Goodness-of-fit diagnostics for the propensity score model when estimating treatment effects using covariate adjustment with the propensity score

2008 ◽  
Vol 17 (12) ◽  
pp. 1202-1217 ◽  
Author(s):  
Peter C. Austin
Keyword(s):  
Propensity Score ◽  
Goodness Of Fit ◽  
Treatment Effects ◽  
Covariate Adjustment ◽  
Propensity Score Model ◽  
Score Model

Estimation of average treatment effects based on parametric propensity score model

2010 ◽  
Vol 140 (3) ◽  
pp. 806-816 ◽  
Author(s):  
Lili Yao ◽  
Zhihua Sun ◽  
Qihua Wang
Keyword(s):  
Propensity Score ◽  
Treatment Effects ◽  
Propensity Score Model ◽  
Average Treatment ◽  
Average Treatment Effects ◽  
Score Model

scholarly journals Robust Causal Estimation from Observational Studies Using Penalized Spline of Propensity Score for Treatment Comparison

Stats ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 529-549
Author(s):  
Tingting Zhou ◽  
Michael R. Elliott ◽  
Roderick J. A. Little
Keyword(s):  
Propensity Score ◽  
Observational Studies ◽  
Treatment Effects ◽  
Single Measure ◽  
Propensity Score Model ◽  
Penalized Spline ◽  
Treatment Comparison ◽  
Control Subjects ◽  
Score Model ◽  
The Impact

Without randomization of treatments, valid inference of treatment effects from observational studies requires controlling for all confounders because the treated subjects generally differ systematically from the control subjects. Confounding control is commonly achieved using the propensity score, defined as the conditional probability of assignment to a treatment given the observed covariates. The propensity score collapses all the observed covariates into a single measure and serves as a balancing score such that the treated and control subjects with similar propensity scores can be directly compared. Common propensity score-based methods include regression adjustment and inverse probability of treatment weighting using the propensity score. We recently proposed a robust multiple imputation-based method, penalized spline of propensity for treatment comparisons (PENCOMP), that includes a penalized spline of the assignment propensity as a predictor. Under the Rubin causal model assumptions that there is no interference across units, that each unit has a non-zero probability of being assigned to either treatment group, and there are no unmeasured confounders, PENCOMP has a double robustness property for estimating treatment effects. In this study, we examine the impact of using variable selection techniques that restrict predictors in the propensity score model to true confounders of the treatment-outcome relationship on PENCOMP. We also propose a variant of PENCOMP and compare alternative approaches to standard error estimation for PENCOMP. Compared to the weighted estimators, PENCOMP is less affected by inclusion of non-confounding variables in the propensity score model. We illustrate the use of PENCOMP and competing methods in estimating the impact of antiretroviral treatments on CD4 counts in HIV+ patients.

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.

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

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.

A Care Bundle Intervention to Prevent Surgical Site Infections After a Craniotomy

2020 ◽  
Author(s):  
Emilio Jiménez-Martínez ◽  
Guillermo Cuervo ◽  
Jordi Carratalà ◽  
Ana Hornero ◽  
Pilar Ciercoles ◽  
...  
Keyword(s):  
Propensity Score ◽  
Cox Regression ◽  
Surgical Site Infections ◽  
Care Bundle ◽  
Primary Study ◽  
Preventive Strategy ◽  
Propensity Score Model ◽  
Vancomycin Powder ◽  
Score Model ◽  
Control Study

Abstract Background Although surgical site infections after a craniotomy (SSI-CRANs) are a serious problem that involves significant morbidity and costs, information on their prevention is scarce. We aimed to determine whether the implementation of a care bundle was effective in preventing SSI-CRANs. Methods A historical control study was used to evaluate the care bundle, which included a preoperative shower with 4% chlorhexidine soap, appropriate hair removal, adequate preoperative systemic antibiotic prophylaxis, the administration of 1 g of vancomycin powder into the subgaleal space before closing, and a postoperative dressing of the incisional surgical wound with a sterile absorbent cover. Patients were divided into 2 groups: preintervention (January 2013 to December 2015) and intervention (January 2016 to December 2017). The primary study end point was the incidence of SSI-CRANs within 1 year postsurgery. Propensity score matching was performed, and differences between the 2 study periods were assessed using Cox regression models. Results A total of 595 and 422 patients were included in the preintervention and intervention periods, respectively. The incidence of SSI-CRANs was lower in the intervention period (15.3% vs 3.5%; P < .001). Using a propensity score model, 421 pairs of patients were matched. The care bundle intervention was independently associated with a reduced incidence of SSI-CRANs (adjusted odds ratio, 0.23; 95% confidence interval, .13–.40; P < .001). Conclusions The care bundle intervention was effective in reducing SSI-CRAN rates. The implementation of this multimodal preventive strategy should be considered in centers with high SSI-CRAN incidences.

Inadvertent Splenectomy During Resection for Colorectal Cancer Does Not Increase Long-term Mortality in a Propensity Score Model

2016 ◽  
Vol 59 (12) ◽  
pp. 1150-1159 ◽  
Author(s):  
Ida Lolle ◽  
Hans-Christian Pommergaard ◽  
David F. Schefte ◽  
Orhan Bulut ◽  
Peter-Martin Krarup ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Propensity Score ◽  
Propensity Score Model ◽  
Score Model ◽  
Long Term Mortality

A new weighted balance measure helped to select the variables to be included in a propensity score model

2015 ◽  
Vol 68 (12) ◽  
pp. 1415-1422.e2 ◽  
Author(s):  
Emmanuel Caruana ◽  
Sylvie Chevret ◽  
Matthieu Resche-Rigon ◽  
Romain Pirracchio
Keyword(s):  
Propensity Score ◽  
Propensity Score Model ◽  
Score Model
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