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Martens EP, Pestman WR, Klungel OH.. Re: Conditioning on the propensity score can result in biased estimation of common measures of treatment effect: a Monte Carlo study

Institute for Clinical Evaluative Sciences, Toronto, Ont., Canada.
Statistics in Medicine (Impact Factor: 2.04). 02/2007; 26(4):754-68. DOI: 10.1002/sim.2618
Source: PubMed

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.

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    • "We performed a propensity score analysis to adjust for imbalances in baseline characteristics between patients with-and without maintenance TMS (D'Agostino, 1998). For this purpose, we first developed a nonparsimonious logistic regression model to derive a propensity score for patients receiving TMS based on all the covariates (Austin et al., 2007). This logistic regression model yielded a c-statistic (Area Under Curve) of 0.73, indicating a satisfactory ability to discriminate between maintenance TMS or no additional TMS treatment. "
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    ABSTRACT: The effectiveness of repetitive transcranial magnetic stimulation (TMS) is well established while studies of maintenance TMS are lacking. We aim here to determine whether maintenance is associated to a decrease in the relapse rate of depression, following successful acute treatment. We enrolled 59 consecutive patients with pharmacoresistant depression who have responded (>50% decrease in symptom severity) up to 6 weeks of acute TMS treatment. These patients received either 20 weeks of maintenance TMS (n=37) or no additional TMS treatment (n=22). We performed propensity adjusted-analysis to examine the association between the relapse rate over this 20-week period and maintenance TMS. Propensity analysis eliminated differences in baseline characteristics between patient with and without maintenance TMS and approximated the conditions of random site-of-treatment assignment. At 20 weeks, relapse rate was significantly different between the two groups (p=0.004, propensity analysis): 14 patients in the maintenance TMS group (37.8%) vs. 18 in the non-maintenance TMS group (81.8%), with an adjusted Hazard Ratio (HR)=0.288 (0.124-0.669). Maintenance TMS was associated with a significantly lower relapse rate in patients with pharmacoresistant depression in routine practice among responders.
    Journal of Affective Disorders 06/2013; 151(1). DOI:10.1016/j.jad.2013.05.062 · 3.71 Impact Factor
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    • "The choice of the odds ratio as a measure of treatment effect has been debated. Indeed, the choice of the appropriate PS based estimators for conditional and marginal non-linear treatment effects has been thoroughly discussed in the recent literature [21,36-38]. Actually, the problem with OR, usually described as non-collapsibility, refers to the fact that conditional and marginal effects might differ unless the true effect is null [21,39,40]. "
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    BMC Medical Research Methodology 05/2012; 12(1):70. DOI:10.1186/1471-2288-12-70 · 2.17 Impact Factor
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    • "We performed a propensity score analysis to adjust for imbalances in baseline characteristics between patients with monotherapy and polypharmacy. For this purpose, we first developed a nonparsimonious logistic regression model to derive a propensity score for patients receiving polypharmacy based on all the covariates [44]. This logistic regression model yielded a c-statistic of 0.80, indicating a strong ability to discriminate between monotherapy and polypharmacy. "
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