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.

scholarly journals Propensity score matching and complex surveys

2016 ◽  
Vol 27 (4) ◽  
pp. 1240-1257 ◽  
Author(s):  
Peter C Austin ◽  
Nathaniel Jembere ◽  
Maria Chiu
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Canadian Community Health Survey ◽  
Population Based ◽  
Sample Surveys ◽  
Complex Surveys ◽  
Propensity Score Model ◽  
Control Subjects ◽  
Score Model ◽  
Using Data

Researchers are increasingly using complex population-based sample surveys to estimate the effects of treatments, exposures and interventions. In such analyses, statistical methods are essential to minimize the effect of confounding due to measured covariates, as treated subjects frequently differ from control subjects. Methods based on the propensity score are increasingly popular. Minimal research has been conducted on how to implement propensity score matching when using data from complex sample surveys. We used Monte Carlo simulations to examine two critical issues when implementing propensity score matching with such data. First, we examined how the propensity score model should be formulated. We considered three different formulations depending on whether or not a weighted regression model was used to estimate the propensity score and whether or not the survey weights were included in the propensity score model as an additional covariate. Second, we examined whether matched control subjects should retain their natural survey weight or whether they should inherit the survey weight of the treated subject to which they were matched. Our results were inconclusive with respect to which method of estimating the propensity score model was preferable. In general, greater balance in measured baseline covariates and decreased bias was observed when natural retained weights were used compared to when inherited weights were used. We also demonstrated that bootstrap-based methods performed well for estimating the variance of treatment effects when outcomes are binary. We illustrated the application of our methods by using the Canadian Community Health Survey to estimate the effect of educational attainment on lifetime prevalence of mood or anxiety disorders.

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 De-commoditizing Ethiopian coffees after the establishment of the Ethiopian Commodity Exchange: an empirical investigation of smallholder coffee producers in Ethiopia

2019 ◽  
Vol 22 (4) ◽  
pp. 499-518
Author(s):  
Tinsae Demise Handino ◽  
Marijke D’Haese ◽  
Freaw Demise ◽  
Misginaw Tamirat
Keyword(s):  
Value Chain ◽  
Household Income ◽  
Small Scale ◽  
Sustainable Livelihood ◽  
Commodity Exchange ◽  
Propensity Score Model ◽  
Southwestern Ethiopia ◽  
Score Model ◽  
Sustainable Livelihood Approach ◽  
The Impact

The repercussions of reforming an agricultural market are mainly observed at the most vulnerable segment of the value chain, namely, the producers. In the current commodity market created with trade through the Ethiopian Commodity Exchange (ECX), coffee is less traceable to its producers. Only cooperatives that sell certified coffee through the unions they belong to, are allowed to bypass the more commodified ECX market. This study aims to investigate if small-scale coffee producers in southwestern Ethiopia that sell coffee through the certified cooperative are better off. It is assumed that the coffee sales through, and membership of, a cooperative, allows farmers to improve their coffee production as well as to improve other aspects of their livelihood. A sustainable livelihood approach was used as the inspiration for the welfare indicators that needed to be considered, data collected amongst members and non-members of certified cooperatives, and a propensity score model to investigate the impact of cooperative membership on the livelihood indicators. Results suggest that members of certified cooperatives indeed receive, on average, better prices. Yet, no evidence was found that indicates that the higher price is translated into better household income. Furthermore, coffee plantation productivity of those members who were interviewed was lower than that of the non-members. This finding could explain the failure to find an overall effect. Since the majority of the producers’ income emanate from coffee, a sustainable way of enhancing the productivity of the coffee could revitalize the welfare of the coffee producers.

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.

Abstract 154: Estimating Treatment Effects in More Than Two Treatment Groups via Propensity Score Weighting: Practical Guidance and Application from Anticoagulant Therapy in Atrial Fibrillation Study

2016 ◽  
Vol 9 (suppl_2) ◽  
Author(s):  
Gboyega Adeboyeje ◽  
Gosia Sylwestrzak ◽  
John Barron
Keyword(s):  
Atrial Fibrillation ◽  
Propensity Score ◽  
Anticoagulant Therapy ◽  
Observational Studies ◽  
Treatment Effects ◽  
Propensity Scores ◽  
Multinomial Logistic Regression ◽  
Baseline Risk ◽  
Risk Scores ◽  
Treatment Groups

Background: The methods for estimating and assessing propensity scores in the analysis of treatment effects between two treatment arms in observational studies have been well described in the outcomes research methodology literature. However, in practice, the decision makers may need information on the comparative effectiveness of more than two treatment strategies. There’s little guidance on the estimation of treatment effects using inverse probability of treatment weights (IPTW) in studies where more than two treatment arms are to be compared. Methods: Data from an observational cohort study on anticoagulant therapy in atrial fibrillation is used to illustrate the practical steps involved in estimating the IPTW from multiple propensity scores and assessing the balance achieved under certain assumptions. For all patients in the study, we estimated the propensity score for the treatment each patient received using a multinomial logistic regression. We used the inverse of the propensity scores as weights in Cox proportional hazards to compare study outcomes for each treatment group Results: Before IPTW adjustment, there were large and statistically significant baseline differences between treatment groups in terms of demographic, plan type, and clinical characteristics including validated stroke and bleeding risk scores. After IPTW, there were no significant differences in all measured baseline risk factors. In unadjusted estimates of stroke outcome, there were large differences between dabigatran [Hazard ratio, HR, 0.59 (95% CI: 0.53 - 0.66)], apixaban [HR, 0.69 (CI: 0.57, 0.83)], rivaroxaban [HR, 0.60 (CI: 0.53 0.68)] and warfarin users. After IPTW, estimated stroke risk differences were significantly reduced or eliminated between dabigatran [HR, 0.89 (CI: 0.80, 0.98)], apixaban [HR, 0.92 (0.76, 1.10)], rivaroxaban [HR, 0.84 (CI: 0.75, 0.95)] and warfarin users. Conclusions: Our results showed IPTW methods, correctly employed under certain assumptions, are practical and relatively simple tools to control for selection bias and other baseline differences in observational studies evaluating the comparative treatment effects of more than two treatment arms. When preserving sample size is important and in the presence of time-varying confounders, IPTW methods have distinct advantages over propensity matching or adjustment.

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.

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