Propensity score analysis methods with balancing constraints: A Monte Carlo study

2021 ◽  
pp. 096228022098351
Author(s):  
Yan Li ◽  
Liang Li
Keyword(s):  
Monte Carlo ◽  
Propensity Score ◽  
Propensity Scores ◽  
Probability Weighting ◽  
Inverse Probability ◽  
Propensity Score Model ◽  
Propensity Score Weighting ◽  
Estimation Performance ◽  
Score Model ◽  
Weighting Methods

The inverse probability weighting is an important propensity score weighting method to estimate the average treatment effect. Recent literature shows that it can be easily combined with covariate balancing constraints to reduce the detrimental effects of excessively large weights and improve balance. Other methods are available to derive weights that balance covariate distributions between the treatment groups without the involvement of propensity scores. We conducted comprehensive Monte Carlo experiments to study whether the use of covariate balancing constraints circumvent the need for correct propensity score model specification, and whether the use of a propensity score model further improves the estimation performance among methods that use similar covariate balancing constraints. We compared simple inverse probability weighting, two propensity score weighting methods with balancing constraints (covariate balancing propensity score, covariate balancing scoring rule), and two weighting methods with balancing constraints but without using the propensity scores (entropy balancing and kernel balancing). We observed that correct specification of the propensity score model remains important even when the constraints effectively balance the covariates. We also observed evidence suggesting that, with similar covariate balance constraints, the use of a propensity score model improves the estimation performance when the dimension of covariates is large. These findings suggest that it is important to develop flexible data-driven propensity score models that satisfy covariate balancing conditions.

scholarly journals Causal Subclassification Tree Algorithm and Robust Causal Effect Estimation via Subclassification

2020 ◽  
Vol 10 (1) ◽  
pp. 40
Author(s):  
Tomoshige Nakamura ◽  
Mihoko Minami
Keyword(s):  
Propensity Score ◽  
Propensity Scores ◽  
Causal Effect ◽  
Model Misspecification ◽  
Robust Estimator ◽  
Propensity Score Model ◽  
Confounding Variables ◽  
Score Model ◽  
Effect Estimation ◽  
Weighting Methods

In observational studies, the existence of confounding variables should be attended to, and propensity score weighting methods are often used to eliminate their e ects. Although many causal estimators have been proposed based on propensity scores, these estimators generally assume that the propensity scores are properly estimated. However, researchers have found that even a slight misspecification of the propensity score model can result in a bias of estimated treatment effects. Model misspecification problems may occur in practice, and hence, using a robust estimator for causal effect is recommended. One such estimator is a subclassification estimator. Wang, Zhang, Richardson, & Zhou (2020) presented the conditions necessary for subclassification estimators to have $\sqrt{N}$-consistency and to be asymptotically well-defined and suggested an idea how to construct subclasses.

scholarly journals Propensity score weighting under limited overlap and model misspecification

2020 ◽  
Vol 29 (12) ◽  
pp. 3721-3756
Author(s):  
Yunji Zhou ◽  
Roland A Matsouaka ◽  
Laine Thomas
Keyword(s):  
Propensity Score ◽  
Mean Squared Error ◽  
Specific Treatment ◽  
Inverse Probability Weighting ◽  
Alternative Methods ◽  
Probability Weighting ◽  
Inverse Probability ◽  
Propensity Score Model ◽  
Propensity Score Weighting ◽  
Wide Range

Propensity score weighting methods are often used in non-randomized studies to adjust for confounding and assess treatment effects. The most popular among them, the inverse probability weighting, assigns weights that are proportional to the inverse of the conditional probability of a specific treatment assignment, given observed covariates. A key requirement for inverse probability weighting estimation is the positivity assumption, i.e. the propensity score must be bounded away from 0 and 1. In practice, violations of the positivity assumption often manifest by the presence of limited overlap in the propensity score distributions between treatment groups. When these practical violations occur, a small number of highly influential inverse probability weights may lead to unstable inverse probability weighting estimators, with biased estimates and large variances. To mitigate these issues, a number of alternative methods have been proposed, including inverse probability weighting trimming, overlap weights, matching weights, and entropy weights. Because overlap weights, matching weights, and entropy weights target the population for whom there is equipoise (and with adequate overlap) and their estimands depend on the true propensity score, a common criticism is that these estimators may be more sensitive to misspecifications of the propensity score model. In this paper, we conduct extensive simulation studies to compare the performances of inverse probability weighting and inverse probability weighting trimming against those of overlap weights, matching weights, and entropy weights under limited overlap and misspecified propensity score models. Across the wide range of scenarios we considered, overlap weights, matching weights, and entropy weights consistently outperform inverse probability weighting in terms of bias, root mean squared error, and coverage probability.

An Introduction to the Augmented Inverse Propensity Weighted Estimator

2010 ◽  
Vol 18 (1) ◽  
pp. 36-56 ◽  
Author(s):  
Adam N. Glynn ◽  
Kevin M. Quinn
Keyword(s):  
Monte Carlo ◽  
Propensity Score ◽  
Regression Model ◽  
Interesting Property ◽  
Outcome Variable ◽  
Monte Carlo Experiment ◽  
Finite Sample ◽  
Propensity Score Model ◽  
Matching Estimator ◽  
Score Model

In this paper, we discuss an estimator for average treatment effects (ATEs) known as the augmented inverse propensity weighted (AIPW) estimator. This estimator has attractive theoretical properties and only requires practitioners to do two things they are already comfortable with: (1) specify a binary regression model for the propensity score, and (2) specify a regression model for the outcome variable. Perhaps the most interesting property of this estimator is its so-called “double robustness.” Put simply, the estimator remains consistent for the ATE if either the propensity score model or the outcome regression is misspecified but the other is properly specified. After explaining the AIPW estimator, we conduct a Monte Carlo experiment that compares the finite sample performance of the AIPW estimator to three common competitors: a regression estimator, an inverse propensity weighted (IPW) estimator, and a propensity score matching estimator. The Monte Carlo results show that the AIPW estimator has comparable or lower mean square error than the competing estimators when the propensity score and outcome models are both properly specified and, when one of the models is misspecified, the AIPW estimator is superior.

scholarly journals Chasing Balance and Other Recommendations for Improving Nonparametric Propensity Score Models

2017 ◽  
Vol 5 (2) ◽  
Author(s):  
Beth Ann Griffin ◽  
Daniel F. McCaffrey ◽  
Daniel Almirall ◽  
Lane F. Burgette ◽  
Claude Messan Setodji
Keyword(s):  
Propensity Score ◽  
Propensity Scores ◽  
Model Fit ◽  
Estimation Model ◽  
Propensity Score Model ◽  
Treatment Groups ◽  
Covariate Balance ◽  
Learning Technique ◽  
Accurate Treatment ◽  
Score Model

Abstract:In this article, we carefully examine two important implementation issues when estimating propensity scores using generalized boosted models (GBM), a promising machine learning technique. First, we examine which of the following methods for tuning GBM lead to better covariate balance and inferences about causal effects: pursuing covariate balance between the treatment groups or tuning the propensity score model on the basis of a model fit criterion. Second, we examine how well GBM can handle irrelevant covariates that are included in the estimation model. We find that chasing balance rather than model fit when estimating propensity scores yielded better covariate balance and more accurate treatment effect estimates. Additionally, we find that adding irrelevant covariates to GBM increased imbalance and bias in the treatment effects. The findings from this paper have useful implications for other work focused on improving methods for estimating propensity scores.

scholarly journals Propensity Score Weighting for Causal Inference with Clustered Data

2018 ◽  
Vol 6 (2) ◽  
Author(s):  
Shu Yang
Keyword(s):  
Propensity Score ◽  
Causal Inference ◽  
Clustered Data ◽  
Overweight And Obesity ◽  
Average Treatment Effect ◽  
Treatment Assignment ◽  
Propensity Score Model ◽  
Propensity Score Weighting ◽  
Score Model ◽  
Cluster Level

AbstractPropensity score weighting is a tool for causal inference to adjust for measured confounders in observational studies. In practice, data often present complex structures, such as clustering, which make propensity score modeling and estimation challenging. In addition, for clustered data, there may be unmeasured cluster-level covariates that are related to both the treatment assignment and outcome. When such unmeasured cluster-specific confounders exist and are omitted in the propensity score model, the subsequent propensity score adjustment may be biased. In this article, we propose a calibration technique for propensity score estimation under the latent ignorable treatment assignment mechanism, i. e., the treatment-outcome relationship is unconfounded given the observed covariates and the latent cluster-specific confounders. We impose novel balance constraints which imply exact balance of the observed confounders and the unobserved cluster-level confounders between the treatment groups. We show that the proposed calibrated propensity score weighting estimator is doubly robust in that it is consistent for the average treatment effect if either the propensity score model is correctly specified or the outcome follows a linear mixed effects model. Moreover, the proposed weighting method can be combined with sampling weights for an integrated solution to handle confounding and sampling designs for causal inference with clustered survey data. In simulation studies, we show that the proposed estimator is superior to other competitors. We estimate the effect of School Body Mass Index Screening on prevalence of overweight and obesity for elementary schools in Pennsylvania.

scholarly journals The performance of inverse probability of treatment weighting and full matching on the propensity score in the presence of model misspecification when estimating the effect of treatment on survival outcomes

2015 ◽  
Vol 26 (4) ◽  
pp. 1654-1670 ◽  
Author(s):  
Peter C Austin ◽  
Elizabeth A Stuart
Keyword(s):  
Propensity Score ◽  
Selection Process ◽  
Model Misspecification ◽  
Average Treatment Effect ◽  
Treatment Selection ◽  
Survival Outcomes ◽  
Inverse Probability ◽  
Propensity Score Model ◽  
Propensity Score Methods ◽  
Score Model

There is increasing interest in estimating the causal effects of treatments using observational data. Propensity-score matching methods are frequently used to adjust for differences in observed characteristics between treated and control individuals in observational studies. Survival or time-to-event outcomes occur frequently in the medical literature, but the use of propensity score methods in survival analysis has not been thoroughly investigated. This paper compares two approaches for estimating the Average Treatment Effect (ATE) on survival outcomes: Inverse Probability of Treatment Weighting (IPTW) and full matching. The performance of these methods was compared in an extensive set of simulations that varied the extent of confounding and the amount of misspecification of the propensity score model. We found that both IPTW and full matching resulted in estimation of marginal hazard ratios with negligible bias when the ATE was the target estimand and the treatment-selection process was weak to moderate. However, when the treatment-selection process was strong, both methods resulted in biased estimation of the true marginal hazard ratio, even when the propensity score model was correctly specified. When the propensity score model was correctly specified, bias tended to be lower for full matching than for IPTW. The reasons for these biases and for the differences between the two methods appeared to be due to some extreme weights generated for each method. Both methods tended to produce more extreme weights as the magnitude of the effects of covariates on treatment selection increased. Furthermore, more extreme weights were observed for IPTW than for full matching. However, the poorer performance of both methods in the presence of a strong treatment-selection process was mitigated by the use of IPTW with restriction and full matching with a caliper restriction when the propensity score model was correctly specified.

scholarly journals Subgroup balancing propensity score

2019 ◽  
Vol 29 (3) ◽  
pp. 659-676 ◽  
Author(s):  
Jing Dong ◽  
Junni L Zhang ◽  
Shuxi Zeng ◽  
Fan Li
Keyword(s):  
Propensity Score ◽  
Treatment Effects ◽  
Propensity Scores ◽  
Search Algorithm ◽  
Propensity Score Method ◽  
Moment Conditions ◽  
Propensity Score Model ◽  
Propensity Score Methods ◽  
Covariate Balance ◽  
Score Model

This paper concerns estimation of subgroup treatment effects with observational data. Existing propensity score methods are mostly developed for estimating overall treatment effect. Although the true propensity scores balance covariates in any subpopulations, the estimated propensity scores may result in severe imbalance in subgroup samples. Indeed, subgroup analysis amplifies a bias-variance tradeoff, whereby increasing complexity of the propensity score model may help to achieve covariate balance within subgroups, but it also increases variance. We propose a new method, the subgroup balancing propensity score, to ensure good subgroup balance as well as to control the variance inflation. For each subgroup, the subgroup balancing propensity score chooses to use either the overall sample or the subgroup (sub)sample to estimate the propensity scores for the units within that subgroup, in order to optimize a criterion accounting for a set of covariate-balancing moment conditions for both the overall sample and the subgroup samples. We develop two versions of subgroup balancing propensity score corresponding to matching and weighting, respectively. We devise a stochastic search algorithm to estimate the subgroup balancing propensity score when the number of subgroups is large. We demonstrate through simulations that the subgroup balancing propensity score improves the performance of propensity score methods in estimating subgroup treatment effects. We apply the subgroup balancing propensity score method to the Italy Survey of Household Income and Wealth (SHIW) to estimate the causal effects of having debit card on household consumption for different income groups.

A doubly robust approach for cost–effectiveness estimation from observational data

2017 ◽  
Vol 27 (10) ◽  
pp. 3126-3138 ◽  
Author(s):  
Jiaqi Li ◽  
Anil Vachani ◽  
Andrew Epstein ◽  
Nandita Mitra
Keyword(s):  
Cost Effectiveness ◽  
Propensity Score ◽  
Propensity Scores ◽  
Cancer Surveillance ◽  
Robust Approach ◽  
Propensity Score Model ◽  
Ensemble Machine Learning ◽  
Score Model ◽  
Doubly Robust ◽  
Time Varying Covariates

Estimation of common cost–effectiveness measures, including the incremental cost–effectiveness ratio and the net monetary benefit, is complicated by the need to account for informative censoring and inherent skewness of the data. In addition, since the two components of these measures, medical costs and survival are often collected from observational claims data, one must account for potential confounders. We propose a novel doubly robust, unbiased estimator for cost–effectiveness based on propensity scores that allow the incorporation of cost history and time-varying covariates. Further, we use an ensemble machine learning approach to obtain improved predictions from parametric and non-parametric cost and propensity score models. Our simulation studies demonstrate that the proposed doubly robust approach performs well even under mis-specification of either the propensity score model or the outcome model. We apply our approach to a cost–effectiveness analysis of two competing lung cancer surveillance procedures, CT vs. chest X-ray, using SEER-Medicare data.

scholarly journals Evaluating the performance of propensity score matching based approaches in individual patient data meta-analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Fatema Tuj Johara ◽  
Andrea Benedetti ◽  
Robert Platt ◽  
Dick Menzies ◽  
Piret Viiklepp ◽  
...  
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Propensity Scores ◽  
Individual Patient Data ◽  
Meta Analysis ◽  
Random Effect ◽  
Multidrug Resistant ◽  
Patient Data ◽  
Propensity Score Model ◽  
Score Model

Abstract Background Individual-patient data meta-analysis (IPD-MA) is an increasingly popular approach because of its analytical benefits. IPD-MA of observational studies must overcome the problem of confounding, otherwise biased estimates of treatment effect may be obtained. One approach to reducing confounding bias could be the use of propensity score matching (PSM). IPD-MA can be considered as two-stage clustered data (patients within studies) and propensity score matching can be implemented within studies, across studies, and combining both. Methods This article focuses on implementation of four PSM-based approaches for the analysis of data structure that exploit IPD-MA in two ways: (i) estimation of propensity score model using single-level or random-effects logistic regression; and (ii) matching of propensity scores (PS) across studies, within studies or preferential-within studies. We investigated the performance of these approaches through a simulation study, which considers an IPD-MA that examined the success of different treatments for multidrug-resistant tuberculosis (MDR-TB). The simulation parameters were varied according to three treatment prevalences (according to studies, 50% and 30%), three levels of heterogeneity between studies (low, moderate and high) and three levels of pooled odds ratio (1, 1.5, 3). Results All approaches showed greater biases at the higher levels of heterogeneity regardless of the choices of treatment prevalences. However, matching of propensity scores using within-study and preferential-within study reported better performance compared to matching across studies when treatment prevalence varied across-studies. For fixed prevalences, a random-effect propensity score model to estimate propensity scores followed by matching of propensity scores across-studies achieved lower biases compared to other PSM-based approaches. Conclusions Propensity score matching has wide application in health research while only limited literature is available on the implementation of PSM methods in IPD-MA, and until now methodological performance of PSM methods have not been examined. We believe, this work offers an intuition to the applied researcher for the choice of the PSM-based approaches.

Should a propensity score model be super? The utility of ensemble procedures for causal adjustment

2018 ◽  
Vol 38 (9) ◽  
pp. 1690-1702 ◽  
Author(s):  
Shomoita Alam ◽  
Erica E. M. Moodie ◽  
David A. Stephens
Keyword(s):  
Propensity Score ◽  
Propensity Score Model ◽  
Score Model
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