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Causal Inference Methods in Pharmacoepidemiology

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NeuroPsychopharmacotherapy

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Abstract

This chapter provides an introduction to pharmacoepidemiology, with a special focus on some of the key concepts related to the application of causal inference methods in pharmacoepidemiology, particularly in the time-varying setting. The chapter first describes frequently used study designs in pharmacoepidemiologic studies of treatment effects. The chapter then follows with drug exposures and confounding in time-fixed and time-varying settings. The chapter continues with a range of causal inference methods developed to evaluate treatment effects while appropriately accounting for time-fixed and time-varying confounding, which is followed by select examples of published studies that have applied these methods to evaluate effects of drug treatments in the field of Neuro-Psycho-Pharmacoepidemiology. Finally, the chapter concludes with a discussion on the considerations needed in the applications of these causal inference methods in studies with observational data.

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References

  • What is Pharmacoepidemiology? Pharmacoepidemiology. 2019a:1–26.

    Google Scholar 

  • Basic principles of clinical pharmacology relevant to Pharmacoepidemiologic studies. Pharmacoepidemiology. 2019b:27–43.

    Google Scholar 

  • Basic principles of clinical epidemiology relevant to Pharmacoepidemiologic studies. Pharmacoepidemiology. 2019c:44–59.

    Google Scholar 

  • AM W. confounding by indication. Epidemiology. 1996;7(4):335–6.

    Google Scholar 

  • Bodenheimer T, Wagner EH, Grumbach K. Improving primary care for patients with chronic illness. JAMA. 2002a;288(14):1775–9.

    PubMed  Google Scholar 

  • Bodenheimer T, Wagner EH, Grumbach K. Improving primary care for patients with chronic illness: the chronic care model, part 2. JAMA. 2002b;288(15):1909–14.

    PubMed  Google Scholar 

  • Bykov K, Bateman BT, Franklin JM, Vine SM, Patorno E. Association of Gabapentinoids with the risk of opioid-related adverse events in surgical patients in the United States. JAMA Netw Open. 2020;3(12):e2031647-e.

    Google Scholar 

  • Caniglia EC, Rebecca Z, Jacobson DL, Diseko M, Mayondi G, Lockman S, et al. Emulating a target trial of antiretroviral therapy regimens started before conception and risk of adverse birth outcomes. AIDS (London, England). 2018;32(1):113.

    PubMed  Google Scholar 

  • Caniglia EC, Rojas-Saunero LP, Hilal S, Licher S, Logan R, Stricker B, et al. Emulating a target trial of statin use and risk of dementia using cohort data. Neurology. 2020;95(10):e1322–e32.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Cochran WG. Observational studies. Ames Iowa State University Press; 1972.

    Google Scholar 

  • Daniel RM, Cousens SN, De Stavola BL, Kenward MG, Sterne JA. Methods for dealing with time-dependent confounding. Stat Med. 2013;32(9):1584–618.

    CAS  PubMed  Google Scholar 

  • Dawid AP. Causal inference without counterfactuals. J Am Stat Assoc. 2000;95(450):407–24.

    Google Scholar 

  • Desai RJ, Rothman KJ, Bateman BT, Hernandez-Diaz S, Huybrechts KF. A propensity score based fine stratification approach for confounding adjustment when exposure is infrequent. Epidemiology. 2017;28(2):249.

    PubMed  PubMed Central  Google Scholar 

  • Dickerman BA, García-Albéniz X, Logan RW, Denaxas S, Hernán MA. Emulating a target trial in case-control designs: an application to statins and colorectal cancer. Int J Epidemiol. 2020;49(5):1637–46.

    PubMed  PubMed Central  Google Scholar 

  • Dorn HF. Philosophy of inferences from retrospective studies. Am J Public Health Nations Health. 1953;43(6_Pt_1):677–83.

    CAS  PubMed  PubMed Central  Google Scholar 

  • ElZarrad MK, Corrigan-Curay J. The US Food and Drug Administration’s real-world evidence framework: a commitment for engagement and transparency on real-world evidence. Clin Pharmacol Ther. 2019;106(1):33–5.

    PubMed  Google Scholar 

  • Farrington C. Control without separate controls: evaluation of vaccine safety using case-only methods. Vaccine. 2004;22(15–16):2064–70.

    CAS  PubMed  Google Scholar 

  • Feinstein AR. XI. Sources of ‘chronology bias’ in cohort statistics. Clin Pharmacol Ther. 1971;12(5):864–79.

    CAS  PubMed  Google Scholar 

  • Franklin JM, Glynn RJ, Martin D, Schneeweiss S. Evaluating the use of nonrandomized real-world data analyses for regulatory decision making. Clin Pharmacol Ther. 2019;105(4):867–77.

    PubMed  Google Scholar 

  • Glymour MM, Gibbons LE, Gilsanz P, Gross AL, Mez J, Brewster PW, et al. Initiation of antidepressant medication and risk of incident stroke: using the adult changes in thought cohort to address time-varying confounding. Ann Epidemiol. 2019;35:42-7. e1.

    Google Scholar 

  • Glynn RJ, Knight EL, Levin R, Avorn J. Paradoxical relations of drug treatment with mortality in older persons. Epidemiology. 2001:682–9.

    Google Scholar 

  • Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology. 1999a:37–48.

    Google Scholar 

  • Greenland S, Robins JM, Pearl J. Confounding and collapsibility in causal inference. Stat Sci. 1999b;14(1):29–46.

    Google Scholar 

  • Hernán MA, Alonso A, Logan R, Grodstein F, Michels KB, Stampfer MJ, et al. Observational studies analyzed like randomized experiments: an application to postmenopausal hormone therapy and coronary heart disease. Epidemiology. 2008;19(6):766.

    PubMed  PubMed Central  Google Scholar 

  • Hernan MA, McAdams M, McGrath N, Lanoy E, Costagliola D. Observation plans in longitudinal studies with time-varying treatments. Stat Methods Med Res. 2009;18(1):27–52.

    PubMed  Google Scholar 

  • Hernán MARJ. Causal inference: what if. Boca Raton: Chapman & Hall/CRC; 2020.

    Google Scholar 

  • Hernán MA, Robins JM. Using big data to emulate a target trial when a randomized trial is not available. Am J Epidemiol. 2016;183(8):758–64.

    PubMed  PubMed Central  Google Scholar 

  • Huybrechts KF, Bateman BT, Palmsten K, Desai RJ, Patorno E, Gopalakrishnan C, et al. Antidepressant use late in pregnancy and risk of persistent pulmonary hypertension of the newborn. JAMA. 2015;313(21):2142–51.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Huybrechts KF, Palmsten K, Avorn J, Cohen LS, Holmes LB, Franklin JM, et al. Antidepressant use in pregnancy and the risk of cardiac defects. N Engl J Med. 2014;370(25):2397–407.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Imai K, Van Dyk DA. Causal inference with general treatment regimes: generalizing the propensity score. J Am Stat Assoc. 2004;99(467):854–66.

    Google Scholar 

  • Jackson JW, Schmid I, Stuart EA. Propensity scores in pharmacoepidemiology: beyond the horizon. Curr Epidemiol Rep. 2017;4(4):271–80.

    PubMed  PubMed Central  Google Scholar 

  • Johnson ES, Bartman BA, Briesacher BA, Fleming NS, Gerhard T, Kornegay CJ, et al. The incident user design in comparative effectiveness research. Pharmacoepidemiol Drug Saf. 2013;22(1):1–6.

    PubMed  Google Scholar 

  • Li X, Young JG, Toh S. Estimating effects of dynamic treatment strategies in Pharmacoepidemiologic studies with time-varying confounding: a primer. Curr Epidemiol Rep. 2017;4(4):288–97.

    PubMed  PubMed Central  Google Scholar 

  • Lund JL, Richardson DB, Stürmer T. The active comparator, new user study design in pharmacoepidemiology: historical foundations and contemporary application. Curr Epidemiol Rep. 2015;2(4):221–8.

    PubMed  PubMed Central  Google Scholar 

  • Maclure M. The case-crossover design: a method for studying transient effects on the risk of acute events. Am J Epidemiol. 1991;133(2):144–53.

    CAS  PubMed  Google Scholar 

  • Martinez C, Assimes TL, Mines D, Dell’aniello S, Suissa S. Use of venlafaxine compared with other antidepressants and the risk of sudden cardiac death or near death: a nested case-control study. BMJ. 2010;340:c249.

    PubMed  PubMed Central  Google Scholar 

  • Mcevoy P, Barnes P. Using the chronic care model to tackle depression among older adults who have long-term physical conditions. J Psychiatr Ment Health Nurs. 2007;14(3):233–8.

    CAS  PubMed  Google Scholar 

  • Moura LM, Westover MB, Kwasnik D, Cole AJ, Hsu J. Causal inference as an emerging statistical approach in neurology: an example for epilepsy in the elderly. Clin Epidemiol. 2017;9:9.

    PubMed  Google Scholar 

  • Naimi AI, Cole SR, Kennedy EH. An introduction to g methods. Int J Epidemiol. 2017;46(2):756–62.

    PubMed  Google Scholar 

  • Newsome SJ, Keogh RH, Daniel RM. Estimating long-term treatment effects in observational data: a comparison of the performance of different methods under real-world uncertainty. Stat Med. 2018;37(15):2367–90.

    PubMed  PubMed Central  Google Scholar 

  • Palmsten K, Hernández-Díaz S, Huybrechts KF, Williams PL, Michels KB, Achtyes ED, et al. Use of antidepressants near delivery and risk of postpartum hemorrhage: cohort study of low income women in the United States. BMJ. 2013;347

    Google Scholar 

  • Park Y, Bateman BT, Kim DH, Hernandez-Diaz S, Patorno E, Glynn RJ, et al. Use of haloperidol versus atypical antipsychotics and risk of in-hospital death in patients with acute myocardial infarction: cohort study. BMJ. 2018;360

    Google Scholar 

  • Patorno E, Bohn RL, Wahl PM, Avorn J, Patrick AR, Liu J, et al. Anticonvulsant medications and the risk of suicide, attempted suicide, or violent death. JAMA. 2010;303(14):1401–9.

    CAS  PubMed  Google Scholar 

  • Pazzagli L, Linder M, Zhang M, Vago E, Stang P, Myers D, et al. Methods for time-varying exposure related problems in pharmacoepidemiology: an overview. Pharmacoepidemiol Drug Saf. 2018;27(2):148–60.

    PubMed  Google Scholar 

  • Pearl J. Causal diagrams for empirical research. Biometrika. 1995;82(4):669–88.

    Google Scholar 

  • Pearl J, Glymour M, Jewell NP. Causal inference in statistics: a primer. John Wiley & Sons; 2016.

    Google Scholar 

  • Prentice RL. A case-cohort design for epidemiologic cohort studies and disease prevention trials. Biometrika. 1986;73(1):1–11.

    Google Scholar 

  • Ray WA. Evaluating medication effects outside of clinical trials: new-user designs. Am J Epidemiol. 2003;158(9):915–20.

    PubMed  Google Scholar 

  • Robins J. A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect. Math Model. 1986;7(9–12):1393–512.

    Google Scholar 

  • Robins JM. Correcting for non-compliance in randomized trials using structural nested mean models. Commun Stat Theory Methods. 1994;23(8):2379–412.

    Google Scholar 

  • Robins JM, Hernán MA. Estimation of the causal effects of time-varying exposures. Longitudinal Data Anal. 2009;553:599.

    Google Scholar 

  • Rosenbaum P, Armitage P, Colton T. Encyclopedia of biostatistics. 2005.

    Google Scholar 

  • Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70(1):41–55.

    Google Scholar 

  • Rubin DB. Comment: which ifs have causal answers. J Am Stat Assoc. 1986;81(396):961–2.

    Google Scholar 

  • Rubin DB. Bayesian inference for causal effects: the role of randomization. Ann Stat. 1978:34–58.

    Google Scholar 

  • Rubin DB. Assignment to treatment group on the basis of a covariate. J Educ Stat. 1977;2(1):1–26.

    Google Scholar 

  • Rubin DB. Estimating causal effects of treatments in randomized and nonrandomized studies. J Educ Psychol. 1974;66(5):688–701.

    Google Scholar 

  • Schneeweiss S, Eichler HG, Garcia-Altes A, Chinn C, Eggimann AV, Garner S, et al. Real world data in adaptive biomedical innovation: a framework for generating evidence fit for decision-making. Clin Pharmacol Ther. 2016;100(6):633–46.

    CAS  PubMed  Google Scholar 

  • Schneeweiss S, Patrick AR, Solomon DH, Mehta J, Dormuth C, Miller M, et al. Variation in the risk of suicide attempts and completed suicides by antidepressant agent in adults: a propensity score–adjusted analysis of 9 years’ data. Arch Gen Psychiatry. 2010;67(5):497–506.

    PubMed  PubMed Central  Google Scholar 

  • 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 Supl 2):S131–42.

    PubMed  PubMed Central  Google Scholar 

  • Schuemie MJ, Ryan PB, Man KK, Wong IC, Suchard MA, Hripcsak G. A plea to stop using the case-control design in retrospective database studies. Stat Med. 2019;38(22):4199–208.

    PubMed  PubMed Central  Google Scholar 

  • 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(1):49–53.

    PubMed  Google Scholar 

  • Suissa S. The case-time-control design. Epidemiology. 1995:248–53.

    Google Scholar 

  • Sun JW, Hernández-Díaz S, Haneuse S, Bourgeois FT, Vine SM, Olfson M, et al. Association of Selective Serotonin Reuptake Inhibitors with the risk of type 2 diabetes in children and adolescents. JAMA Psychiat. 2021;78(1):91–100.

    Google Scholar 

  • VanderWeele TJ. Concerning the consistency assumption in causal inference. Epidemiology. 2009;20(6):880–3.

    PubMed  Google Scholar 

  • Wang PS, Schneeweiss S, Avorn J, Fischer MA, Mogun H, Solomon DH, et al. Risk of death in elderly users of conventional vs. atypical antipsychotic medications. N Engl J Med. 2005;353(22):2335–41.

    CAS  PubMed  Google Scholar 

  • Whitaker HJ, Paddy Farrington C, Spiessens B, Musonda P. Tutorial in biostatistics: the self-controlled case series method. Stat Med. 2006;25(10):1768–97.

    PubMed  Google Scholar 

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Author information

Authors and Affiliations

  1. Centre for Pharmacoepidemiology, Department of Medicine, Karolinska Institutet, Stockholm, Sweden

    Laura Pazzagli

  2. Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA

    Xiaojuan Li

Authors
  1. Laura Pazzagli
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  2. Xiaojuan Li
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Corresponding author

Correspondence to Laura Pazzagli .

Editor information

Editors and Affiliations

  1. Clinic and Policlinic for Psychiatry, Psychosomatics and Psychotherapy, University Hospital Wuerzburg, Wuerzburg, Germany

    Peter Riederer

  2. Institute of Psychological Medicine (IPM), Soyen, Germany

    Gerd Laux

  3. Centre for Research Promotion and Support, Fujita Health University, Toyoake, Aichi, Japan

    Toshiharu Nagatsu

  4. Center Translational Res Neurol Diseases, Dalian Med Uni Shanghai Inst Health Sc Center Translational Res Neurol Diseases, Dalian, China

    Weidong Le

  5. Würzburg, Germany

    Christian Riederer

Section Editor information

  1. Clinic and Policlinic for Psychiatry, Psychosomatics and Psychotherapy, Univsersity of Hospital Würzburg, Würzburg, Germany

    Peter Riederer

  2. Department of Psychiatry, University of South Denmark, Odense, Denmark

    Peter Riederer

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Pazzagli, L., Li, X. (2022). Causal Inference Methods in Pharmacoepidemiology. In: Riederer, P., Laux, G., Nagatsu, T., Le, W., Riederer, C. (eds) NeuroPsychopharmacotherapy. Springer, Cham. https://doi.org/10.1007/978-3-030-62059-2_14

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  • DOI: https://doi.org/10.1007/978-3-030-62059-2_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62058-5

  • Online ISBN: 978-3-030-62059-2

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