Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group
- PMID: 9802183
- DOI: 10.1002/(sici)1097-0258(19981015)17:19<2265::aid-sim918>3.0.co;2-b
Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group
Abstract
In observational studies, investigators have no control over the treatment assignment. The treated and non-treated (that is, control) groups may have large differences on their observed covariates, and these differences can lead to biased estimates of treatment effects. Even traditional covariance analysis adjustments may be inadequate to eliminate this bias. The propensity score, defined as the conditional probability of being treated given the covariates, can be used to balance the covariates in the two groups, and therefore reduce this bias. In order to estimate the propensity score, one must model the distribution of the treatment indicator variable given the observed covariates. Once estimated the propensity score can be used to reduce bias through matching, stratification (subclassification), regression adjustment, or some combination of all three. In this tutorial we discuss the uses of propensity score methods for bias reduction, give references to the literature and illustrate the uses through applied examples.
Similar articles
-
[Unbiased estimation of factorial effect by using analysis of covariance or propensity score method for observational studies in laboratory medicine].Rinsho Byori. 2012 Jul;60(7):689-97. Rinsho Byori. 2012. PMID: 22973732 Japanese.
-
Estimating effects of nursing intervention via propensity score analysis.Nurs Res. 2008 Nov-Dec;57(6):444-52. doi: 10.1097/NNR.0b013e31818c66f6. Nurs Res. 2008. PMID: 19018219 Free PMC article. Review.
-
Prognostic models and the propensity score.Int J Epidemiol. 1995 Feb;24(1):183-7. doi: 10.1093/ije/24.1.183. Int J Epidemiol. 1995. PMID: 7797341
-
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 Feb 20;26(4):734-53. doi: 10.1002/sim.2580. Stat Med. 2007. PMID: 16708349
-
Invited commentary: propensity scores.Am J Epidemiol. 1999 Aug 15;150(4):327-33. doi: 10.1093/oxfordjournals.aje.a010011. Am J Epidemiol. 1999. PMID: 10453808 Review.
Cited by
-
Long-term efficacy of mepolizumab in patients with eosinophilic granulomatosis with polyangiitis: a propensity score matching analysis in the multicenter REVEAL cohort study.Front Immunol. 2024 Oct 2;15:1457202. doi: 10.3389/fimmu.2024.1457202. eCollection 2024. Front Immunol. 2024. PMID: 39416779 Free PMC article.
-
Sex-Specific Association Patterns of Bone Microstructure and Lower Leg Arterial Calcification.Calcif Tissue Int. 2024 Oct 14. doi: 10.1007/s00223-024-01299-w. Online ahead of print. Calcif Tissue Int. 2024. PMID: 39397150
-
A preventive integrated eHealth approach for individuals with a low socioeconomic position: protocol for a realist evaluation.BMC Public Health. 2024 Oct 3;24(1):2700. doi: 10.1186/s12889-024-20113-8. BMC Public Health. 2024. PMID: 39363257 Free PMC article.
-
Propensity score matching: a tool for consumer risk modeling and portfolio underwriting.J Appl Stat. 2024 Jan 9;51(12):2481-2488. doi: 10.1080/02664763.2024.2302058. eCollection 2024. J Appl Stat. 2024. PMID: 39267709
-
Kidney Outcomes Following Angiotensin Receptor-Neprilysin Inhibitor vs Angiotensin-Converting Enzyme Inhibitor/Angiotensin Receptor Blocker Therapy for Thrombotic Microangiopathy.JAMA Netw Open. 2024 Sep 3;7(9):e2432862. doi: 10.1001/jamanetworkopen.2024.32862. JAMA Netw Open. 2024. PMID: 39264627 Free PMC article.