An Introduction to Propensity Score Analysis: Checklist for Clinical Researches

Nazlı Totik; Sevinç Püren Yücel Karakaya; Zeliha Nazan Alparslan

doi:10.58600/eurjther1813

Authors

Nazlı Totik Department of Biostatistics, Faculty of Medicine, Çukurova University, Adana, Turkey https://orcid.org/0000-0003-1938-1221
Sevinç Püren Yücel Karakaya Department of Biostatistics, Faculty of Medicine, Çukurova University, Adana, Turkey https://orcid.org/0000-0002-5768-3549
Zeliha Nazan Alparslan Department of Biostatistics, Faculty of Medicine, Çukurova University, Adana, Turkey https://orcid.org/0000-0001-8497-8281

DOI:

https://doi.org/10.58600/eurjther1813

Keywords:

Observational study, propensity score, treatment effect, selection bias, STROBE

Abstract

Background: Propensity score analysis is a widely used method to estimate treatment effect in dealing with the selection bias (i.e. lack of randomization) of observational studies. Although, there are relatively many guidelines in the literature for the adoption of this analysis, no checklists exist.

Objective: In this study, we propose a basic guideline for propensity score analysis, a tutorial that may be used to improve the quality of studies which implement this analysis. Additionally, in line with this guideline, we present an easy-to-use checklist which will assist researchers in the analysis process.

Conclusion: In light of the principles in this guideline/checklist, we propose that minor updates be considered for STROBE.

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References

Stürmer T, Joshi M, Glynn RJ, Avorn J, Rothman KJ, Schneeweiss S. (2006) A review of the application of propensity score methods yielded increasing use, advantages in specific settings, but not substantially different estimates compared with conventional multivariable methods. Journal of clinical epidemiology. 9(5), 437-e1. https://doi.org/10.1016/j.jclinepi.2005.07.004

Yao XI, Wang X, Speicher PJ. Hwang ES, Cheng P, Harpole DH, Pang HH (2017) Reporting and guidelines in propensity score analysis: a systematic review of cancer and cancer surgical studies. JNCI: Journal of the National Cancer Institute. 109(8), djw323. https://doi.org/10.1093/jnci/djw323

Olmos A, Govindasamy P. (2015) Propensity scores: a practical introduction using R. Journal of MultiDisciplinary Evaluation. 11(25), 68-88. https://doi.org/10.56645/jmde.v11i25.431

Pattanayak CW, Rubin DB, Zell ER (2011) Propensity score methods for creating covariate balance in observational studies. Revista Española de Cardiología (English Edition). 64(10), 897-903. https://doi.org/10.1016/j.rec.2011.06.008

Heinze G, Jüni P (2011) An overview of the objectives of and the approaches to propensity score analyses. European heart journal. 32(14), 1704-1708. https://doi.org/10.1093/eurheartj/ehr031

Luo Z, Gardiner JC, Bradley CJ (2010) Applying propensity score methods in medical research: pitfalls and prospects. Medical Care Research and Review. 67(5), 528-554 https://doi.org/10.1177/1077558710361486

Austin PC, Grootendorst P, Anderson GM. (2007) A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: a Monte Carlo study. Statistics in medicine. 26(4), 734-753. https://doi.org/10.1002/sim.2580

Austin PC (2011) An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate behavioral research. 46(3), 399-424. https://doi.org/10.1080/00273171.2011.568786

Rosenbaum PR, Rubin DB. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika. 70(1), 41-55. https://doi.org/10.1093/biomet/70.1.41 [10] Klompmaker S, van Hilst J, Wellner UF, Busch OR, Coratti A, D'Hondt M, Lips DJ (2020) Outcomes after minimally-invasive versus open pancreatoduodenectomy: a pan-European propensity score matched study. Annals of surgery. 271(2), 356-363. https://doi.org/10.1097/SLA.0000000000002850 [11] Signori A, Pellegrini F, Bovis F, Carmisciano L, De Moor C, Sormani MP (2020) Comparison of Placebos and Propensity Score Adjustment in Multiple Sclerosis Nonrandomized Studies. JAMA neurology. https://doi.org/10.1001/jamaneurol.2020.0678.

Lapointe-Shaw L, Bell CM, Austin PC, Abrahamyan L, Ivers NM, Li P, Dolovich L (2020) Community pharmacy medication review, death and re-admission after hospital discharge: a propensity score-matched cohort study. BMJ Quality & Safety. 29(1), 41-51. https://doi.org/10.1136/bmjqs-2019-009545

Lee J, Little TD (2017) A practical guide to propensity score analysis for applied clinical research. Behaviour research and therapy. 98, 76-90. https://doi.org/10.1016/j.brat.2017.01.005

Valojerdi AE, Janani L (2018) A brief guide to propensity score analysis. Medical journal of the Islamic Republic of Iran. 32, 122. https://doi.org/10.14196/mjiri.32.122

Elze MC, Gregson J, Baber U, Williamson E, Sartori S, Mehran R, Pocock SJ (2017) Comparison of propensity score methods and covariate adjustment: evaluation in 4 cardiovascular studies. Journal of the American College of Cardiology. 69(3), 345-357. https://doi.org/10.1016/j.jacc.2016.10.060

Stuart EA (2010) Matching methods for causal inference: A review and a look forward. Statistical science: a review journal of the Institute of Mathematical Statistics. 25(1), 1. https://doi.org/10.1214/09-STS313

Deb S, Austin PC, Tu JV, Ko DT, Mazer CD, Kiss A, Fremes SE (2016) A review of propensity-score methods and their use in cardiovascular research. Canadian Journal of Cardiology. 32(2), 259-265. https://doi.org/10.1016/j.cjca.2015.05.015

Austin PC (2011) A tutorial and case study in propensity score analysis: an application to estimating the effect of in-hospital smoking cessation counseling on mortality. Multivariate behavioral research. 46(1), 119-151. https://doi.org/10.1080/00273171.2011.540480

Brookhart MA, Schneeweiss S, Rothman KJ, Glynn RJ, Avorn J, Stürmer T (2006) Variable selection for propensity score models. American journal of epidemiology. 163(12), 1149-1156. https://doi.org/10.1093/aje/kwj149

de Vries BBP, van Smeden M, Groenwold RH (2018) Propensity Score Estimation Using Classification and Regression Trees in the Presence of Missing Covariate Data. Epidemiologic Methods, 7(1). https://doi.org/10.1515/em-2017-0020

Zhou J (2015) Comparison of approaches for handling missingness in covariates for propensity score models. PhD Thesis, The Pennsylvania State University. https://doi.org/10.1186/s12874-020-01053-4

Belitser SV, Martens EP, Pestman WR, Groenwold RH, De Boer A, Klungel OH (2011) Measuring balance and model selection in propensity score methods. Pharmacoepidemiology and drug safety. 20(11), 1115-1129. https://doi.org/10.1002/pds.2188

Weitzen S, Lapane KL, Toledano AY, Hume AL, Mor V (2004) Principles for modeling propensity scores in medical research: a systematic literature review. Pharmacoepidemiology and drug safety. 13(12), 841-853. https://doi.org/10.1002/pds.969.

Austin, P. C (2011) Optimal caliper widths for propensity‐score matching when estimating differences in means and differences in proportions in observational studies. Pharmaceutical statistics. 10(2), 150-161. https://doi.org/10.1002/pst.433.

Campbell MJ (2017) What is propensity score modelling? https://doi.org/10.1136/emermed-2016-206542

Dehejia RH, Wahba S (2002) Propensity score-matching methods for nonexperimental causal studies. Review of Economics and statistics, 84(1), 151-161 https://doi.org/10.1162/003465302317331982

Harder VS, Stuart EA, Anthony JC (2010) Propensity score techniques and the assessment of measured covariate balance to test causal associations in psychological research. Psychological methods. 15(3), 234. https://doi.org/10.1037/a0019623

Austin PC, Stuart EA (2015) Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Statistics in medicine, 34(28), 3661-3679. https://doi.org/10.1002/sim.6607

Yoshida K, Solomon DH, Haneuse S, Kim SC, Patorno E, Tedeschi SK, Glynn RJ (2019) Multinomial extension of propensity score trimming methods: a simulation study. American Journal of Epidemiology, 188(3), 609-616. https://doi.org/10.1093/aje/kwy263

Ali MS, Groenwold RH, Belitser SV, Pestman WR, Hoes AW, Roes KC, Klungel OH (2015) Reporting of covariate selection and balance assessment in propensity score analysis is suboptimal: a systematic review. Journal of clinical epidemiology, ; 68(2), 122-131.

https://doi.org/10.1016/j.jclinepi.2014.08.011

Kuss O, Blettner M, Börgermann J (2016) Propensity Score: An Alternative Method of Analyzing Treatment Effects: Part 23 of a Series on Evaluation of Scientific Publications. Deutsches Ärzteblatt International, 113(35-36), 597. https://doi.org/10.3238/arztebl.2016.0597

Alam S, Moodie EE, Stephens DA (2019) Should a propensity score model be super? The utility of ensemble procedures for causal adjustment. Statistics in medicine. 38(9), 1690-1702. https://doi.org/10.1002/sim.8075

Lunt M (2014) Propensity analysis in Stata revision: 1.1. Documento disponible en: http://personalpages.manchester.ac.uk/staff/mark.lunt/propensity_guide.pdf

Lanehart RE, de Gil PR, Kim ES, Bellara AP, Kromrey JD, Lee RS (2012) Propensity score analysis and assessment of propensity score approaches using SAS procedures. In Proceedings of the SAS Global Forum Conference (pp. 22-25). Cary, North Carolina: SAS Institute Inc.

von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP, et al (2007) The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. PLoS Med. 4(10):e296. https://doi.org/10.1371/journal.pmed.0040296