Abstract
Pharmacoepidemiology is the study of the utilization and effects of drugs in clinical and population settings, and the outcomes of drug therapy. The growing trend of recording computerized data that will increasingly be automated into health care delivery is making the use of large datasets more and more common in pharmacoepidemiologic research. Most retrospective databases offer large populations and longer observation periods with real-world practice and can answer a variety of research questions quickly and cost-effectively. Observational studies, specifically using large databases, can complement findings from randomized clinical trials (RCTs) by assessing treatment effectiveness in patients encountered in daily clinical practice, although they are more exposed to bias and certainly are lower on the hierarchy of evidence than RCTs. Furthermore, careful defining of the research question with appropriate design and application of advanced statistical techniques, e.g., propensity-score analysis or marginal structural models, can yield findings with validity and improve causal inference of treatment effects. Some existing guidelines for comparative effectiveness help decision makers to evaluate the quality of observational studies comparing the effectiveness of various medical products and services. Thus, the trend for utilization of databases for pharmacoepidemiology will continue to grow in coming years.
Similar content being viewed by others
References
Guidelines for good pharmacoepidemiology practices (GPP) (2008) Pharmacoepidemiol Drug Saf 17 (2):200–208
Silverman SL (2009) From randomized controlled trials to observational studies. Am J Med 122(2):114–120
Wong IC, Murray ML (2005) The potential of UK clinical databases in enhancing paediatric medication research. Br J Clin Pharmacol 59(6):750–755
Dreyer NA, Schneeweiss S, McNeil BJ, Berger ML, Walker AM, Ollendorf DA, Gliklich RE (2010) GRACE principles: recognizing high-quality observational studies of comparative effectiveness. Am J Manag Care 16(6):467–471
Trojano M, Pellegrini F, Paolicelli D, Fuiani A, Di Renzo V (2009) Observational studies: propensity score analysis of non-randomized data. Int MS J 16(3):90–97
Concato J, Shah N, Horwitz RI (2000) Randomized, controlled trials, observational studies, and the hierarchy of research designs. N Engl J Med 342(25):1887–1892
Berger ML, Mamdani M, Atkins D, Johnson ML (2009) Good research practices for comparative effectiveness research: defining, reporting and interpreting nonrandomized studies of treatment effects using secondary data sources: the ISPOR Good Research Practices for Retrospective Database Analysis Task Force Report—part I. Value Health 12(8):1044–1052
Cox E, Martin BC, Van Staa T, Garbe E, Siebert U, Johnson ML (2009) Good research practices for comparative effectiveness research: approaches to mitigate bias and confounding in the design of nonrandomized studies of treatment effects using secondary data sources: the International Society for Pharmacoeconomics and Outcomes Research Good Research Practices for Retrospective Database Analysis Task Force Report—part I. Value Health 12(8):1053–1061
Johnson ML, Crown W, Martin BC, Dormuth CR, Siebert U (2009) Good research practices for comparative effectiveness research: analytic methods to improve causal inference from nonrandomized studies of treatment effects using secondary data sources: the ISPOR Good Research Practices for Retrospective Database Analysis Task Force Report—part II. Value Health 12(8):1062–1073
Schneeweiss S, Avorn J (2005) A review of uses of health care utilization databases for epidemiologic research on therapeutics. J Clin Epidemiol 58(4):323–337
Hennessy S (2006) Use of health care databases in pharmacoepidemiology. Basic Clin Pharmacol Toxicol 98(3):311–313
Strom BL, Carson JL, Halpern AC, Schinnar R, Snyder ES, Stolley PD, Shaw M, Tilson HH, Joseph M, Dai WS et al (1991) Using a claims database to investigate drug-induced Stevens-Johnson syndrome. Stat Med 10(4):565–576
Freedman AN, Sansbury LB, Figg WD, Potosky AL, Weiss Smith SR, Khoury MJ, Nelson SA, Weinshilboum RM, Ratain MJ, McLeod HL, Epstein RS, Ginsburg GS, Schilsky RL, Liu G, Flockhart DA, Ulrich CM, Davis RL, Lesko LJ, Zineh I, Randhawa G, Ambrosone CB, Relling MV, Rothman N, Xie H, Spitz MR, Ballard-Barbash R, Doroshow JH, Minasian LM (2010) Cancer pharmacogenomics and pharmacoepidemiology: setting a research agenda to accelerate translation. J Natl Cancer Inst 102:1–8
Arnold RG, Kotsanos JG (1999) Panel 3: methodological issues in conducting pharmacoeconomic evaluations—retrospective and claims database studies. Value Health 2(2):82–87
Sorensen HT, Sabroe S, Olsen J (1996) A framework for evaluation of secondary data sources for epidemiological research. Int J Epidemiol 25(2):435–442
Motheral B, Brooks J, Clark MA, Crown WH, Davey P, Hutchins D, Martin BC, Stang P (2003) A checklist for retrospective database studies—report of the ISPOR Task Force on Retrospective Databases. Value Health 6(2):90–97
Iezzoni LI (1997) Assessing quality using administrative data. Ann Intern Med 127(8 Pt 2):666–674
Glynn RJ, Schneeweiss S, Sturmer T (2006) Indications for propensity scores and review of their use in pharmacoepidemiology. Basic Clin Pharmacol Toxicol 98(3):253–259
D'Agostino RB Jr (1998) Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat Med 17(19):2265–2281
Rubin DB (1997) Estimating causal effects from large data sets using propensity scores. Ann Intern Med 127(8 Pt 2):757–763
Weitzen S, Lapane KL, Toledano AY, Hume AL, Mor V (2004) Principles for modeling propensity scores in medical research: a systematic literature review. Pharmacoepidemiol Drug Saf 13(12):841–853
Shah BR, Laupacis A, Hux JE, Austin PC (2005) Propensity score methods gave similar results to traditional regression modeling in observational studies: a systematic review. J Clin Epidemiol 58(6):550–559
McWilliams JM, Meara E, Zaslavsky AM, Ayanian JZ (2007) Use of health services by previously uninsured Medicare beneficiaries. N Engl J Med 357(2):143–153
Fu AZ, Liu GG, Christensen DB, Hansen RA (2007) Effect of second-generation antidepressants on mania- and depression-related visits in adults with bipolar disorder: a retrospective study. Value Health 10(2):128–136
Linden A, Adams JL (2010) Evaluating health management programmes over time: application of propensity score-based weighting to longitudinal data. J Eval Clin Pract 16(1):180–185
Leslie S, Thiebaud P (2007) Using propensity score to adjust for treatment selection bias. SAS global forum 2007 paper 184. http://www2.sas.com/proceedings/forum2007/184-2007.pdf. Accessed 16 March 2011
Patel BV, Leslie RS, Thiebaud P, Nichol MB, Tang SS, Solomon H, Honda D, Foody JM (2008) Adherence with single-pill amlodipine/atorvastatin vs a two-pill regimen. Vasc Health Risk Manag 4(3):673–681
Nishida Y, Takahashi Y, Nakayama T, Soma M, Kitamura N, Asai S (2010) Effect of candesartan monotherapy on lipid metabolism in patients with hypertension: a retrospective longitudinal survey using data from electronic medical records. Cardiovasc Diabetol 9:38
Kitamura N, Takahashi Y, Yamadate S, Asai S (2007) Angiotensin II receptor blockers decreased blood glucose levels: a longitudinal survey using data from electronic medical records. Cardiovasc Diabetol 6:26
Schneeweiss S (2007) Developments in post-marketing comparative effectiveness research. Clin Pharmacol Ther 82(2):143–156
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Takahashi, Y., Nishida, Y. & Asai, S. Utilization of health care databases for pharmacoepidemiology. Eur J Clin Pharmacol 68, 123–129 (2012). https://doi.org/10.1007/s00228-011-1088-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00228-011-1088-2