The importance and implications of comparator selection in pharmacoepidemiologic research
Purpose of review
Pharmacoepidemiologic studies employing large databases are critical to evaluating the effectiveness and safety of drug exposures in large and diverse populations. Because treatment is not randomized, researchers must select a relevant comparison group for the treatment of interest. The comparator group can consist of individuals initiating: (1) a similarly indicated treatment (active comparator), (2) a treatment used for a different indication (inactive comparator), or (3) no particular treatment (non-initiators). Herein, we review recent literature and describe considerations and implications of comparator selection in pharmacoepidemiologic studies.
Comparator selection depends on the scientific question and feasibility constraints. Because pharmacoepidemiologic studies rely on the choice to initiate or not initiate a specific treatment, rather than randomization, they are at-risk for confounding related to the comparator choice including by indication, disease severity, and frailty. We describe forms of confounding specific to pharmacoepidemiologic studies and discuss each comparator along with informative examples and a case study. We provide commentary on potential issues relevant to comparator selection in each study, highlighting the importance of understanding the population in whom the treatment is given and how patient characteristics are associated with the outcome.
Advanced statistical techniques may be insufficient for reducing confounding in observational studies. Evaluating the extent to which comparator selection may mitigate or induce systematic bias is a critical component of pharmacoepidemiologic studies.
KeywordsPharmacoepidemiology Comparator selection New user Confounding Detection bias
Compliance with Ethical Standards
Conflict of Interest
Monica D’Arcy declares no conflict of interest; Til Stürmer reports grants from the National Institute on Aging, during the conduct of the study, grants from Astrazeneca and Amgen, outside the submitted work, membership (Center for Pharmacoepidemiology) of GlaxoSmithKline, UCB BioSciences, Merck, and Shire, outside the submitted work, and stock in Novartis, Roche, BASF, AstraZeneca, and NovoNordisk; Jennifer L. Lund reports grants from PhRMA Foundation, outside the submitted work; Dr. Lund’s husband is a full-time, paid employee of GlaxoSmithKline.
Human and Animal Rights and Informed Consent
This article does not contain any studies with human or animal subjects performed by any of the authors.
Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major Importance
- 3.•• Huitfeldt A, Hernan MA, Kalager M, Robins JM. Comparative Effectiveness Research Using Observational Data: Active Comparators to Emulate Target Trials with Inactive Comparators. EGEMS (Wash DC). 2016;4(1):1234. https://doi.org/10.13063/2327-9214.1234. Clearly describes how to think about and design a study using observational data to emulate a target trial. There are very intuitive diagrams in this paper. CrossRefGoogle Scholar
- 5.Graham DJ, Ouellet-Hellstrom R, MaCurdy TE, Ali F, Sholley C, Worrall C, et al. Risk of acute myocardial infarction, stroke, heart failure, and death in elderly Medicare patients treated with rosiglitazone or pioglitazone. JAMA. 2010;304(4):411–8. https://doi.org/10.1001/jama.2010.920.CrossRefPubMedGoogle Scholar
- 11.•• Glynn RJ, Knight EL, Levin R, Avorn J. Paradoxical relations of drug treatment with mortality in older persons. Epidemiology. 2001;12(6):682–9. https://doi.org/10.1097/00001648-200111000-00017. Shows how a series of medications that should not affect mortality are associated with mortality. Factors such as proximity to death and overall health status drive the associations. CrossRefPubMedGoogle Scholar
- 12.Sturmer T, Rothman KJ, Avorn J, Glynn RJ. Treatment effects in the presence of unmeasured confounding: dealing with observations in the tails of the propensity score distribution—a simulation study. Am J Epidemiol. 2010;172(7):843–54. https://doi.org/10.1093/aje/kwq198.CrossRefPubMedPubMedCentralGoogle Scholar
- 16.Roche MW, Boyle DJ, Cheng CC, Del Pozzo J, Cherneski L, Pascarella J et al. Prevalence and risk of violent ideation and behavior in serious mental illnesses: an analysis of 63,572 patient records. J Interpers Violence. 2018:886260518759976. https://doi.org/10.1177/0886260518759976.
- 22.Gokhale M, Girman C, Chen Y, Pate V, Funk MJ, Sturmer T. Comparison of diagnostic evaluations for cough among initiators of angiotensin converting enzyme inhibitors and angiotensin receptor blockers. Pharmacoepidemiol Drug Saf. 2016;25(5):512–20. https://doi.org/10.1002/pds.3977.CrossRefPubMedPubMedCentralGoogle Scholar
- 24.Brookhart MA, Patrick AR, Dormuth C, Avorn J, Shrank W, Cadarette SM, et al. Adherence to lipid-lowering therapy and the use of preventive health services: an investigation of the healthy user effect. Am J Epidemiol. 2007;166(3):348–54. https://doi.org/10.1093/aje/kwm070.CrossRefPubMedGoogle Scholar
- 27.Toh S, Baker MA, Brown JS, Kornegay C, Platt R, Mini-Sentinel I. Rapid assessment of cardiovascular risk among users of smoking cessation drugs within the US Food and Drug Administration’s Mini-Sentinel program. JAMA Intern Med. 2013;173(9):817–9. https://doi.org/10.1001/jamainternmed.2013.3004.CrossRefPubMedGoogle Scholar
- 28.Lund JL, Sturmer T, Sanoff HK. Comparative effectiveness of postoperative chemotherapy among older patients with non-metastatic rectal cancer treated with preoperative chemoradiotherapy. J Geriatr Oncol. 2016;7(3):176–86. https://doi.org/10.1016/j.jgo.2016.01.011.CrossRefPubMedPubMedCentralGoogle Scholar
- 29.•• Jackson LA, Jackson ML, Nelson JC, Neuzil KM, Weiss NS. Evidence of bias in estimates of influenza vaccine effectiveness in seniors. Int J Epidemiol. 2006;35(2):337–44. https://doi.org/10.1093/ije/dyi274. Elegantly demostrates how much of the observed association between receipt of the influenza vaccine and reduced mortality likely resulted from the differential receipt of the vaccine whereby individuals not expected to survive to the influenza season did not receive the vaccine. CrossRefPubMedGoogle Scholar
- 31.Zhang HT, McGrath LJ, Wyss R, Ellis AR, Sturmer T. Controlling confounding by frailty when estimating influenza vaccine effectiveness using predictors of dependency in activities of daily living. Pharmacoepidemiol Drug Saf. 2017;26(12):1500–6. https://doi.org/10.1002/pds.4298.CrossRefPubMedGoogle Scholar
- 32.Patrick AR, Schneeweiss S, Brookhart MA, Glynn RJ, Rothman KJ, Avorn J, et al. The implications of propensity score variable selection strategies in pharmacoepidemiology: an empirical illustration. Pharmacoepidemiol Drug Saf. 2011;20(6):551–9. https://doi.org/10.1002/pds.2098.CrossRefPubMedPubMedCentralGoogle Scholar
- 33.Schneeweiss S, Patrick AR, Sturmer T, Brookhart MA, Avorn J, Maclure M, et al. Increasing levels of restriction in pharmacoepidemiologic database studies of elderly and comparison with randomized trial results. Med Care. 2007;45(10):S131–S42. https://doi.org/10.1097/MLR.0b013e318070c08e.CrossRefPubMedPubMedCentralGoogle Scholar
- 34.Setoguchi S, Glynn RJ, Avorn J, Mogun H, Schneeweiss S. Statins and the risk of lung, breast, and colorectal cancer in the elderly. Circulation. 2007;115(1):27–33. https://doi.org/10.1161/CIRCULATIONAHA.106.650176. CrossRefPubMedGoogle Scholar
- 40.Sansone RA, Sansone LA. Pain, pain, go away: antidepressants and pain management. Psychiatry (Edgmont). 2008;5(12):16–9.Google Scholar
- 41.Lund JL, Horvath-Puho E, Komjathine Szepligeti S, Sorensen HT, Pedersen L, Ehrenstein V, et al. Conditioning on future exposure to define study cohorts can induce bias: the case of low-dose acetylsalicylic acid and risk of major bleeding. Clin Epidemiol. 2017;9:611–26. https://doi.org/10.2147/CLEP.S147175.CrossRefPubMedPubMedCentralGoogle Scholar
- 44.Nordin J, Mullooly J, Poblete S, Strikas R, Petrucci R, Wei F, et al. Influenza vaccine effectiveness in preventing hospitalizations and deaths in persons 65 years or older in Minnesota, New York, and Oregon: data from 3 health plans. J Infect Dis. 2001;184(6):665–70. https://doi.org/10.1086/323085.CrossRefPubMedGoogle Scholar
- 47.Hak E, Nordin J, Wei F, Mullooly J, Poblete S, Strikas R, et al. Influence of high-risk medical conditions on the effectiveness of influenza vaccination among elderly members of 3 large managed-care organizations. Clin Infect Dis. 2002;35(4):370–7. https://doi.org/10.1086/341403.CrossRefPubMedGoogle Scholar
- 49.• Wirtz HS, Calip GS, Buist DSM, Gralow JR, Barlow WE, Gray S, et al. Evidence for detection bias by medication use in a cohort study of breast cancer survivors. Am J Epidemiol. 2017;185(8):661–72. https://doi.org/10.1093/aje/kww242. Demonstrates how individuals at the extremes of health status (very sick/very healthy) are more/less likely to obtain certain medications and/or be adherent. Individuals at both extremes may differentially utilize cancer screening and may therefore be differentially diagnosed with cancer potentially leading to spurious associations. CrossRefPubMedPubMedCentralGoogle Scholar
- 52.Boudreau DM, Yu O, Chubak J, Wirtz HS, Bowles EJ, Fujii M, et al. Comparative safety of cardiovascular medication use and breast cancer outcomes among women with early stage breast cancer. Breast Cancer Res Treat. 2014;144(2):405–16. https://doi.org/10.1007/s10549-014-2870-5.CrossRefPubMedPubMedCentralGoogle Scholar
- 53.Merikangas KR, Akiskal HS, Angst J, Greenberg PE, Hirschfeld RM, Petukhova M, et al. Lifetime and 12-month prevalence of bipolar spectrum disorder in the National Comorbidity Survey replication. Arch Gen Psychiatry. 2007;64(5):543–52. https://doi.org/10.1001/archpsyc.64.5.543.CrossRefPubMedPubMedCentralGoogle Scholar
- 54.Blanco C, Compton WM, Saha TD, Goldstein BI, Ruan WJ, Huang B, et al. Epidemiology of DSM-5 bipolar I disorder: results from the National Epidemiologic Survey on Alcohol and Related Conditions—III. J Psychiatr Res. 2017;84:310–7. https://doi.org/10.1016/j.jpsychires.2016.10.003. CrossRefPubMedGoogle Scholar
- 58.Cloonan SM, Williams DC. The antidepressants maprotiline and fluoxetine induce type II autophagic cell death in drug-resistant Burkitt’s lymphoma. Int J Cancer. 2011;128(7):1712–23. https://doi.org/10.1002/ijc.25477; 10.1002/ijc.25477.
- 59.Levkovitz Y, Gil-Ad I, Zeldich E, Dayag M, Weizman A. Differential induction of apoptosis by antidepressants in glioma and neuroblastoma cell lines: evidence for p-c-Jun, cytochrome c, and caspase-3 involvement. J Mol Neurosci. 2005;27(1):29–42. https://doi.org/10.1385/JMN:27:1:029. CrossRefPubMedGoogle Scholar
- 60.Reddy KK, Lefkove B, Chen LB, Govindarajan B, Carracedo A, Velasco G, et al. The antidepressant sertraline downregulates Akt and has activity against melanoma cells. Pigment Cell Melanoma Res. 2008;21(4):451–6. https://doi.org/10.1111/j.1755-148X.2008.00481.x; 10.1111/j.1755-148X.2008.00481.x.
- 64.Kerr SJ, Rowett DS, Sayer GP, Whicker SD, Saltman DC, Mant A. All-cause mortality of elderly Australian veterans using COX-2 selective or non-selective NSAIDs: a longitudinal study. Br J Clin Pharmacol. 2011;71(6):936–42. https://doi.org/10.1111/j.1365-2125.2010.03702.x.CrossRefPubMedPubMedCentralGoogle Scholar
- 71.Fearon ER. Molecular genetics of colorectal cancer. Annu Rev Pathol. 2011;6:479–507. https://doi.org/10.1146/annurev-pathol-011110-130235; 10.1146/annurev-pathol-011110-130235.
- 75.Brenner H, Kloor M, Pox CP. Colorectal cancer. Lancet. 2013; https://doi.org/10.1016/S0140-6736(13)61649-9.
- 76.Rogers CR, Robinson CD, Arroyo C, Obidike OJ, Sewali B, Okuyemi KS. Colorectal cancer screening uptake’s association with psychosocial and sociodemographic factors among homeless Blacks and Whites. Health Educ Behav. 2017;44(6):928–36. https://doi.org/10.1177/1090198117734284.CrossRefPubMedPubMedCentralGoogle Scholar
- 78.Centers for M, Medicaid Services HHS. Medicare program; revisions to payment policies and five-year review of and adjustments to the relative value units under the physician fee schedule for calendar year 2002. Final rule with comment period. Fed Regist. 2001;66(212):55245–503.Google Scholar
- 79.Chubak J, Boudreau DM, Rulyak SJ, Mandelson MT. Colorectal cancer risk in relation to antidepressant medication use. Int J Cancer. 2011;128(1):227–32. https://doi.org/10.1002/ijc.25322; 10.1002/ijc.25322.
- 80.Coogan PF, Strom BL, Rosenberg L. Antidepressant use and colorectal cancer risk. Pharmacoepidemiol Drug Saf. 2009;18(11):1111–4. https://doi.org/10.1002/pds.1808; 10.1002/pds.1808.
- 81.Cronin-Fenton DP, Riis AH, Lash TL, Dalton SO, Friis S, Robertson D, et al. Antidepressant use and colorectal cancer risk: a Danish population-based case-control study. Br J Cancer. 2011;104(1):188–92. https://doi.org/10.1038/sj.bjc.6605911; 10.1038/sj.bjc.6605911.
- 82.Haukka J, Sankila R, Klaukka T, Lonnqvist J, Niskanen L, Tanskanen A, et al. Incidence of cancer and antidepressant medication: record linkage study. Int J Cancer. 2010;126(1):285–96. https://doi.org/10.1002/ijc.24537; 10.1002/ijc.24537.
- 83.Walker AJ, Card T, Bates TE, Muir K. Tricyclic antidepressants and the incidence of certain cancers: a study using the GPRD. Br J Cancer. 2011;104(1):193–7. https://doi.org/10.1038/sj.bjc.6605996; 10.1038/sj.bjc.6605996.
- 85.Yanik EL, Pfeiffer RM, Freedman DM, Weinstock MA, Cahoon EK, Arron ST, et al. Spectrum of immune-related conditions associated with risk of keratinocyte cancers among elderly adults in the United States. Cancer Epidemiol Biomark Prev. 2017;26(7):998–1007. https://doi.org/10.1158/1055-9965.EPI-17-0003.CrossRefGoogle Scholar