scholarly journals Probabilistic Analysis of Balancing Scores for Causal Inference

2015 ◽  
Vol 7 (2) ◽  
pp. 90
Author(s):  
Priyantha Wijayatunga
Keyword(s):  
Propensity Score ◽  
Propensity Scores ◽  
Causal Effect ◽  
Prognostic Score ◽  
Causal Modeling ◽  
Modeling Framework ◽  
Confounding Bias ◽  
Outcome Score ◽  
Basic Behavior ◽  
And Control

Propensity scores are often used for stratification of treatment and control groups of subjects in observational data to remove confounding bias when estimating of  causal effect of the treatment on an outcome in so-called potential outcome causal modeling framework. In this article, we try to get some insights into basic behavior of  the propensity scores in a probabilistic sense. We do a simple analysis of their usage confining to the case of discrete confounding covariates and outcomes. While making clear about behavior of the propensity score our analysis shows how the so-called prognostic score can be derived simultaneously. However the prognostic score is derived in a limited sense in the current literature whereas our derivation is more general and shows all possibilities of having the score. And we call it outcome score. We argue that application of both the propensity score and the outcome score is the most efficient way for  reduction of dimension in the confounding covariates as opposed to current belief that the propensity score alone is the most efficient way.

scholarly journals Comparison of Methods for Handling Covariate Missingness in Propensity Score Estimation with a Binary Exposure

2019 ◽  
Author(s):  
Donna Coffman ◽  
Jiangxiu Zhou ◽  
Xizhen Cai
Keyword(s):  
Propensity Score ◽  
Multiple Imputation ◽  
Missing Values ◽  
Propensity Scores ◽  
Causal Effect ◽  
Nonparametric Approach ◽  
Split Method ◽  
Mean Imputation ◽  
Substantial Bias ◽  
Effect Estimation

Abstract Background Causal effect estimation with observational data is subject to bias due to confounding, which is often controlled for using propensity scores. One unresolved issue in propensity score estimation is how to handle missing values in covariates.Method Several approaches have been proposed for handling covariate missingness, including multiple imputation (MI), multiple imputation with missingness pattern (MIMP), and treatment mean imputation. However, there are other potentially useful approaches that have not been evaluated, including single imputation (SI) + prediction error (PE), SI+PE + parameter uncertainty (PU), and Generalized Boosted Modeling (GBM), which is a nonparametric approach for estimating propensity scores in which missing values are automatically handled in the estimation using a surrogate split method. To evaluate the performance of these approaches, a simulation study was conducted.Results Results suggested that SI+PE, SI+PE+PU, MI, and MIMP perform almost equally well and better than treatment mean imputation and GBM in terms of bias; however, MI and MIMP account for the additional uncertainty of imputing the missingness.Conclusions Applying GBM to the incomplete data and relying on the surrogate split approach resulted in substantial bias. Imputation prior to implementing GBM is recommended.

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.

Propensity score specification for optimal estimation of average treatment effect with binary response

2020 ◽  
Vol 29 (12) ◽  
pp. 3623-3640
Author(s):  
John A Craycroft ◽  
Jiapeng Huang ◽  
Maiying Kong
Keyword(s):  
Propensity Score ◽  
Treatment Effect ◽  
Propensity Scores ◽  
Optimal Estimation ◽  
Theoretical Explanation ◽  
Average Treatment Effect ◽  
Adaptive Lasso ◽  
Confounding Bias ◽  
Average Treatment ◽  
The Impact

Propensity score methods are commonly used in statistical analyses of observational data to reduce the impact of confounding bias in estimations of average treatment effect. While the propensity score is defined as the conditional probability of a subject being in the treatment group given that subject’s covariates, the most precise estimation of average treatment effect results from specifying the propensity score as a function of true confounders and predictors only. This property has been demonstrated via simulation in multiple prior research articles. However, we have seen no theoretical explanation as to why this should be so. This paper provides that theoretical proof. Furthermore, this paper presents a method for performing the necessary variable selection by means of elastic net regression, and then estimating the propensity scores so as to obtain optimal estimates of average treatment effect. The proposed method is compared against two other recently introduced methods, outcome-adaptive lasso and covariate balancing propensity score. Extensive simulation analyses are employed to determine the circumstances under which each method appears most effective. We applied the proposed methods to examine the effect of pre-cardiac surgery coagulation indicator on mortality based on a linked dataset from a retrospective review of 1390 patient medical records at Jewish Hospital (Louisville, KY) with the Society of Thoracic Surgeons database.

Propensity scores: From naïve enthusiasm to intuitive understanding

2011 ◽  
Vol 21 (3) ◽  
pp. 273-293 ◽  
Author(s):  
Elizabeth Williamson ◽  
Ruth Morley ◽  
Alan Lucas ◽  
James Carpenter
Keyword(s):  
Propensity Score ◽  
Regression Models ◽  
Propensity Scores ◽  
Causal Effect ◽  
Future Research ◽  
Propensity Score Methods ◽  
Advantages And Disadvantages ◽  
Alternative Approach ◽  
Using Data ◽  
Unmeasured Confounders

Estimation of the effect of a binary exposure on an outcome in the presence of confounding is often carried out via outcome regression modelling. An alternative approach is to use propensity score methodology. The propensity score is the conditional probability of receiving the exposure given the observed covariates and can be used, under the assumption of no unmeasured confounders, to estimate the causal effect of the exposure. In this article, we provide a non-technical and intuitive discussion of propensity score methodology, motivating the use of the propensity score approach by analogy with randomised studies, and describe the four main ways in which this methodology can be implemented. We carefully describe the population parameters being estimated — an issue that is frequently overlooked in the medical literature. We illustrate these four methods using data from a study investigating the association between maternal choice to provide breast milk and the infant's subsequent neurodevelopment. We outline useful extensions of propensity score methodology and discuss directions for future research. Propensity score methods remain controversial and there is no consensus as to when, if ever, they should be used in place of traditional outcome regression models. We therefore end with a discussion of the relative advantages and disadvantages of each.

A model averaging approach for estimating propensity scores by optimizing balance

2017 ◽  
Vol 28 (1) ◽  
pp. 84-101 ◽  
Author(s):  
Yuying Xie ◽  
Yeying Zhu ◽  
Cecilia A Cotton ◽  
Pan Wu
Keyword(s):  
Propensity Score ◽  
Exclusive Breastfeeding ◽  
Propensity Scores ◽  
Causal Effect ◽  
Model Averaging ◽  
Real Data ◽  
Parametric Modeling ◽  
Machine Learning Techniques ◽  
Mixed Feeding ◽  
Data Set

Many approaches, including traditional parametric modeling and machine learning techniques, have been proposed to estimate propensity scores. This paper describes a new model averaging approach to propensity score estimation in which parametric and nonparametric estimates are combined to achieve covariate balance. Simulation studies are conducted across different scenarios varying in the degree of interactions and nonlinearities in the treatment model. The results show that, based on inverse probability weighting, the proposed propensity score estimator produces less bias and smaller standard errors than existing approaches. They also show that a model averaging approach with the objective of minimizing the average Kolmogorov–Smirnov statistic leads to the best performing IPW estimator. The proposed approach is also applied to a real data set in evaluating the causal effect of formula or mixed feeding versus exclusive breastfeeding on a child’s body mass index Z-score at age 4. The data analysis shows that formula or mixed feeding is more likely to lead to obesity at age 4, compared to exclusive breastfeeding.

scholarly journals Using Propensity Score Matching Technique to Estimate Utilization and Costs of General Practitioners’ Services associated with Alzheimer’s Disease

10.36469/9827 ◽  
2016 ◽  
Vol 4 (1) ◽  
pp. 67-79
Author(s):  
Rajan Sharma ◽  
Elizaveta Sopina ◽  
Jan Sørensen
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
General Practitioners ◽  
Service Use ◽  
Propensity Scores ◽  
Service Utilisation ◽  
Gp Services ◽  
And Control

Objective: General practitioners (GPs) play an important role in caring for people with Alzheimer’s disease (AD). However, the cost and the extent of service utilization from GPs due to AD patients are difficult to assess. This study aimed to explore the principles of propensity score matching (PSM) technique to assess the additional GP service use and cost imposed by AD in persons aged ≥60 years in Denmark. Design: PSM was used to estimate the additional use and cost of GP services attributable to AD. Case and control baseline characteristics were compared with and without the application of PSM. Propensity scores were then estimated using the generalized boosted model, a multivariate, nonparametric and automated algorithm technique. Setting: Observational data from Statistics Denmark registry. Subjects: 3368 cases and 3368 controls; cases with AD were defined as patients with diagnoses G30 and F00 and/or those with primary care prescriptions for anti-AD drugs from the years 2004 until 2009. Main Outcome Measures: GP service utilisation and costs attributable to AD. Results: PSM brought a large improvement to the balance of observed covariates among the cases and control groups. AD patients received around 20% more GP services and utilized services that cost 15% more than non-AD controls during a calendar year. Conclusion: AD patients utilize more GP services and incur higher costs as compared to their matched controls. The PSM technique can be an effective tool to reduce imbalance of observable confounders from register based data and improve the estimations.

scholarly journals Does knee replacement surgery for osteoarthritis improve survival? The jury is still out

2016 ◽  
Vol 76 (1) ◽  
pp. 140-146 ◽  
Author(s):  
Devyani Misra ◽  
Na Lu ◽  
David Felson ◽  
Hyon K Choi ◽  
John Seeger ◽  
...  
Keyword(s):  
Propensity Score ◽  
Protective Effect ◽  
Knee Replacement ◽  
Propensity Scores ◽  
Age Groups ◽  
Health Improvement ◽  
Confounding Bias ◽  
Knee Oa ◽  
Risk Of Mortality ◽  
All Cause Mortality

BackgroundThe relation of knee replacement (KR) surgery to all-cause mortality has not been well established owing to potential biases in previous studies. Thus, we aimed to examine the relation of KR to mortality risk among patients with knee osteoarthritis (OA) focusing on identifying biases that may threaten the validity of prior studies.MethodsWe included knee OA subjects (ages 50–89 years) from The Health Improvement Network, an electronic medical records database in the UK. Risk of mortality among KR subjects was compared with propensity score-matched non-KR subjects. To explore residual confounding bias, subgroup analyses stratified by age and propensity scores were performed.ResultsSubjects with KR had 28% lower risk of mortality than non-KR subjects (HR 0.72, 95% CI 0.66 to 0.78). However, when stratified by age, protective effect was noted only in older age groups (>63 years) but not in younger subjects (≤63 years). Further, the mortality rate among KR subjects decreased as candidacy (propensity score) for KR increased among subjects with KR, but no such consistent trend was noted among non-KR subjects.ConclusionsWhile a protective effect of KR on mortality cannot be ruled out, findings of lower mortality among older KR subjects and those with higher propensity scores suggest that prognosis-based selection for KR may lead to intractable confounding by indication; hence, the protective effect of KR on all-cause mortality may be overestimated.

Comparing different propensity score estimation methods for estimating the marginal causal effect through standardization to propensity scores

2018 ◽  
Vol 47 (4) ◽  
pp. 964-976 ◽  
Author(s):  
Safoora Gharibzadeh ◽  
Mohammad Ali Mansournia ◽  
Abbas Rahimiforoushani ◽  
Ahad Alizadeh ◽  
Atieh Amouzegar ◽  
...  
Keyword(s):  
Propensity Score ◽  
Propensity Scores ◽  
Causal Effect ◽  
Estimation Methods

scholarly journals Parental Incarceration and Child Well-being: Conceptual and Practical Concerns Regarding the Use of Propensity Scores

2018 ◽  
Vol 4 ◽  
pp. 237802311877930 ◽  
Author(s):  
Jennifer E. Copp ◽  
Peggy C. Giordano ◽  
Wendy D. Manning ◽  
Monica A. Longmore
Keyword(s):  
Propensity Score ◽  
Problem Behaviors ◽  
Propensity Scores ◽  
Propensity Score Analysis ◽  
Well Being ◽  
Propensity Score Methods ◽  
Score Analysis ◽  
Data Limitations ◽  
Using Data ◽  
And Control

The aim of the current investigation was to examine the appropriateness of propensity score methods for the study of incarceration effects on children by directing attention to a range of conceptual and practical concerns, including the exclusion of theoretically meaningful covariates, the comparability of treatment and control groups, and potential ambiguities resulting from researcher-driven analytic decisions. Using data from the Fragile Families and Child Wellbeing Study, we examined the effects of maternal and paternal incarceration on a range of child well-being outcomes, including internalizing and externalizing problem behaviors, Peabody Picture Vocabulary Test scores, and early juvenile delinquency. Our findings suggested that propensity scores and treatment effect estimates are highly sensitive to a number of decisions made by the researcher, including aspects where little consensus exists. In light of the conceptual underpinnings of propensity score analysis and existing data limitations, we suggest the potential utility of different identification methods and specialized data collection efforts.

scholarly journals Comparison of Methods for Handling Covariate Missingness in Propensity Score Estimation with a Binary Exposure

2020 ◽  
Author(s):  
Donna Coffman ◽  
Jiangxiu Zhou ◽  
Xizhen Cai
Keyword(s):  
Propensity Score ◽  
Multiple Imputation ◽  
Missing Values ◽  
Propensity Scores ◽  
Causal Effect ◽  
Nonparametric Approach ◽  
Split Method ◽  
Mean Imputation ◽  
Substantial Bias ◽  
Effect Estimation

Abstract Background: Causal effect estimation with observational data is subject to bias due to confounding, which is often controlled for using propensity scores. One unresolved issue in propensity score estimation is how to handle missing values in covariates.Method: Several approaches have been proposed for handling covariate missingness, including multiple imputation (MI), multiple imputation with missingness pattern (MIMP), and treatment mean imputation. However, there are other potentially useful approaches that have not been evaluated, including single imputation (SI) + prediction error (PE), SI+PE + parameter uncertainty (PU), and Generalized Boosted Modeling (GBM), which is a nonparametric approach for estimating propensity scores in which missing values are automatically handled in the estimation using a surrogate split method. To evaluate the performance of these approaches, a simulation study was conducted. Results: Results suggested that SI+PE, SI+PE+PU, MI, and MIMP perform almost equally well and better than treatment mean imputation and GBM in terms of bias; however, MI and MIMP account for the additional uncertainty of imputing the missingness. Conclusions: Applying GBM to the incomplete data and relying on the surrogate split approach resulted in substantial bias. Imputation prior to implementing GBM is recommended.

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