Tags

Type your tag names separated by a space and hit enter

Conditioning on the propensity score can result in biased estimation of common measures of treatment effect: a Monte Carlo study.
Stat Med. 2007 Feb 20; 26(4):754-68.SM

Abstract

Propensity score methods are increasingly being used to estimate causal treatment effects in the medical literature. Conditioning on the propensity score results in unbiased estimation of the expected difference in observed responses to two treatments. The degree to which conditioning on the propensity score introduces bias into the estimation of the conditional odds ratio or conditional hazard ratio, which are frequently used as measures of treatment effect in observational studies, has not been extensively studied. We conducted Monte Carlo simulations to determine the degree to which propensity score matching, stratification on the quintiles of the propensity score, and covariate adjustment using the propensity score result in biased estimation of conditional odds ratios, hazard ratios, and rate ratios. We found that conditioning on the propensity score resulted in biased estimation of the true conditional odds ratio and the true conditional hazard ratio. In all scenarios examined, treatment effects were biased towards the null treatment effect. However, conditioning on the propensity score did not result in biased estimation of the true conditional rate ratio. In contrast, conventional regression methods allowed unbiased estimation of the true conditional treatment effect when all variables associated with the outcome were included in the regression model. The observed bias in propensity score methods is due to the fact that regression models allow one to estimate conditional treatment effects, whereas propensity score methods allow one to estimate marginal treatment effects. In several settings with non-linear treatment effects, marginal and conditional treatment effects do not coincide.

Authors+Show Affiliations

Institute for Clinical Evaluative Sciences, Toronto, Ont., Canada. peter.austin@ices.on.caNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Comparative Study
Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

16783757

Citation

Austin, Peter C., et al. "Conditioning On the Propensity Score Can Result in Biased Estimation of Common Measures of Treatment Effect: a Monte Carlo Study." Statistics in Medicine, vol. 26, no. 4, 2007, pp. 754-68.
Austin PC, Grootendorst P, Normand SL, et al. Conditioning on the propensity score can result in biased estimation of common measures of treatment effect: a Monte Carlo study. Stat Med. 2007;26(4):754-68.
Austin, P. C., Grootendorst, P., Normand, S. L., & Anderson, G. M. (2007). Conditioning on the propensity score can result in biased estimation of common measures of treatment effect: a Monte Carlo study. Statistics in Medicine, 26(4), 754-68.
Austin PC, et al. Conditioning On the Propensity Score Can Result in Biased Estimation of Common Measures of Treatment Effect: a Monte Carlo Study. Stat Med. 2007 Feb 20;26(4):754-68. PubMed PMID: 16783757.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Conditioning on the propensity score can result in biased estimation of common measures of treatment effect: a Monte Carlo study. AU - Austin,Peter C, AU - Grootendorst,Paul, AU - Normand,Sharon-Lise T, AU - Anderson,Geoffrey M, PY - 2006/6/20/pubmed PY - 2007/3/28/medline PY - 2006/6/20/entrez SP - 754 EP - 68 JF - Statistics in medicine JO - Stat Med VL - 26 IS - 4 N2 - Propensity score methods are increasingly being used to estimate causal treatment effects in the medical literature. Conditioning on the propensity score results in unbiased estimation of the expected difference in observed responses to two treatments. The degree to which conditioning on the propensity score introduces bias into the estimation of the conditional odds ratio or conditional hazard ratio, which are frequently used as measures of treatment effect in observational studies, has not been extensively studied. We conducted Monte Carlo simulations to determine the degree to which propensity score matching, stratification on the quintiles of the propensity score, and covariate adjustment using the propensity score result in biased estimation of conditional odds ratios, hazard ratios, and rate ratios. We found that conditioning on the propensity score resulted in biased estimation of the true conditional odds ratio and the true conditional hazard ratio. In all scenarios examined, treatment effects were biased towards the null treatment effect. However, conditioning on the propensity score did not result in biased estimation of the true conditional rate ratio. In contrast, conventional regression methods allowed unbiased estimation of the true conditional treatment effect when all variables associated with the outcome were included in the regression model. The observed bias in propensity score methods is due to the fact that regression models allow one to estimate conditional treatment effects, whereas propensity score methods allow one to estimate marginal treatment effects. In several settings with non-linear treatment effects, marginal and conditional treatment effects do not coincide. SN - 0277-6715 UR - https://www.unboundmedicine.com/medline/citation/16783757/Conditioning_on_the_propensity_score_can_result_in_biased_estimation_of_common_measures_of_treatment_effect:_a_Monte_Carlo_study_ DB - PRIME DP - Unbound Medicine ER -