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Articles

Statistical methods for assessing drug interactions using observational data

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Pages 298-323 | Received 16 Oct 2021, Accepted 04 Sep 2022, Published online: 20 Sep 2022

References

  • M. Ataei, F.M. Shirazi, R.J. Lamarine, S. Nakhaee, and O. Mehrpour, A double-edged sword of using opioids and COVID-19: A toxicological view, Subst. Abuse. Treat. Prev. Policy. 15 (2020), pp. 1–4.
  • P.C. Austin and E.A. Stuart, Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies, Stat. Med. 34 (2015), pp. 3661–3679.
  • K. Baxter, Stockley's Drug Interactions, Pharmaceutical Press, London, 2008.
  • M.A. Brookhart, S. Schneeweiss, K.J. Rothman, R.J. Glynn, J. Avorn, and T. Sturmer, Variable selection for propensity score models, Am. J. Epidemiol. 163 (2006), pp. 1149–1156.
  • S.R. Cole and M.A. Hernan, Constructing inverse probability weights for marginal structural models, Am. J. Epidemiol. 168 (2008), pp. 656–664.
  • J.A. Craycroft, J. Huang, and M. Kong, Propensity score specification for optimal estimation of ATE with binary response, Stat. Methods. Med. Res. 29 (2020), pp. 3623–3640.
  • L.B. Daniels, J. Ren, K. Kumar, Q.M. Bui, J. Zhang, X. Zhang, M.A. Sawan, H. Eisen, C.A. Longhurst, and K. Messer, Relation of prior statin and anti-hypertensive use to severity of disease among patients hospitalized with COVID-19: findings from the American heart association's COVID-19 cardiovascular disease registry, PLoS. ONE. 16 (2021), pp. e0254635.
  • T.M.C. de Lucena, A.F. da Silva Santos, B.R. de Lima, M.E. de Albuquerque Borborema, and J. de Azevedo Silva, Mechanism of inflammatory response in associated comorbidities in COVID-19, Diabetes Metab Syndr 14 (2020), pp. 597–600.
  • A. De Spiegeleer, A. Bronselaer, J.T. Teo, G. Byttebier, G. De Tre, L. Belmans, and R. Dobson et al, The effects of ARBs, ACEis, and statins on clinical outcomes of COVID-19 infection among nursing home residents, J. Am. Med. Dir. Assoc. 21 (2020), pp. 909–914.
  • H.B. Fang, G.L. Tian, and M. Tan, Hierarchical models for tumor xenograft experiments in drug development, J. Biopharm. Stat. 14 (2004), pp. 931–945.
  • A.J. Garber, D.S. Donovan Jr, P. Dandona, S. Bruce, and J.S. Park, Efficacy of glyburide/metformin tablets compared with initial monotherapy in type 2 diabetes, The Journal of Clinical Endocrinology & Metabolism 88 (2003), pp. 3598–3604.
  • A. Gelman and J. Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2006.
  • S. Greenland and M.A. Mansournia, Limitations of individual causal models, causal graphs, and ignorability assumptions, as illustrated by random confounding and design unfaithfulness, Eur. J. Epidemiol. 30 (2015), pp. 1101–1110.
  • M.A. Hernan and J.M. Robins, Causal Inference: What If, Chapman & Hill/CRC, Boca Raton, FL, 2020.
  • K. Hirano and G.W. Imbens, The propensity score with continuous treatments, in Applied Bayesian Modeling and Causal Inference from Incomplete-data Perspectives, A. Gelman and X. Meng, eds., Wiley, New York, 2004, pp. 73–84
  • D.G. Horvitz and D.J. Thompson, A generalization of sampling without replacement from a finite universe, J. Am. Stat. Assoc. 47 (1952), pp. 663–685.
  • G.W. Imbens, The role of the propensity score in estimating dose-response functions, Biometrika 87 (2000), pp. 706–710.
  • K. Imai and M. Ratkovic, Covariate balancing propensity score, J. R. Stat. Soc.: Series B (Statistical Methodology) 76 (2014), pp. 243–263.
  • J.D. Kang and J.L. Schafer, Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data, Stat. Sci. 22 (2007), pp. 523–539.
  • M. Kong and J.J. Lee, A generalized response surface model with varying relative potency for assessing drug interaction, Biometrics 62 (2006), pp. 986–995.
  • J.J. Lee, M. Kong, G.D. Ayers, and R. Lotan, Interaction index and different methods for determining drug interaction in combination therapy, J. Biopharm. Stat. 17 (2007), pp. 461–480.
  • B.K. Lee, J. Lessler, and E.A. Stuart, Weight trimming and propensity score weighting, PLoS. ONE. 6 (2011), pp. e18174.
  • T. Lumley, Analysis of complex survey samples, J. Stat. Softw. 9 (2004), pp. 1–19.
  • F. Li and F. Li, Propensity score weighting for causal inference with multiple treatments, Ann. Appl. Stat. 13 (2019), pp. 2389–2415.
  • D.C. Malone, E.P. Armstrong, J. Abarca, A.J. Grizzle, P.D. Hansten, R.C. Van Bergen, B.S. Duncan-Edgar, S.L Solomon, and R.B. Lipton, Identification of serious drug-drug interactions: Results of the partnership to prevent drug-drug interactions, J. Am. Pharm. Assoc. (2003) 44 (2004), pp. 142–151.
  • D.F. McCaffrey, B.A. Griffin, D. Almirall, M.E. Slaughter, R. Ramchand, and L.F. Burgette, A tutorial on propensity score estimation for multiple treatments using generalized boosted models, Stat. Med. 32 (2013), pp. 3388–3414.
  • R. Mokhtari, T.S. Homayouni, N. Baluch, E. Morgatskaya, S. Kumar, B. Das, and H. Yeger, Combination therapy in combating cancer, Oncotarget 8 (2017), pp. 38022–38043.
  • C.Z. Mooney and R.D. Duval, Bootstrapping: A Nonparametric Approach to Statistical Inference, SAGE Publications Inc., Thousand Oaks, CA, 1993.
  • J.C. Naveiro-Rilo, D. Diez-Juarez, M.L. Flores-Zurutuza, P. Javierre Perez, C. Alberte Perez, and R. Molina Mazo, Quality of life in the elderly on polymedication and with multiple morbidities, Rev. Esp Geriatr Gerontol 49 (2014), pp. 158–164.
  • Y. Noguchi, T. Tachi, and H. Teramachi, Review of statistical methodologies for detecting drug-drug interactions using spontaneous reporting systems, Front. Pharmacol. 10 (2019), pp. 1319.
  • A. Oesterle, U. Laufs, and J.K. Liao, CPleiotropic effects of statins on the cardiovascular system, Circ. Res. 120 (2017), pp. 229–243.
  • J.M. Robins, M.A. Hernan, and B. Brumback, Marginal structural models and causal inference in epidemiology, Epidemiology 11 (2000), pp. 550–560.
  • P.R. Rosenbaum and D.B. Rubin, The central role of the propensity score in observational studies for causal effects, Biometrika 70 (1983), pp. 41–55.
  • D.B. Rubin, Causal inference using potential outcomes: Design, modeling, decisions, Journal of the American Statistical Association 100 (2005), pp. 322–331.
  • B. Strack, J.P. DeShazo, C. Gennings, J.L. Olmo, S. Ventura, K.J. Cios, and J.N. Clore, Impact of HbA1c measurement on hospital readmission rates: Analysis of 70, 000 clinical database patient records, Biomed. Res. Int. (2014), doi: 10.1155/2014/781670.
  • N.P. Tatonetti, P.P. Ye, R. Daneshjou, and R.B. Altman, Data-driven prediction of drug effects and interactions, Sci. Transl. Med. 4 (2012), pp. 125ra31–125ra31.
  • K. Tikoo, G. Patel, S. Kumar, P.A. Karpe, M. Sanghavi, V. Malek, and K. Srinivasan, Tissue specific up regulation of ACE2 in rabbit model of atherosclerosis by atorvastatin: Role of epigenetic histone modifications, Biochem. Pharmacol. 93 (2015), pp. 343–351.
  • M.Y. Tramontina, M.B. Ferreira, M.S. Castro, and I. Heineck, Comorbidities, potentially dangerous and low therapeutic index medications: Factors linked to emergency visits, Ciencia & Saude Coletiva 23 (2018), pp. 1471–1482.
  • T. VanderWeele, Explanation in Causal Inference: Methods for Mediation and Interaction, Oxford University Press, New York, NY, 2015.
  • T.J. VanderWeele and M.J. Knol, A tutorial on interaction, Epidemiol. Method. 3 (2014), pp. 33–72.
  • N. Vogt-Ferrier, Older patients, multiple comorbidities, polymedication ···  should we treat everything?, Eur. Geriatr. Med. 2 (2011), pp. 48–51.
  • X. Yan, Y. Abdia, S. Datta, K.B. Kulasekera, B. Ugiliweneza, M. Boakye, and M. Kong, Estimation of average treatment effects among multiple treatment groups by using an ensemble approach, Stat. Med. 38 (2019), pp. 2828–2846.
  • Z. Zhang, H.J. Kim, G. Lonjon, and Y. Zhu, Balance diagnostics after propensity score matching, Ann. Transl. Med. 7 (2019), pp. 16.
  • W. Zhao and H. Yang, Statistical Methods in Drug Combination Studies, Vol. 69, CRC Press, 2014.
  • H Zou and T. Hastie, Regularization and variable selection via the elastic net, J. R. Stat. Soc.: Series B (Stat. Methodol.) 67 (2005), pp. 301–320.

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