scholarly journals Augmented Inverse Probability Weighting and the Double Robustness Property

2021 ◽  
pp. 0272989X2110271
Author(s):  
Christoph F. Kurz
Keyword(s):  
Estimation Accuracy ◽  
Probability Weighting ◽  
Robust Method ◽  
Robustness Property ◽  
Inverse Probability ◽  
Double Robustness ◽  
Average Treatment Effects ◽  
Inverse Probability Weighted ◽  
Doubly Robust ◽  
Augmented Inverse Probability Weighting

This article discusses the augmented inverse propensity weighted (AIPW) estimator as an estimator for average treatment effects. The AIPW combines both the properties of the regression-based estimator and the inverse probability weighted (IPW) estimator and is therefore a “doubly robust” method in that it requires only either the propensity or outcome model to be correctly specified but not both. Even though this estimator has been known for years, it is rarely used in practice. After explaining the estimator and proving the double robustness property, I conduct a simulation study to compare the AIPW efficiency with IPW and regression under different scenarios of misspecification. In 2 real-world examples, I provide a step-by-step guide on implementing the AIPW estimator in practice. I show that it is an easily usable method that extends the IPW to reduce variability and improve estimation accuracy. [Box: see text]

scholarly journals A GENERAL DOUBLE ROBUSTNESS RESULT FOR ESTIMATING AVERAGE TREATMENT EFFECTS

2017 ◽  
Vol 34 (1) ◽  
pp. 112-133 ◽  
Author(s):  
Tymon Słoczyński ◽  
Jeffrey M. Wooldridge
Keyword(s):  
Instrumental Variable ◽  
Exponential Family ◽  
Treatment Effects ◽  
Robust Estimators ◽  
Conditional Mean ◽  
Double Robustness ◽  
Average Treatment ◽  
Average Treatment Effects ◽  
Inverse Probability Weighted ◽  
Doubly Robust

In this paper we study doubly robust estimators of various average and quantile treatment effects under unconfoundedness; we also consider an application to a setting with an instrumental variable. We unify and extend much of the recent literature by providing a very general identification result which covers binary and multi-valued treatments; unnormalized and normalized weighting; and both inverse-probability weighted (IPW) and doubly robust estimators. We also allow for subpopulation-specific average treatment effects where subpopulations can be based on covariate values in an arbitrary way. Similar to Wooldridge (2007), we then discuss estimation of the conditional mean using quasi-log likelihoods (QLL) from the linear exponential family.

scholarly journals Analysis of Incomplete Data Using Inverse Probability Weighting and Doubly Robust Estimators

Methodology ◽  
2010 ◽  
Vol 6 (1) ◽  
pp. 37-48 ◽  
Author(s):  
Stijn Vansteelandt ◽  
James Carpenter ◽  
Michael G. Kenward
Keyword(s):  
Incomplete Data ◽  
Missing At Random ◽  
Inverse Probability Weighting ◽  
Estimation Methods ◽  
Data Sets ◽  
Probability Weighting ◽  
Inverse Probability ◽  
Doubly Robust Estimation ◽  
Doubly Robust ◽  
Standard Software

This article reviews inverse probability weighting methods and doubly robust estimation methods for the analysis of incomplete data sets. We first consider methods for estimating a population mean when the outcome is missing at random, in the sense that measured covariates can explain whether or not the outcome is observed. We then sketch the rationale of these methods and elaborate on their usefulness in the presence of influential inverse weights. We finally outline how to apply these methods in a variety of settings, such as for fitting regression models with incomplete outcomes or covariates, emphasizing the use of standard software programs.

scholarly journals G-computation and doubly robust standardisation for continuous-time data: A comparison with inverse probability weighting

2021 ◽  
pp. 096228022110473
Author(s):  
Arthur Chatton ◽  
Florent Le Borgne ◽  
Clémence Leyrat ◽  
Yohann Foucher
Keyword(s):  
Continuous Time ◽  
Inverse Probability Weighting ◽  
Model Specification ◽  
Practical Implementation ◽  
Probability Weighting ◽  
Survival Times ◽  
Time Data ◽  
Inverse Probability ◽  
Real World Datasets ◽  
Doubly Robust

In time-to-event settings, g-computation and doubly robust estimators are based on discrete-time data. However, many biological processes are evolving continuously over time. In this paper, we extend the g-computation and the doubly robust standardisation procedures to a continuous-time context. We compare their performance to the well-known inverse-probability-weighting estimator for the estimation of the hazard ratio and restricted mean survival times difference, using a simulation study. Under a correct model specification, all methods are unbiased, but g-computation and the doubly robust standardisation are more efficient than inverse-probability-weighting. We also analyse two real-world datasets to illustrate the practical implementation of these approaches. We have updated the R package RISCA to facilitate the use of these methods and their dissemination.

scholarly journals Improved Doubly Robust Estimation in Marginal Mean Models for Dynamic Regimes

2020 ◽  
Vol 8 (1) ◽  
pp. 300-314
Author(s):  
Hao Sun ◽  
Ashkan Ertefaie ◽  
Xin Lu ◽  
Brent A. Johnson
Keyword(s):  
Estimating Function ◽  
Treatment Sequence ◽  
Treatment Assignment ◽  
Inverse Probability ◽  
Treatment Regimes ◽  
Dynamic Treatment Regimes ◽  
Dynamic Treatment ◽  
Doubly Robust Estimation ◽  
Inverse Probability Weighted ◽  
Doubly Robust

Abstract Doubly robust (DR) estimators are an important class of statistics derived from a theory of semiparametric efficiency. They have become a popular tool in causal inference, including applications to dynamic treatment regimes. The doubly robust estimators for the mean response to a dynamic treatment regime may be conceived through the augmented inverse probability weighted (AIPW) estimating function, defined as the sum of the inverse probability weighted (IPW) estimating function and an augmentation term. The IPW estimating function of the causal estimand via marginal structural model is defined as the complete-case score function for those subjects whose treatment sequence is consistent with the dynamic regime in question divided by the probability of observing the treatment sequence given the subject's treatment and covariate histories. The augmentation term is derived by projecting the IPW estimating function onto the nuisance tangent space and has mean-zero under the truth. The IPW estimator of the causal estimand is consistent if (i) the treatment assignment mechanism is correctly modeled and the AIPW estimator is consistent if either (i) is true or (ii) nested functions of intermediate and final outcomes are correctly modeled. Hence, the AIPW estimator is doubly robust and, moreover, the AIPW is semiparametric efficient if both (i) and (ii) are true simultaneously. Unfortunately, DR estimators can be inferior when either (i) or (ii) is true and the other false. In this case, the misspecified parts of the model can have a detrimental effect on the variance of the DR estimator. We propose an improved DR estimator of causal estimand in dynamic treatment regimes through a technique originally developed by [4] which aims to mitigate the ill-effects of model misspecification through a constrained optimization. In addition to solving a doubly robust system of equations, the improved DR estimator simultaneously minimizes the asymptotic variance of the estimator under a correctly specified treatment assignment mechanism but misspecification of intermediate and final outcome models. We illustrate the desirable operating characteristics of the estimator through Monte Carlo studies and apply the methods to data from a randomized study of integrilin therapy for patients undergoing coronary stent implantation. The methods proposed here are new and may be used to further improve personalized medicine, in general.

Abstract P198: Racial Disparities in Cardiovascular Outcomes in REGARDS Persist After Using Inverse Probability Weighting to Account for Missing Data

Circulation ◽  
2017 ◽  
Vol 135 (suppl_1) ◽  
Author(s):  
D. Leann Long ◽  
George Howard ◽  
Suzanne Judd ◽  
Jennifer Manly ◽  
Leslie McClure ◽  
...  
Keyword(s):  
Left Ventricular Hypertrophy ◽  
Selection Bias ◽  
Ventricular Hypertrophy ◽  
Inverse Probability Weighting ◽  
Left Ventricular ◽  
Probability Weighting ◽  
Complete Case ◽  
Inverse Probability ◽  
Weighted Analysis ◽  
Inverse Probability Weighted

Introduction: When data are missing, analysts often choose to perform ‘complete case’ analysis, restricting statistical analysis to those participants with all necessary fields completed. However, this analysis is subject to selection bias if those participants excluded are somehow inherently different from those included. One approach to address the potential selection bias is inverse probability weighting, where participants with complete data are weighted to reflect the original population. We compare estimated racial disparities in hypertension and left ventricular hypertrophy using complete cases to those estimates from inverse probability weighted analysis. Methods: The REasons for Geographic and Racial Differences in Stroke (REGARDS) study enrolled 30,183 participants aged 45+ between 2003 and 2007 to study racial and geographic differences in stroke and cardiovascular health. When the second in-home visits were conducted, 18% (5542) of participants had died and 23% (7071) of participants had withdrawn. For the baseline population, the probabilities of being a complete case are estimated through logistic regression models using baseline characteristics. These predicted probabilities are inverted to create the weights used in statistical analysis, such that the complete participants are weighted to represent the enrolled population. Through logistic regression, we estimate the association between race and hypertension and left ventricular hypertrophy at the second in-home visit for complete data and compare these results to the results from inverse probability weighted analysis. All models are adjusted for sex, age and region. Results: The logistic models for dropout and death have low and moderate predictive abilities (c-statistics 0.602 and 0.811, respectively). For incident hypertension, the estimated odds ratio comparing blacks to whites differs little between the complete case (1.87 (1.66, 2.10)) and the weighted (1.83 (1.61, 2.09)) analysis. For left ventricular hypertrophy, the estimated odds ratio comparing blacks to whites changes little from the complete case analysis (1.54 (1.32, 1.79)) to the weighted analysis (1.45 (1.21, 1.74)). Discussion: Estimated racial inequalities in the odds of incident hypertension and left ventricular hypertrophy were similar in the complete case and inverse probability weighting analyses, indicating little evidence of selection bias in the estimation of racial inequalities for these outcomes.

Combining Multiple Imputation and Inverse-Probability Weighting for Analyzing Response with Missing in the Presence of Covariates

2020 ◽  
Author(s):  
Freshteh Osmani ◽  
Ebrahim Hajizadeh
Keyword(s):  
Missing Data ◽  
Missing Values ◽  
Clinical Trial Data ◽  
Real Data ◽  
Inverse Probability Weighting ◽  
Trial Data ◽  
Data Sets ◽  
Probability Weighting ◽  
Inverse Probability ◽  
Doubly Robust

Introduction: Missing values are frequently seen in data sets of research studiesespecially in medical studies.Therefore, it is essential that the data, especially in medical research should evaluate in terms of the structure of missingness.This study aims to provide new statistical methods for analyzing such data. Methods:Multiple imputation (MI) and inverse-probability weighting (IPW)aretwo common methods whichused to deal with missing data. MI method is more effectiveand complexthan IPW.MI requires a model for the joint distribution of the missing data given the observed data.While IPW need only a model for the probability that a subject has fulldata .Inefficacy in each of these models may causeto serious bias if missingness in dataset is large .Anothermethod that combines these approaches to give a doubly robust estimator.In addition, using of these methodswill demonstrate in the clinical trial data related to postpartum bleeding. Results:In this article, we examine the performance of IPW/MI relative to MI and IPW alone in terms of bias and efficiency.According to the results of simulation can be said that that IPW/MI have advantages over alternatives.Also results of real data showed that,results of MI/MI doesnot differ with the results ofIPW/MIsignificantly. Conclusion:Problem of missing data are in many studies that causes bias and decreasing efficacy inmodel.In this study, after comparing the results of these techniques,it was concludedthat IPW/MI method has better performance than other methods.

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