• 1
    Schneeweiss S. Developments in post-marketing comparative effectiveness research. Clin Pharmacol Ther 2007;82:14356.
  • 2
    Roos LL, Sharp SM, Wajda A. Assessing data quality: a computerized approach. Soc Sci Med 1989;28:17582.
  • 3
    Motheral BR, Fairman KA. The use of claims databases for outcomes research: rationale, challenges, and strategies. Clin Ther 1997;19:34666.
  • 4
    Tamblyn R, LaVoie G, Petrella L, Monette J. The use of prescription claims databases in pharmacoepidemiological research: the accuracy and comprehensiveness of the prescription claims database in Quebec. J Clin Epidemiol 1995;48:9991009.
  • 5
    Berger M, Mamdani M, Atkins D, Johnson ML. Good research practices for comparative effectiveness research: defining, reporting and interpreting non-randomized studies of treatment effects using secondary data sources. ISPOR TF Report 2009—Part I.
  • 6
    Johnson ML, Crown W, Martin B, et al. Good research practices for comparative effectiveness research: analytic methods to improve causal inference from non-randomized studies of treatment effects using secondary data sources. ISPOR TF Report 2009—Part III.
  • 7
    Schneeweiss S, Avorn J. A review of uses of health care utilization databases for epidemiologic research on therapeutics. J Clin Epidemiol 2005;58:32337.
  • 8
    Wilchesky M, Tamblyn RM, Huang A. Validation of diagnostic codes within medical services claims. J Clin Epidemiol 2004;57:13141.
  • 9
    Erickson SR, Coombs JH, Kirking DM, et al. Compliance from self-reported versus pharmacy claims data with metered-dose inhalers. Ann Pharmacother 2001;35:9971003.
  • 10
    Grymonpre RE, Didur CD, Montgomery PR, et al. Pill count, self-report, and pharmacy claims to measure medication adherence in the elderly. Ann Pharmacother 1998;32:74954.
  • 11
    Quam L, Ellis LB, Venus P, et al. Using claims data for epidemiologic research. The concordance of claims-based criteria with the medical record and patient survey for identifying a hypertensive population. Med Care 1993;31:498507.
  • 12
    Kwon A, Bungay KM, Pei Y, et al. Antidepressant Use: concordance between self-report and claims records. Med Care 2003;41:36874.
  • 13
    McKenzie DA, Semradek J, McFarland BH, et al. The validity of Medicaid pharmacy claims for estimating drug use among elderly nursing home residents: the Oregon experience. J Clin Epidemiol 2000;53:124857.
  • 14
    Kirking DM, Ammann MA, Harrington CA. Comparison of medical records and prescription claims files in documenting prescription medication therapy. J Pharmacoepidemiol 1996;5:315.
  • 15
    King MA, Purdie DM, Roberts MS. Matching prescription claims with medication data for nursing home residents: implications for prescriber feedback, drug utilization studies and selection of prescription claims database. J Clin Epidemiol 2001;54:2029.
  • 16
    Strom BL, Carson JL, Halpern AC, et al. Using a claims database to investigate drug-induced Stevens-Johnson syndrome. Stat Med 1991;10:56576.
  • 17
    Mager DE, Cox ER. Relationship between generic and preferred-brand prescription copayment differentials and generic fill rate. Am J Manag Care 2007;13:34752.
  • 18
    McKnight J, Scott A, Menzies D, et al. A cohort study showed that health insurance databases were accurate to distinguish chronic obstructive pulmonary disease from asthma and classify disease severity. J Clin Epidemiol 2005;58:2068.
  • 19
    Hartzema AG, Perfetto EM. Sources and effects of drug exposure and unintended effect misclassification in pharmacoepidemiologic studies. In: HartzemaAG, PortaMS, TilsonHH, eds. Pharmacoepidemiology (2nd ed.). Cincinnati, OH: Harvey Whitney Books Co., 1991.
  • 20
    Suissa S. Immeasurable time bias in observational studies of drug effects on mortality. Am J Epidemiol 2008;168:32935.
  • 21
    Van Staa TP, Abenhaim L, Leufkens HGM. A study of the effects of exposure misclassification due to the time-window design in pharmacoepidemiologic studies. J Clin Epidemiol 1994;47:1839.
  • 22
    Van Staa TP, Abenhaim L. Utilization dynamic and risk comparisons in studies that use prescription information. Pharmacoepidemiol Drug Saf 1994;3:1917.
  • 23
    Kiyota Y, Schneeweiss S, Glynn RJ, et al. The accuracy of Medicare claims-based diagnosis of acute myocardial infarction: estimating positive predictive value based on review of hospital records. Am Heart J 2004;148:99104.
  • 24
    Lix LM, Yogendran MS, Leslie WD, et al. Using multiple data features improved the validity of osteoporosis case ascertainment from administrative databases. J Clin Epidemiol 2008;61:125060.
  • 25
    Miettinen OS. Confounding and effect modification. Am J Epidemiol 1974;100:3503.
  • 26
    Grayson DA. Confounding confounding. Am J Epidemiol 1987;126:54653.
  • 27
    Weinberg CR. Towards a clearer definition of confounding. Am J Epidemiol 1993;137:18.
  • 28
    Greenland S, Neutra R. Control of confounding in the assessment of medical technology. Int J Epidemiol 1980;9:3617.
  • 29
    Robins JM. Marginal structural models versus structural nested models as tools for causal inference. In: HalloranE, BerryD, eds. Statistical Models in Epidemiology: The Environment and Clinical Trials. New York: Springer-Verlag, 1999.
  • 30
    Siebert U. Comments from the recipient of the award for outstanding short course: causal inference in decision analysis—DAGs as causal roadmaps. Soc Med Dec Mak Newsl 2005;17:910.
  • 31
    Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology 1999;10:3748.
  • 32
    Pearl J. Causality. Cambridge, UK: Cambridge University Press, 2000.
  • 33
    Hernan MA, Brumback B, Robins JM. Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. Epidemiology 2000;11:56170.
  • 34
    Giordano SH, Kuo YF, Duan Z, et al. Limits of observational data in determining outcomes from cancer therapy. Cancer 2008;112:245666. Related Articles, Links.
  • 35
    Maclure M, Schneeweiss S. Causation of bias: the episcope. Epidemiology 2001;12:11422.
  • 36
    MacMahon S, Collins R. Reliable assessment of the effects of treatment on mortality and major morbidity, II: observational studies. Lancet 2001;357:45562.
  • 37
    Perrio M, Waller PC, Shakir SAW. An analysis of the exclusion criteria used in observational pharmacoepidemiological studies. Pharmacoepidemiol Drug Saf 2006;16:32936.
  • 38
    Schneeweiss S, Patrick AR, Sturmer T, et al. Increasing levels of restriction in pharmacoepidemiologic database studies of elderly and comparison with randomized trial results. Med Care 2007;45(Suppl.):S13142.
  • 39
    Ray WA. Evaluating medication effects outside of clinical trials: new-user designs. Am J Epidemiol 2003;158:91520.
  • 40
    Rothman KJ. Epidemiology. An Introduction. New York: Oxford University Press, 2002.
  • 41
    Glynn RJ, Knight EL, Levin R, Avorn J. Paradoxical relations of drug treatment with mortality in older persons. Epidemiology 2001;12:6829.
  • 42
    Redelmeier DA, Tan SH, Booth GL. The treatment of unrelated disorders in patients with chronic medical diseases. N Engl J Med 1998;338:151620.
  • 43
    Glynn RJ, Monane M, Gurwitz JH. Choodnovskiy I. Avorn J. Aging, comorbidity, and reduced rates of drug treatment for diabetes mellitus. J Clin Epidemiol 1999;52:78190.
  • 44
    Petri H, Urquhart J. Channeling bias in the interpretation of drug effects. Stat Med 1991;10:57781.
  • 45
    Sturmer T, Rothman KJ, Glynn RJ. Insights into different results from different causal contrasts in the presence of effect-measure modification. Pharmacoepidemiol Drug Saf 2006;15:698709.
  • 46
    Benner JS, Glynn RJ, Mogun H, et al. Long-term persistence in use of statin therapy in elderly patients. JAMA 2002;288:45561.
  • 47
    Pablos-Mendez A, Barr RG, Shea S. Run-in periods in randomized trials: implications for the application of results in clinical practice. JAMA 1998;279:2225.
  • 48
    Rothwell PM. Subgroup analysis in randomized controlled trials: importance, indications, and interpretation. Lancet 2005;365:17686.