Comparing treatment effects after adjustment with multivariable Cox proportional hazards regression and propensity score methods

PURPOSE: To compare adjusted effects of drug treatment for hypertension on the risk of stroke from propensity score (PS) methods with a multivariable Cox proportional hazards (Cox PH) regression in an observational study with censored data. METHODS: From two prospective population-based cohort studies in The Netherlands a selection of subjects was used who either received drug treatment for hypertension (n = 1293) or were untreated 'candidates' for treatment (n = 954). A multivariable Cox PH was performed on the risk of stroke using eight covariates along with three PS methods. RESULTS: In multivariable Cox PH regression the adjusted hazard ratio (HR) for treatment was 0.64 (CI(95%): 0.42, 0.98). After stratification on the PS the HR was 0.58 (CI(95%): 0.38, 0.89). Matching on the PS yielded a HR of 0.49 (CI(95%): 0.27, 0.88), whereas adjustment with a continuous PS gave similar results as Cox regression. When more covariates were added (not possible in multivariable Cox model) a similar reduction in HR was reached by all PS methods. The inclusion of a simulated balanced covariate gave largest changes in HR using the multivariable Cox model and matching on the PS. CONCLUSIONS: In PS methods in general a larger number of confounders can be used. In this data set matching on the PS is sensitive to small changes in the model, probably because of the small number of events. Stratification, and covariate adjustment, were less sensitive to the inclusion of a non-confounder than multivariable Cox PH regression. Attention should be paid to PS model building and balance checking.     

Principles for modeling propensity scores in medical research: a systematic literature review

PURPOSE: To document which established criteria for logistic regression modeling researchers consider when using propensity scores in observational studies. METHODS: We performed a systematic review searching Medline and Science Citation to identify observational studies published in 2001 that addressed clinical questions using propensity score methods to adjust for treatment assignment. We abstracted aspects of propensity score model development (e.g. variable selection criteria, continuous variables included in correct functional form, interaction inclusion criteria), model discrimination and goodness of fit for 47 studies meeting inclusion criteria. RESULTS: We found few studies reporting on the propensity score model development or evaluation of model fit. CONCLUSIONS: Reporting of aspects related to propensity score model development is limited and raises questions about the value of these principles in developing propensity scores from which unbiased treatment effects are estimated.     

Weaknesses of goodness-of-fit tests for evaluating propensity score models: the case of the omitted confounder

PURPOSE: Propensity scores are used in observational studies to adjust for confounding, although they do not provide control for confounders omitted from the propensity score model. We sought to determine if tests used to evaluate logistic model fit and discrimination would be helpful in detecting the omission of an important confounder in the propensity score. METHODS: Using simulated data, we estimated propensity scores under two scenarios: (1) including all confounders and (2) omitting the binary confounder. We compared the propensity score model fit and discrimination under each scenario, using the Hosmer-Lemeshow goodness-of-fit (GOF) test and the c-statistic. We measured residual confounding in treatment effect estimates adjusted by the propensity score omitting the confounder. RESULTS: The GOF statistic and discrimination of propensity score models were the same for models excluding an important predictor of treatment compared to the full propensity score model. The GOF test failed to detect poor model fit for the propensity score model omitting the confounder. C-statistics under both scenarios were similar. Residual confounding was observed from using the propensity score excluding the confounder (range: 1-30%). CONCLUSIONS: Omission of important confounders from the propensity score leads to residual confounding in estimates of treatment effect. However, tests of GOF and discrimination do not provide information to detect missing confounders in propensity score models. Our findings suggest that it may not be necessary to compute GOF statistics or model discrimination when developing propensity score models.     

Applying propensity scores estimated in a full cohort to adjust for confounding in subgroup analyses

BACKGROUND: A correctly specified propensity score (PS) estimated in a cohort ("cohort PS") should, in expectation, remain valid in a subgroup population. OBJECTIVE: We sought to determine whether using a cohort PS can be validly applied to subgroup analyses and, thus, add efficiency to studies with many subgroups or restricted data. METHODS: In each of three cohort studies, we estimated a cohort PS, defined five subgroups, and then estimated subgroup-specific PSs. We compared difference in treatment effect estimates for subgroup analyses adjusted by cohort PSs versus subgroup-specific PSs. Then, over 10 million times, we simulated a population with known characteristics of confounding, subgroup size, treatment interactions, and treatment effect and again assessed difference in point estimates. RESULTS: We observed that point estimates in most subgroups were substantially similar with the two methods of adjustment. In simulations, the effect estimates differed by a median of 3.4% (interquartile (IQ) range 1.3-10.0%). The IQ range exceeded 10% only in cases where the subgroup had 相似文献   

An application of propensity score matching using claims data

BACKGROUND: Confounding by indication is a common problem in pharmacoepidemiology, where predictors of treatment also have prognostic value for the outcome of interest. The tools available to the epidemiologist that can be used to mitigate the effects of confounding by indication often have limits with respect to the number of variables that can be simultaneously incorporated as components of the confounding. This constraint becomes particularly apparent in the context of a rich data source (such as administrative claims data), applied to the study of an outcome that occurs infrequently. In such settings, there will typically be many more variables available for control as potential confounders than traditional epidemiologic techniques will allow. METHODS: One tool that can indirectly permit control of a large number of variables is the propensity score approach. This paper illustrates the application of the propensity score to a study conducted in an administrative database, and raises critical issues to be addressed in such an analysis. In this example, the effect of statin therapy on the occurrence of myocardial infarction was examined, and numerous potential confounders of this association were adjusted simultaneously using a propensity score to form matched cohorts of statin initiators and non-initiators. RESULTS: The incidence of myocardial infarction observed in the statin treated cohort was lower than the incidence in the untreated cohort, and the magnitude of this effect was consistent with results from randomized placebo controlled clinical trials of statin therapy. CONCLUSIONS: This example illustrates how confounding by indication can be mitigated by the propensity score matching technique. Concerns remain over the generalizability of estimates obtained from such a study, and how to know when propensity scores are removing bias, since apparent balance between compared groups on measured variables could leave variables not included in the propensity score unbalanced and lead to confounded effect estimates.     

基于倾向性评分匹配的老年患者低血糖危险因素分析

目的:分析老年患者低血糖的相关危险因素,为降低老年患者特别是老年非糖尿病患者低血糖风险提供依据.方法:纳入2018年1月至2019年12月合肥市第三人民医院住院的老年患者病例合计17 132例,低血糖组47例,非低血糖组17 085例.运用倾向性评分匹配对低血糖组和非低血糖组进行1 ∶ 1匹配.匹配后进行单因素分析后,...