Cutting Feedback in Bayesian Regression Adjustment for the Propensity Score

McCandless, Lawrence C.; Douglas, Ian; Evans, Stephen; Smeeth, Liam

doi:10.2202/1557-4679.1205

Cited by 54 publications

(54 citation statements)

References 13 publications

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“…Despite relatively infrequent use in orthopedics propensity scores have become popular especially in the area of the epidemiological analysis of treatment effects. 9 The generalized propensity score determines the probability of receiving a particular treatment based on a variety of variables. 5 Our hypothesis that surgeons can impact graft choice was confirmed.…”

Section: Discussionmentioning

confidence: 99%

Factors Influencing Graft Choice in Revision Anterior Cruciate Ligament Reconstruction in the MARS Group

Group

2015

J Knee Surg

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Background It has not been known what drives revision ACL reconstruction graft choice in the past. We undertook this study to utilize the MARS (Multicenter ACL Revision Study) cohort and propensity score statistical analysis to determine the drivers of revision ACL reconstruction graft choice. We hypothesized that propensity analysis would demonstrate that individual surgeons still have significant impact on revision ACL reconstruction. Methods Twelve hundred patients were enrolled in this longitudinal revision cohort by 83 surgeons at 52 sites. The median age was 26 years and 505 (42%) were females. 1049 (87%) were undergoing their first ACL revision. Graft choice for revision ACL reconstruction for these patients was 48% autograft, 49% allograft, and 3% combination. The independent variables of this model included gender, age, ethnicity, BMI, smoking status, sport, activity level, previous graft, revision number, surgeon, surgeon’s opinion of failure, previous technical aspects, etc. Surgeon was defined as those who contributed more than 15 patients during the enrollment period. We calculated a propensity score for graft type based on the predicted probability of receiving an allograft from a logistic regression model. Results Propensity scores demonstrated that surgeon, prior graft choice, and patient age each had significant influence on which graft type was chosen for the revision ACL reconstruction (p<0.0001). The revising surgeon had the largest impact upon graft choice: approximately 5 times that of the second most important factor (prior graft). If the prior graft type was an autograft, then an allograft was 3.6 times more likely to be chosen for the revision. Conclusions This current study demonstrates that the individual surgeon is ultimately the most important factor in revision ACL reconstruction graft choice. Additional statistically significant influences of graft choice included age, gender, previous graft choice, ACL revision number, concurrent MCL/PM repair, and opinion of the previous failure. This demonstrates that if graft choice is determined to impact outcome then surgeons have the ability to change and determine the graft utilized.

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Section: Discussionmentioning

confidence: 99%

Factors Influencing Graft Choice in Revision Anterior Cruciate Ligament Reconstruction in the MARS Group

Group

2015

J Knee Surg

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“…Various methods described as “quasi-Bayesian,” “approximately-Bayesian,” or “two-step Bayesian” have been recently proposed to combine estimation of the propensity score with that of causal effects without appealing to Bayes theorem to unify these two stages with a joint likelihood (Hoshino, 2008; McCandless et al, 2010; Kaplan and Chen, 2012; Zigler and Dominici, 2014). These methods have been described as “cutting the feedback” between the propensity score and outcome stages (Lunn et al, 2009), and represent a special case of so-called “modularization” in Bayesian inference (Liu et al, 2009).…”

Section: Bayesian Estimation Of Causal Effects With Propensity Scoresmentioning

confidence: 99%

“…While the usefulness of propensity scores for Bayesian inference was motivated decades ago (Rubin, 1985), the promise of modern Bayesian analysis tools has spawned renewed interest in Bayesian estimation of causal effects with propensity scores (Hoshino, 2008; McCandless et al, 2009; An, 2010; McCandless et al, 2010; Kaplan and Chen, 2012; Zigler et al, 2013; McCandless et al, 2012; Zigler and Dominici, 2014; Saarela et al, 2015). …”

Section: Introductionmentioning

confidence: 99%

“…More recently, Saarela et al (2015) proposed a Bayesian approach that uses propensity scores to estimate the parameters of a marginal structural model, which incited interesting discussions about whether the approach could be truly “Bayesian” (Robins et al, 2015). A variety of other methods have been proposed and described with terms such as “quasi-Bayesian” or “two-step Bayesian” to employ the Bayesian inferential paradigm in propensity score and causal effect estimation while maintaining the conceptual separation of design and analysis stages (McCandless et al, 2010; Kaplan and Chen, 2012; Zigler and Dominici, 2014). The purpose of this article is to clarify, against the backdrop of existing literature on “Bayesian propensity score analysis,” the fundamental issues at play when attempting to conduct Bayesian inference amid the two-stage nature of estimating causal effects with propensity scores.…”

Section: Introductionmentioning

confidence: 99%

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The Central Role of Bayes’ Theorem for Joint Estimation of Causal Effects and Propensity Scores

Zigler

2016

The American Statistician

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Although propensity scores have been central to the estimation of causal effects for over 30 years, only recently has the statistical literature begun to consider in detail methods for Bayesian estimation of propensity scores and causal effects. Underlying this recent body of literature on Bayesian propensity score estimation is an implicit discordance between the goal of the propensity score and the use of Bayes theorem. The propensity score condenses multivariate covariate information into a scalar to allow estimation of causal effects without specifying a model for how each covariate relates to the outcome. Avoiding specification of a detailed model for the outcome response surface is valuable for robust estimation of causal effects, but this strategy is at odds with the use of Bayes theorem, which presupposes a full probability model for the observed data that adheres to the likelihood principle. The goal of this paper is to explicate this fundamental feature of Bayesian estimation of causal effects with propensity scores in order to provide context for the existing literature and for future work on this important topic.

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“…In fact, Zigler et al (2013) assessed the joint estimation of the Bayesian propensity score and the treatment effect and found that the feedback between propensity score model and outcome model can lead to poor treatment effect estimates. This model feedback is especially problematic if the relationship between the outcome and the propensity score is misspecified (McCandless, Douglas, Evans, & Smeeth, 2010 (2010) included the posterior distribution of the propensity score parameters as covariate input in the outcome model so that the flow of information between the propensity score and the outcome is restricted. This so-called sequential Bayesian propensity score analysis yields treatment effect estimates that are comparable to estimates obtained from frequentist propensity score analysis.…”

Section: Bayesian Propensity Score Approachmentioning

confidence: 99%

Covariate Balance in Bayesian Propensity Score Approaches for Observational Studies

Chen

Kaplan

2015

Journal of Research on Educational Effectiveness

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Bayesian alternatives to frequentist propensity score approaches have recently been proposed. However, few studies have investigated their covariate balancing properties. This article compares a recently developed two-step Bayesian propensity score approach to the frequentist approach with respect to covariate balance. The effects of different priors on covariate balance are evaluated and the differences between frequentist and Bayesian covariate balance are discussed. Results of the case study reveal that both the Bayesian and frequentist propensity score approaches achieve good covariate balance. The frequentist propensity score approach performs slightly better on covariate balance for stratification and weighting methods, whereas the two-step Bayesian approach offers slightly better covariate balance in the optimal full matching method. Results of a comprehensive simulation study reveal that accuracy and precision of prior information on propensity score model parameters do not greatly influence balance performance. Results of the simulation study also show that overall, the optimal full matching method provides the best covariate balance and treatment effect estimates compared to the stratification and weighting methods. A unique feature of covariate balance within Bayesian propensity score analysis is that we can obtain a distribution of balance indices in addition to the point estimates so that the variation in balance indices can be naturally captured to assist in covariate balance checking.

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Cutting Feedback in Bayesian Regression Adjustment for the Propensity Score

Cited by 54 publications

References 13 publications

Factors Influencing Graft Choice in Revision Anterior Cruciate Ligament Reconstruction in the MARS Group

Factors Influencing Graft Choice in Revision Anterior Cruciate Ligament Reconstruction in the MARS Group

The Central Role of Bayes’ Theorem for Joint Estimation of Causal Effects and Propensity Scores

Covariate Balance in Bayesian Propensity Score Approaches for Observational Studies

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