85
Views
4
CrossRef citations to date
0
Altmetric
Short Report

Project ECHO Revisited: Propensity Score Analysis And HCV Treatment Outcomes

ORCID Icon, , , &
Pages 149-152 | Published online: 07 Oct 2019

References

  • Arora S, Thornton K, Murata G, et al. Outcomes of treatment for hepatitis C virus infection by primary care providers. NEJM. 2011;364:2199–2207. doi:10.1056/NEJMoa100937021631316
  • D’Agostino RB Jr. Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat Med. 1998;17:2265–2281. doi:10.1002/(sici)1097-0258(19981015)17:19<2265::aid-sim918>3.0.co;2-b9802183
  • Austin PC, Grootendorst P, Anderson GM. A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: a monte carlo study. Stat Med. 2007;26:734–753. doi:10.1002/sim.258016708349
  • Patrick AR, Schneeweiss S, Brookhart MA, et al. The implications of propensity score variable selection strategies in pharmacoepidemiology: an empirical illustration. Pharmacoepidemiol Drug Saf. 2011;20:551–559. doi:10.1002/pds.209821394812
  • Austin 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;34:3661–3679. doi:10.1002/sim.660726238958
  • Xu S, Ross C, Raebel MA, Shetterly S, Blanchette C, Smith D. Use of stabilized inverse propensity scores as weights to directly estimate relative risk and its confidence intervals. Value Health. 2010;13:273–277. doi:10.1111/j.1524-4733.2009.00671.x19912596
  • Moons KG, Donders AR, Steyerberg EW, Harrell FE. Penalized maximum likelihood estimation to directly adjust diagnostic and prognostic prediction models for overoptimism: a clinical example. J Clin Epidemiol. 2004;57:1262–1270. doi:10.1016/j.jclinepi.2004.01.02015617952
  • Steyerberg EW, Vickers AJ, Cook NR, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology. 2010;21:128–138. doi:10.1097/EDE.0b013e3181c30fb220010215
  • Rubin DB. Using propensity scores to help design observational studies: application to the tobacco litigation. Health Serv Outcomes Res Method. 2001;2:169–188. doi:10.1023/A:1020363010465
  • Stuart EA. Matching methods for causal inference: a review and a look forward. Stat Sci. 2010;25:1–21. doi:10.1214/09-STS31320871802
  • Cole SR, Hernan MA. Constructing inverse probability weights for marginal structural models. Am J Epidemiol. 2008;168:656–664. doi:10.1093/aje/kwn16418682488
  • Rubin DB. The design versus the analysis of observational studies for causal effects: parallels with the design of randomized trials. Stat Med. 2007;26:20–36. doi:10.1002/sim.273917072897
  • Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70:41–55. doi:10.1093/biomet/70.1.41
  • Shadish W, Luellen J, Clark M. Propensity scores and quasi-experiments: a testimony to the practical side of Lee Sechrest In: Bootzin RR, McKnight PE, editors. Strengthening Research Methodology: Psychological Measurement and Evaluation. Washington, DC: American Psychological Association; 2006:143–157.
  • Weitzen S, Lapane KL, Toledano AY, Hume AL, Mor V. Principles for modeling propensity scores in medical research: a systematic literature review. Pharmacoepidemiol Drug Saf. 2004;13:841–853. doi:10.1002/pds.96915386709
  • Biondi-Zoccai G, Romagnoli E, Agostoni P, et al. Are propensity scores really superior to standard multivariable analysis? Contemp Clin Trials. 2011;32:731–740. doi:10.1016/j.cct.2011.05.00621616172
  • Cavuto S, Bravi F, Grassi MC, Apolone G. Propensity score for the abnalysis of observational data: an introduction and an illustrative example. Drug Dev Res. 2006;67:208–216. doi:10.1002/ddr.20079
  • Thoemmes FJ, Kim ES. A systematic review of propensity score methods in the social sciences. Multivariate Behav Res. 2011;46:90–118. doi:10.1080/00273171.2011.54047526771582
  • Bourliere M, Pietri O. Hepatitis C virus therapy: no one will be left behind. Int J Antimicrob Agents. 2019;53:755–760. doi:10.1016/j.ijantimicag.2018.12.01030605721