View Item 

Show simple item record

Robust causal inference of drug-drug interactions
dc.contributor.authorShu, Di
dc.contributor.authorHan, Peisong
dc.contributor.authorHennessy, Sean
dc.contributor.authorMiano, Todd A
dc.date.accessioned2023-04-04T17:38:37Z
dc.date.available2024-04-04 13:38:36en
dc.date.available2023-04-04T17:38:37Z
dc.date.issued2023-03-30
dc.identifier.citationShu, Di; Han, Peisong; Hennessy, Sean; Miano, Todd A (2023). "Robust causal inference of drug-drug interactions." Statistics in Medicine 42(7): 970-992.
dc.identifier.issn0277-6715
dc.identifier.issn1097-0258
dc.identifier.urihttps://hdl.handle.net/2027.42/176013
dc.publisherJohn Wiley & Sons, Inc.
dc.subject.otherpropensity score weighting
dc.subject.otherpharmacoepidemiology
dc.subject.othermultiple robustness
dc.subject.otherdrug-drug interaction
dc.subject.othercausal inference
dc.titleRobust causal inference of drug-drug interactions
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelStatistics and Numeric Data
dc.subject.hlbsecondlevelPublic Health
dc.subject.hlbsecondlevelMedicine (General)
dc.subject.hlbtoplevelSocial Sciences
dc.subject.hlbtoplevelHealth Sciences
dc.subject.hlbtoplevelScience
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/176013/1/sim9653_am.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/176013/2/sim9653.pdf
dc.identifier.doi10.1002/sim.9653
dc.identifier.sourceStatistics in Medicine
dc.identifier.citedreferenceQin J, Zhang B. Empirical-likelihood-based inference in missing response problems and its application in observational studies. J R Stat Soc Series B Stat Methodol. 2007; 69 ( 1 ): 101 - 122.
dc.identifier.citedreferenceHan P, Wang L. Estimation with missing data: beyond double robustness. Biometrika. 2013; 100 ( 2 ): 417 - 430.
dc.identifier.citedreferenceSchisterman EF, Cole SR, Platt RW. Overadjustment bias and unnecessary adjustment in epidemiologic studies. Epidemiology. 2009; 20 ( 4 ): 488 - 495.
dc.identifier.citedreferenceLu H, Cole SR, Platt RW, Schisterman EF. Revisiting overadjustment bias. Epidemiology. 2021; 32 ( 5 ): e22 - e23.
dc.identifier.citedreferenceBrookhart MA, Schneeweiss S, Rothman KJ, Glynn RJ, Avorn J, Stürmer T. Variable selection for propensity score models. Am J Epidemiol. 2006; 163 ( 12 ): 1149 - 1156.
dc.identifier.citedreferenceMickey RM, Greenland S. The impact of confounder selection criteria on effect estimation. Am J Epidemiol. 1989; 129 ( 1 ): 125 - 137.
dc.identifier.citedreferenceSchneeweiss S, Rassen JA, Glynn RJ, Avorn J, Mogun H, Brookhart MA. High-dimensional propensity score adjustment in studies of treatment effects using health care claims data. Epidemiology. 2009; 20 ( 4 ): 512 - 522.
dc.identifier.citedreferenceVanderWeele TJ. Principles of confounder selection. Eur J Epidemiol. 2019; 34: 211 - 219.
dc.identifier.citedreferenceHan P. A further study of the multiply robust estimator in missing data analysis. J Stat Plan Inference. 2014; 148: 101 - 110.
dc.identifier.citedreferenceHan P. Multiply robust estimation in regression analysis with missing data. J Am Stat Assoc. 2014; 109 ( 507 ): 1159 - 1173.
dc.identifier.citedreferenceChan KCG, Yam SCP, Zhang Z. Globally efficient non-parametric inference of average treatment effects by empirical balancing calibration weighting. J R Stat Soc Series B Stat Methodol. 2016; 78 ( 3 ): 673 - 700.
dc.identifier.citedreferenceChen S, Haziza D. Multiply robust imputation procedures for the treatment of item nonresponse in surveys. Biometrika. 2017; 104 ( 2 ): 439 - 453.
dc.identifier.citedreferenceShu D, Han P, Wang R, Toh S. Estimating the marginal hazard ratio by simultaneously using a set of propensity score models: a multiply robust approach. Stat Med. 2021; 40 ( 5 ): 1224 - 1242.
dc.identifier.citedreferenceRubin DB. Estimating causal effects of treatment in randomized and nonrandomized studies. J Educ Psychol. 1974; 66 ( 5 ): 688 - 701.
dc.identifier.citedreferenceHolland PW. Statistics and causal inference. J Am Stat Assoc. 1986; 81 ( 396 ): 945 - 960.
dc.identifier.citedreferenceHájek J. Comment on: An essay on the logical foundations of survey sampling, part one. In: Basu D, Godambe VP, Sprott DA, eds. The Foundations of Survey Sampling. Toronto: Holt, Reinhart & Winston; 1971: 236.
dc.identifier.citedreferenceQin J. Biased Sampling, Over-Identified Parameter Problems and Beyond. Singapore: Springer; 2017.
dc.identifier.citedreferenceHan P. Combining inverse probability weighting and multiple imputation to improve robustness of estimation. Scand J Stat. 2016; 43 ( 1 ): 246 - 260.
dc.identifier.citedreferenceEfron B, Tibshirani RJ. An Introduction to the Bootstrap. New York: Chapman & Hall/CRC; 1993.
dc.identifier.citedreferenceAustin PC. Variance estimation when using inverse probability of treatment weighting (IPTW) with survival analysis. Stat Med. 2016; 35 ( 30 ): 5642 - 5655.
dc.identifier.citedreferenceShu D, Young JG, Toh S, Wang R. Variance estimation in inverse probability weighted cox models. Biometrics. 2021; 77 ( 3 ): 1101 - 1117.
dc.identifier.citedreferenceAustin PC, Stuart EA. 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. 2015; 34 ( 28 ): 3661 - 3679.
dc.identifier.citedreferenceMiano TA, Shashaty MGS, Yang W, Brown JR, Zuppa A, Hennessy S. Effect of renin-angiotensin system inhibitors on the comparative nephrotoxicity of NSAIDs and opioids during hospitalization. Kidney360. 2020; 1 ( 7 ): 604 - 613.
dc.identifier.citedreferenceHan P. A further study of propensity score calibration in missing data analysis. Stat Sin. 2018; 28: 1307 - 1332.
dc.identifier.citedreferenceHausman JA. Specification tests in econometrics. Econometrica. 1978; 46 ( 6 ): 1251 - 1271.
dc.identifier.citedreferenceShu D, He W. Perturbation-based null hypothesis tests with an application to Clayton models. Can J Stat. 2021; 49 ( 4 ): 1236 - 1251.
dc.identifier.citedreferenceRosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983; 70 ( 1 ): 41 - 55.
dc.identifier.citedreferenceHernán MA, Robins JM. Causal Inference: What If. Boca Raton: Chapman & Hall/CRC; 2021.
dc.identifier.citedreferenceVanderWeele TJ, Knol MJ. A tutorial on interaction. Epidemiol Method. 2014; 3 ( 1 ): 33 - 72.
dc.identifier.citedreferenceVanderWeele TJ. On the distinction between interaction and effect modification. Epidemiology. 2009; 20 ( 6 ): 863 - 871.
dc.identifier.citedreferenceHennessy S, Flockhart DA. The need for translational research on drug–drug interactions. Clin Pharm Therap. 2012; 91 ( 5 ): 771 - 773.
dc.identifier.citedreferenceHennessy S, Leonard CE, Gagne JJ, et al. Pharmacoepidemiologic methods for studying the health effects of drug–drug interactions. Clin Pharm Therap. 2016; 99 ( 1 ): 92 - 100.
dc.identifier.citedreferenceGurwitz JH, Field TS, Harrold LR, et al. Incidence and preventability of adverse drug events among older persons in the ambulatory setting. JAMA. 2003; 289 ( 9 ): 1107 - 1116.
dc.identifier.citedreferenceHorvitz DG, Thompson DJ. A generalization of sampling without replacement from a finite universe. J Am Stat Assoc. 1952; 47 ( 260 ): 663 - 685.
dc.identifier.citedreferenceRosenbaum PR. Model-based direct adjustment. J Am Stat Assoc. 1987; 82 ( 398 ): 387 - 394.
dc.identifier.citedreferenceLunceford JK, Davidian M. Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Stat Med. 2004; 23 ( 19 ): 2937 - 2960.
dc.identifier.citedreferenceSeaman SR, White IR. Review of inverse probability weighting for dealing with missing data. Stat Methods Med Res. 2013; 22 ( 3 ): 278 - 295.
dc.identifier.citedreferenceImbens GW. The role of the propensity score in estimating dose-response functions. Biometrika. 2000; 87 ( 3 ): 706 - 710.
dc.identifier.citedreferenceImai K, van Dyk DA. Causal inference with general treatment regimes: generalizing the propensity score. J Am Stat Assoc. 2004; 99 ( 467 ): 854 - 866.
dc.identifier.citedreferenceSpreeuwenberg MD, Bartak A, Croon MA, et al. The multiple propensity score as control for bias in the comparison of more than two treatment arms: an introduction from a case study in mental health. Med Care. 2010; 48 ( 2 ): 166 - 174.
dc.identifier.citedreferenceMcCaffrey DF, Griffin BA, Almirall D, Slaughter ME, Ramchand R, Burgette LF. A tutorial on propensity score estimation for multiple treatments using generalized boosted models. Stat Med. 2013; 32 ( 19 ): 3388 - 3414.
dc.identifier.citedreferenceImai K, Ratkovic M. Covariate balancing propensity score. J R Stat Soc Series B Stat Methodol. 2014; 76 ( 1 ): 243 - 263.
dc.identifier.citedreferenceLi F, Li F. Propensity score weighting for causal inference with multiple treatments. Ann Appl Stat. 2019; 13 ( 4 ): 2389 - 2415.
dc.identifier.citedreferenceYoshida K, Solomon DH, Haneuse S, et al. Multinomial extension of propensity score trimming methods: a simulation study. Am J Epidemiol. 2019; 188 ( 3 ): 609 - 616.
dc.identifier.citedreferenceQin J, Lawless J. Empirical likelihood and general estimating equations. Ann Stat. 1994; 22 ( 1 ): 300 - 325.
dc.identifier.citedreferenceOwen AB. Empirical Likelihood. New York: Chapman & Hall/CRC; 2001.
dc.identifier.citedreferenceChen J, Sitter RR, Wu C. Using empirical likelihood methods to obtain range restricted weights in regression estimators for surveys. Biometrika. 2002; 89 ( 1 ): 230 - 237.
dc.working.doiNOen
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Name:
sim9653_am.pdf
Size:
547.8KB
Format:
PDF
View/Open
Name:
sim9653.pdf
Size:
2.100MB
Format:
PDF
View/Open

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.