdrawn from market. Am J Health Syst Pharm. 2000;57:834.

    Google Scholar 

  3. Gale EA. Lessons from the glitazones: a story of drug development. Lancet. 2001;357:1870–5.

    Article  CAS  PubMed  Google Scholar 

  4. Scheen AJ. Thiazolidinediones and liver toxicity. Diabetes Metab. 2001;27:305–13.

    CAS  PubMed  Google Scholar 

  5. Glessner MR, Heller DA. Changes in related drug class utilization after market withdrawal of cisapride. Am J Manag Care. 2002;8:243–50.

    PubMed  Google Scholar 

  6. Griffin JP. Prepulsid withdrawn from UK & US markets. Adverse Drug React Toxicol Rev. 2000;19:177.

    PubMed  Google Scholar 

  7. Graham DJ, Staffa JA, Shatin D, Andrade SE, Schech SD, La Grenade L, et al. Incidence of hospitalized rhabdomyolysis in patients treated with lipid-lowering drugs. JAMA. 2004;292:2585–90.

    Article  CAS  PubMed  Google Scholar 

  8. Piorkowski Jr JD. Bayer’s response to “potential for conflict of interest in the evaluation of suspected adverse drug reactions: use of cerivastatin and risk of rhabdomyolysis”. JAMA. 2004;292:2655–7. discussion 2658–9.

    Article  CAS  PubMed  Google Scholar 

  9. Strom BL. Potential for conflict of interest in the evaluation of suspected adverse drug reactions: a counterpoint. JAMA. 2004;292:2643–6.

    Article  CAS  PubMed  Google Scholar 

  10. Wooltorton E. Bayer pulls cerivastatin (Baycol) from market. Can Med Assoc J. 2001;165:632.

    CAS  Google Scholar 

  11. Juni P, Nartey L, Reichenbach S, Sterchi R, Dieppe PA, Egger M. Risk of cardiovascular events and rofecoxib: cumulative meta-analysis. Lancet. 2004;364:2021–9.

    Article  CAS  PubMed  Google Scholar 

  12. Sibbald B. Rofecoxib (Vioxx) voluntarily withdrawn from market. Can Med Assoc J. 2004;171:1027–8.

    Article  Google Scholar 

  13. Wong M, Chowienczyk P, Kirkham B. Cardiovascular issues of COX-2 inhibitors and NSAIDs. Aust Fam Physician. 2005;34:945–8.

    PubMed  Google Scholar 

  14. Antoniou K, Malamas M, Drosos AA. Clinical pharmacology of celecoxib, a COX-2 selective inhibitor. Expert Opin Pharmacother. 2007;8:1719–32.

    Article  CAS  PubMed  Google Scholar 

  15. Sun SX, Lee KY, Bertram CT, Goldstein JL. Withdrawal of COX-2 selective inhibitors rofecoxib and valdecoxib: impact on NSAID and gastroprotective drug prescribing and utilization. Curr Med Res Opin. 2007;23:1859–66.

    Article  CAS  PubMed  Google Scholar 

  16. Prentice RL, Langer R, Stefanick ML, Howard BV, Pettinger M, Anderson G, et al. Combined postmenopausal hormone therapy and cardiovascular disease: toward resolving the discrepancy between observational studies and the Women’s Health Initiative clinical trial. Am J Epidemiol. 2005;162:404–14.

    Article  PubMed  Google Scholar 

  17. Dubach UC, Rosner B, Sturmer T. An epidemiologic study of abuse of analgesic drugs. Effects of phenacetin and salicylate on mortality and cardiovascular morbidity (1968 to 1987). N Engl J Med. 1991;324:155–60.

    Article  CAS  PubMed  Google Scholar 

  18. Elseviers MM, De Broe ME. A long-term prospective controlled study of analgesic abuse in Belgium. Kidney Int. 1995;48:1912–9.

    Article  CAS  PubMed  Google Scholar 

  19. Morlans M, Laporte JR, Vidal X, Cabeza D, Stolley PD. End-stage renal disease and non-narcotic analgesics: a case-control study. Br J Clin Pharmacol. 1990;30:717–23. PMC1368172.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  20. Murray TG, Stolley PD, Anthony JC, Schinnar R, Hepler-Smith E, Jeffreys JL. Epidemiologic study of regular analgesic use and end-stage renal disease. Arch Intern Med. 1983;143:1687–93.

    Article  CAS  PubMed  Google Scholar 

  21. Perneger TV, Whelton PK, Klag MJ. Risk of kidney failure associated with the use of acetaminophen, aspirin, and nonsteroidal antiinflammatory drugs. N Engl J Med. 1994;331:1675–9.

    Article  CAS  PubMed  Google Scholar 

  22. International Society of Pharmacoepidemiology (ISPE). Guidelines for Good Pharmacoepidemiology Practices (GPP). Pharmacoepidemiol Drug Saf. 2008;17:200–8.

    Article  Google Scholar 

  23. Avorn J. The promise of pharmacoepidemiology in helping clinicians assess drug risk. Circulation. 2013;128:745–8. doi:10.1161/CIRCULATIONAHA.113.003419.

    Article  PubMed  Google Scholar 

  24. Piotrow PT, Kincaid DL, Rani M, Lewis G. Communication for social change. Baltimore: The Rockefeller Foundation/Johns Hopkins Center for Communication Programs; 2002.

    Google Scholar 

  25. ALLHAT Officers and Coordinators for the ALLHAT Collaborative Research Group. The Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial. 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. 2002;288:2981–97.

    Google Scholar 

  26. Pilote L, Abrahamowicz M, Rodrigues E, Eisenberg MJ, Rahme E. Mortality rates in elderly patients who take different angiotensin-converting enzyme inhibitors after acute myocardial infarction: a class effect? Ann Intern Med. 2004;141:102–12.

    Article  PubMed  Google Scholar 

  27. Schneider LS, Tariot PN, Dagerman KS, Davis SM, Hsiao JK, Ismail MS, et al. Effectiveness of atypical antipsychotic drugs in patients with Alzheimer’s disease. N Engl J Med. 2006;355:1525–38.

    Article  CAS  PubMed  Google Scholar 

  28. Schneeweiss S. Developments in post-marketing comparative effectiveness research. Clin Pharmacol Ther. 2007;82:143–56. PMC2905665.

    Article  CAS  PubMed  Google Scholar 

  29. Mellin GW, Katzenstein M. The saga of thalidomide. Neuropathy to embryopathy, with case reports of congenital anomalies. N Engl J Med. 1962;267:1238–44.

    Article  CAS  PubMed  Google Scholar 

  30. Food and Drug Administration. Medwatch Website. www.fda/gov/medwatch. Accessed 20 Aug 2007.

  31. Humphries TJ, Myerson RM, Gifford LM, Aeugle ME, Josie ME, Wood SL, et al. A unique postmarket outpatient surveillance program of cimetidine: report on phase II and final summary. Am J Gastroenterol. 1984;79:593–6.

    CAS  PubMed  Google Scholar 

  32. Stricker BH, Blok AP, Claas FH, Van Parys GE, Desmet VJ. Hepatic injury associated with the use of nitrofurans: a clinicopathological study of 52 reported cases. Hepatology. 1988;8:599–606.

    Article  CAS  PubMed  Google Scholar 

  33. Martin A, Leslie D. Trends in psychotropic medication costs for children and adolescents, 1997–2000. Arch Pediatr Adolesc Med. 2003;157:997–1004.

    Article  PubMed  Google Scholar 

  34. Williams P, Bellantuono C, Fiorio R, Tansella M. Psychotropic drug use in Italy: national trends and regional differences. Psychol Med. 1986;16:841–50.

    Article  CAS  PubMed  Google Scholar 

  35. Paulose-Ram R, Hirsch R, Dillon C, Losonczy K, Cooper M, Ostchega Y. Prescription and non-prescription analgesic use among the US adult population: results from the third National Health and Nutrition Examination Survey (NHANES III). Pharmacoepidemiol Drug Saf. 2003;12:315–26.

    Article  PubMed  Google Scholar 

  36. U.S. Food and Drug Administration. Guidance for industry: good pharmacovigilance practices and pharmacoepidemiologic assessment. March 2005.

    Google Scholar 

  37. Paulose-Ram R, Jonas BS, Orwig D, Safran MA. Prescription psychotropic medication use among the U.S. adult population: results from the third National Health and Nutrition Examination Survey, 1988–1994. J Clin Epidemiol. 2004;57:309–17.

    Article  PubMed  Google Scholar 

  38. Strom B. Study designs available for pharmacoepidemiology studies. In: Pharmacoepidemiology. 3rd ed. Wiley; 2000.

    Google Scholar 

  39. International Agranulocytosis and Aplastic Anemia Study Group. Risks of agranulocytosis and aplastic anemia: a first report of their relation to drug use with special reference to analgesics. JAMA. 1986;256:1749–57.

    Article  Google Scholar 

  40. Wilcox AJ, Baird DD, Weinberg CR, Hornsby PP, Herbst AL. Fertility in men exposed prenatally to diethylstilbestrol. N Engl J Med. 1995;332:1411–6.

    Article  CAS  PubMed  Google Scholar 

  41. Clark DA, Stinson EB, Griepp RB, Schroeder JS, Shumway NE, Harrison DC. Cardiac transplantation in man. VI. Prognosis of patients selected for cardiac transplantation. Ann Intern Med. 1971;75:15–21.

    Article  CAS  PubMed  Google Scholar 

  42. Messmer BJ, Nora JJ, Leachman RD, Cooley DA. Survival-times after cardiac allografts. Lancet. 1969;1:954–6.

    Article  CAS  PubMed  Google Scholar 

  43. Gail MH. Does cardiac transplantation prolong life? A reassessment. Ann Intern Med. 1972;76:815–7.

    Article  CAS  PubMed  Google Scholar 

  44. Donahue JG, Weiss ST, Livingston JM, Goetsch MA, Greineder DK, Platt R. Inhaled steroids and the risk of hospitalization for asthma. JAMA. 1997;277:887–91.

    Article  CAS  PubMed  Google Scholar 

  45. Fan VS, Bryson CL, Curtis JR, Fihn DS, Bridevaux PO, McDonell MD, et al. Inhaled corticosteroids in chronic obstructive pulmonary disease and risk of death and hospitalization: time-dependent analysis. Am J Respir Crit Care Med. 2003;168:1488–94.

    Article  PubMed  Google Scholar 

  46. Kiri VA, Vestbo J, Pride NB, Soriano JB. Inhaled steroids and mortality in COPD: bias from unaccounted immortal time. Eur Respir J. 2004;24:190–1; author reply 191–2.

    Article  CAS  PubMed  Google Scholar 

  47. Mamdani M, Rochon P, Juurlink DN, Anderson GM, Kopp A, Naglie G, et al. Effect of selective cyclooxygenase 2 inhibitors and naproxen on short-term risk of acute myocardial infarction in the elderly. Arch Intern Med. 2003;163:481–6.

    Article  CAS  PubMed  Google Scholar 

  48. Suissa S. Observational studies of inhaled corticosteroids in chronic obstructive pulmonary disease: misconstrued immortal time bias. Am J Respir Crit Care Med. 2006;173:464; author reply 464–5.

    Article  CAS  PubMed  Google Scholar 

  49. Suissa S. Immortal time bias in observational studies of drug effects. Pharmacoepidemiol Drug Saf. 2007;16:241–9.

    Article  PubMed  Google Scholar 

  50. Suissa S. Effectiveness of inhaled corticosteroids in chronic obstructive pulmonary disease: immortal time bias in observational studies. Am J Respir Crit Care Med. 2003;168:49–53.

    Article  PubMed  Google Scholar 

  51. Time-varying explanatory variables. In: Clayton D, Hills M, editors. Statistical models in epidemiology. Oxford: Oxford University Press; 1993. p. 307–18.

    Google Scholar 

  52. Whitaker HJ, Hocine MN, Farrington CP. The methodology of self-controlled case series studies. Stat Methods Med Res. 2009;18:7–26. doi:10.1177/0962280208092342.

    Article  PubMed  Google Scholar 

  53. Sato T. Risk ratio estimation in case-cohort studies. Environ Health Perspect. 1994;102:53–6. PMC1566546.

    Article  PubMed Central  PubMed  Google Scholar 

  54. van der Klauw MM, Stricker BH, Herings RM, Cost WS, Valkenburg HA, Wilson JH. A population based case-cohort study of drug-induced anaphylaxis. Br J Clin Pharmacol. 1993;35:400–8. PMC1381551.

    Article  PubMed Central  PubMed  Google Scholar 

  55. Bernatsky S, Boivin JF, Joseph L, Gordon C, Urowitz M, Gladman D, et al. The relationship between cancer and medication exposures in systemic lupus erythematosus: a case-cohort study. Ann Rheum Dis. 2008;67:74–9.

    Article  CAS  PubMed  Google Scholar 

  56. Maclure M. The case-crossover design: a method for studying transient effects on the risk of acute events. Am J Epidemiol. 1991;133:144–53.

    CAS  PubMed  Google Scholar 

  57. Maclure M, Mittleman MA. Should we use a case-crossover design? Annu Rev Public Health. 2000;21:193–221.

    Article  CAS  PubMed  Google Scholar 

  58. Marshall RJ, Jackson RT. Analysis of case-crossover designs. Stat Med. 1993;12:2333–41.

    Article  CAS  PubMed  Google Scholar 

  59. Donnan PT, Wang J. The case-crossover and case-time-control designs in pharmacoepidemiology. Pharmacoepidemiol Drug Saf. 2001;10:259–62.

    Article  CAS  PubMed  Google Scholar 

  60. Barbone F, McMahon AD, Davey PG, Morris AD, Reid IC, McDevitt DG, et al. Association of road-traffic accidents with benzodiazepine use. Lancet. 1998;352:1331–6.

    Article  CAS  PubMed  Google Scholar 

  61. Handoko KB, Zwart-van Rijkom JE, Hermens WA, Souverein PC, Egberts TC. Changes in medication associated with epilepsy-related hospitalisation: a case-crossover study. Pharmacoepidemiol Drug Saf. 2007;16:189–96.

    Article  CAS  PubMed  Google Scholar 

  62. Greenland S. A unified approach to the analysis of case-distribution (case-only) studies. Stat Med. 1999;18:1–15.

    Article  CAS  PubMed  Google Scholar 

  63. Scneeweiss S, Stϋrmer T, Maclure M. Case-crossover and case = time-control designs as alternatives in pharmacoepidemiologic research. Pharmacoepidemiol Drug Saf. 1997;6:S51–9.

    Article  Google Scholar 

  64. Suissa S. The case-time-control design. Epidemiology. 1995;6:248–53.

    Article  CAS  PubMed  Google Scholar 

  65. Salas M, Hofman A, Stricker BH. Confounding by indication: an example of variation in the use of epidemiologic terminology. Am J Epidemiol. 1999;149:981–3.

    Article  CAS  PubMed  Google Scholar 

  66. Stukel TA, Fisher ES, Wennberg DE, Alter DA, Gottlieb DJ, Vermeulen MJ. Analysis of observational studies in the presence of treatment selection bias: effects of invasive cardiac management on AMI survival using propensity score and instrumental variable methods. JAMA. 2007;297:278–85.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  67. D’Agostino Jr RB. Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat Med. 1998;17:2265–81.

    Article  PubMed  Google Scholar 

  68. Morant SV, Pettitt D, MacDonald TM, Burke TA, Goldstein JL. Application of a propensity score to adjust for channeling bias with NSAIDs. Pharmacoepidemiol Drug Saf. 2004;13:345–53.

    Article  CAS  PubMed  Google Scholar 

  69. Ahmed A, Husain A, Love TE, Gambassi G, Dell’Italia LJ, Francis GS, et al. Heart failure, chronic diuretic use, and increase in mortality and hospitalization: an observational study using propensity score methods. Eur Heart J. 2006;27:1431–9. PMC2443408.

    Article  PubMed Central  PubMed  Google Scholar 

  70. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70:41–55.

    Article  Google Scholar 

  71. Rosenbaum PR, Rubin DB. Reducing bias in observational studies using subclassification on the propensity score. J Am Stat Assoc. 1984;79:516–24.

    Article  Google Scholar 

  72. Austin PC, Mamdani MM, Stukel TA, Anderson GM, Tu JV. The use of the propensity score for estimating treatment effects: administrative versus clinical data. Stat Med. 2005;24:1563–78.

    Article  PubMed  Google Scholar 

  73. Braitman LE, Rosenbaum PR. Rare outcomes, common treatments: analytic strategies using propensity scores. Ann Intern Med. 2002;137:693–5.

    Article  PubMed  Google Scholar 

  74. Harrell FE. Regression modeling strategies with applications to linear models, logistic regression and survival analysis. New York: Springer; 2001.

    Google Scholar 

  75. McClellan M, McNeil BJ, Newhouse JP. Does more intensive treatment of acute myocardial infarction in the elderly reduce mortality? Analysis using instrumental variables. JAMA. 1994;272:859–66.

    Article  CAS  PubMed  Google Scholar 

  76. Newhouse JP, McClellan M. Econometrics in outcomes research: the use of instrumental variables. Annu Rev Public Health. 1998;19:17–34.

    Article  CAS  PubMed  Google Scholar 

  77. Harris KM, Remler DK. Who is the marginal patient? Understanding instrumental variables estimates of treatment effects. Health Serv Res. 1998;33:1337–60. PMC1070319.

    CAS  PubMed Central  PubMed  Google Scholar 

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Correspondence to Maribel Salas M.D., D.Sc., M.Sc. .

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Salas, M., Stricker, B. (2014). Research Methods for Pharmacoepidemiology Studies. In: Glasser, S. (eds) Essentials of Clinical Research. Springer, Cham. https://doi.org/10.1007/978-3-319-05470-4_12

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