990
Views
2
CrossRef citations to date
0
Altmetric
Research Article

Contrast-specific propensity scores

ORCID Icon &
Pages 1-8 | Received 25 Aug 2020, Accepted 23 May 2021, Published online: 05 Jul 2021

References

  • Haukoos JS, Lewis RJ. The propensity score. JAMA. 2015;314:1637–1638.
  • Rosenthal R, Rosnow RL, Rubin DB. Contrasts and effect sizes in behavioral research: a correlational approach. Cambridge University Press; 2000.
  • Snedecor GW, Cochran WG. Statistical methods. 1st ed. Iowa State Univerisity Press; 1967.
  • Wu CJ, Hamada MS. Experiments: planning, analysis, and optimization. Vol. 552. John Wiley & Sons; 2011.
  • Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70:41–55.
  • Austin PC. A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003. Stat Med. 2008;27:2037–2049.
  • Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res. 2011;46:399–424.
  • Brookhart MA, Schneeweiss S, Rothman KJ, et al. Variable selection for propensity score models. Am J Epidemiol. 2006;163:1149–1156.
  • Caliendo M, Kopeinig S. Some practical guidance for the implementation of propensity score matching. J Econ Surv. 2008;22:31–72.
  • Cintina I, Love I. Re-evaluating microfinance: evidence from propensity score matching. World Bank Econ Rev. 2019;33:95–115.
  • d‘Agostino RB. Tutorial in biostatistics: propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat Med. 1998;17:2265–2281.
  • Dehejia RH, Wahba S. Propensity score-matching methods for nonexperimental causal studies. Rev Econ Stat. 2002;84:151–161.
  • Guo S, Fraser MW. Propensity score analysis: statistical methods and applications. Vol. 11. SAGE publications; 2014.
  • Klompmaker S, van Hilst J, Wellner UF, et al. Outcomes after minimally-invasive versus open pancreatoduodenectomy: a pan-european propensity score matched study. Ann Surg. 2020;271:356–363.
  • Thourani VH, Kodali S, Makkar RR, et al. Transcatheter aortic valve replacement versus surgical valve replacement in intermediate-risk patients: a propensity score analysis. The Lancet. 2016;387:2218–2225.
  • Imbens GW. The role of the propensity score in estimating dose-response functions. Biometrika. 2000;87:706–710.
  • Horvitz DG, Thompson DJ. A generalization of sampling without replacement from a finite universe. J Am Stat Assoc. 1952;47:663–685.
  • Lechner M. Identification and estimation of causal effects of multiple treatments under the conditional independence assumption. In: Lechner M, Pfeiffer F, editors. Econometric evaluation of labour market policies. Heidelberg: Physica; 2001. p. 43–58.
  • Imai K, van Dyk DA. Causal inference with general treatment regimes. J Am Stat Assoc. 2004;99:854–866.
  • LaLonde RJ. Evaluating the econometric evaluations of training programs with experimental data. Am Econ Rev. 1986;76:604–620.
  • Kaplan K, Mashash M, Williams R, et al. Effect of light flashes vs sham therapy during sleep with adjunct cognitive behavioral therapy on sleep quality among adolescents: a randomized clinical trial. JAMA Netw Open. 2019;2:Article ID e1911944.
  • Rubin DB. Discussion of randomization analysis of experimental data in the fisher randomization test. J Am Stat Assoc. 1980;75:591–593.
  • Rubin D. Estimating causal effects of treatments in randomized and nonrandomized studies. J Educ Psychol. 1974;66:688–701.
  • Imbens GW, Rubin DB. Causal inference in statistics, social, and biomedical sciences. Cambridge University Press; 2015.
  • Rubin DB. Formal modes of statistical inference for causal inference. J Stat Plan Inference. 1990;25:279–292.
  • Gutman R, Rubin D. Estimation of causal effects of binary treatments in unconfounded studies with one continuous covariate. Stat Methods Med Res. 2017;26:1199–1215.
  • Gutman R, Rubin DB. Robust estimation of causal effects of binary treatments in unconfounded studies with dichotomous outcomes. Stat Med. 2013;32:1795–1814.
  • Rubin DB. Matching to remove bias in observational studies. Biometrics. 1973;29(1):159–183.
  • Cochran WG, Rubin DB. Controlling bias in observational studies: a review. Sankhyā. 1973;35:417–446.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.