Modern Algorithms for Matching in Observational Studies

2020 ◽  
Vol 7 (1) ◽  
pp. 143-176 ◽  
Author(s):  
Paul R. Rosenbaum
Keyword(s):  
Sensitivity Analysis ◽  
Propensity Score ◽  
Observational Studies ◽  
Propensity Scores ◽  
Variable Number ◽  
Exact Matching ◽  
Covariate Balance ◽  
Software Packages ◽  
Effective Use ◽  
Entire Number

Using a small example as an illustration, this article reviews multivariate matching from the perspective of a working scientist who wishes to make effective use of available methods. The several goals of multivariate matching are discussed. Matching tools are reviewed, including propensity scores, covariate distances, fine balance, and related methods such as near-fine and refined balance, exact and near-exact matching, tactics addressing missing covariate values, the entire number, and checks of covariate balance. Matching structures are described, such as matching with a variable number of controls, full matching, subset matching and risk-set matching. Software packages in R are described. A brief review is given of the theory underlying propensity scores and the associated sensitivity analysis concerning an unobserved covariate omitted from the propensity score.

scholarly journals Propensity score matching in otolaryngologic literature: A systematic review and critical appraisal

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0244423
Author(s):  
Aman Prasad ◽  
Max Shin ◽  
Ryan M. Carey ◽  
Kevin Chorath ◽  
Harman Parhar ◽  
...  
Keyword(s):  
Systematic Review ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Observational Studies ◽  
Critical Appraisal ◽  
Propensity Scores ◽  
Final Analysis ◽  
Publication History ◽  
Covariate Balance

Background Propensity score techniques can reduce confounding and bias in observational studies. Such analyses are able to measure and balance pre-determined covariates between treated and untreated groups, leading to results that can approximate those generated by randomized prospective studies when such trials are not feasible. The most commonly used propensity score -based analytic technique is propensity score matching (PSM). Although PSM popularity has continued to increase in medical literature, improper methodology or methodological reporting may lead to biased interpretation of treatment effects or limited scientific reproducibility and generalizability. In this study, we aim to characterize and assess the quality of PSM methodology reporting in high-impact otolaryngologic literature. Methods PubMed and Embase based systematic review of the top 20 journals in otolaryngology, as measured by impact factor from the Journal Citations Reports from 2012 to 2018, for articles using PSM analysis throughout their publication history. Eligible articles were reviewed and assessed for quality and reporting of PSM methodology. Results Our search yielded 101 studies, of which 92 were eligible for final analysis and review. The proportion of studies utilizing PSM increased significantly over time (p < 0.001). Nearly all studies (96.7%, n = 89) specified the covariates used to calculate propensity scores. Covariate balance was illustrated in 67.4% (n = 62) of studies, most frequently through p-values. A minority (17.4%, n = 16) of studies were found to be fully reproducible according to previously established criteria. Conclusions While PSM analysis is becoming increasingly prevalent in otolaryngologic literature, the quality of PSM methodology reporting can be improved. We provide potential recommendations for authors regarding optimal reporting for analyses using PSM.

scholarly journals Entropy Balancing is Doubly Robust

2016 ◽  
Vol 5 (1) ◽  
Author(s):  
Qingyuan Zhao ◽  
Daniel Percival
Keyword(s):  
Propensity Score ◽  
Observational Studies ◽  
Optimization Problem ◽  
Entropy Maximization ◽  
Entropy Balancing ◽  
Covariate Balance ◽  
Original Proposal ◽  
Variance Bound ◽  
Doubly Robust ◽  
Theoretical Results

AbstractCovariate balance is a conventional key diagnostic for methods estimating causal effects from observational studies. Recently, there is an emerging interest in directly incorporating covariate balance in the estimation. We study a recently proposed entropy maximization method called Entropy Balancing (EB), which exactly matches the covariate moments for the different experimental groups in its optimization problem. We show EB is doubly robust with respect to linear outcome regression and logistic propensity score regression, and it reaches the asymptotic semiparametric variance bound when both regressions are correctly specified. This is surprising to us because there is no attempt to model the outcome or the treatment assignment in the original proposal of EB. Our theoretical results and simulations suggest that EB is a very appealing alternative to the conventional weighting estimators that estimate the propensity score by maximum likelihood.

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.

scholarly journals Chasing Balance and Other Recommendations for Improving Nonparametric Propensity Score Models

2017 ◽  
Vol 5 (2) ◽  
Author(s):  
Beth Ann Griffin ◽  
Daniel F. McCaffrey ◽  
Daniel Almirall ◽  
Lane F. Burgette ◽  
Claude Messan Setodji
Keyword(s):  
Propensity Score ◽  
Propensity Scores ◽  
Model Fit ◽  
Estimation Model ◽  
Propensity Score Model ◽  
Treatment Groups ◽  
Covariate Balance ◽  
Learning Technique ◽  
Accurate Treatment ◽  
Score Model

Abstract:In this article, we carefully examine two important implementation issues when estimating propensity scores using generalized boosted models (GBM), a promising machine learning technique. First, we examine which of the following methods for tuning GBM lead to better covariate balance and inferences about causal effects: pursuing covariate balance between the treatment groups or tuning the propensity score model on the basis of a model fit criterion. Second, we examine how well GBM can handle irrelevant covariates that are included in the estimation model. We find that chasing balance rather than model fit when estimating propensity scores yielded better covariate balance and more accurate treatment effect estimates. Additionally, we find that adding irrelevant covariates to GBM increased imbalance and bias in the treatment effects. The findings from this paper have useful implications for other work focused on improving methods for estimating propensity scores.

Confounders and Using the Method of Propensity Scores

2018 ◽  
pp. 362-376
Author(s):  
Chin Lin ◽  
Rui Imamura ◽  
Felipe Fregni
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Observational Studies ◽  
Propensity Scores ◽  
Robust Method ◽  
Case Discussion ◽  
Potential Relationship

This chapter explores the important issue of confounding in observational studies. The potential imbalances that result for not controlling assignment of treatment or exposure may lead to imbalance of variables that are associated with both treatment and intervention (or exposure) thus confounding results. Therefore, in this context, a potential relationship between an intervention and an outcome could be invalid. This chapter therefore explains basic definitions of confounding and presents some methods to control for confounders, highlighting the use of the propensity score, which is considered a robust method for this purpose. Different techniques of adjustment using propensity score (matching, stratification, regression, and weighting) are also discussed. This chapter concludes with a case discussion about confounding and how to address it.

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 Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies

2012 ◽  
Vol 20 (1) ◽  
pp. 25-46 ◽  
Author(s):  
Jens Hainmueller
Keyword(s):  
Monte Carlo ◽  
Propensity Score ◽  
Observational Studies ◽  
Causal Effects ◽  
Control Group ◽  
Higher Moments ◽  
Large Set ◽  
Entropy Balancing ◽  
Covariate Balance ◽  
And Control

This paper proposes entropy balancing, a data preprocessing method to achieve covariate balance in observational studies with binary treatments. Entropy balancing relies on a maximum entropy reweighting scheme that calibrates unit weights so that the reweighted treatment and control group satisfy a potentially large set of prespecified balance conditions that incorporate information about known sample moments. Entropy balancing thereby exactly adjusts inequalities in representation with respect to the first, second, and possibly higher moments of the covariate distributions. These balance improvements can reduce model dependence for the subsequent estimation of treatment effects. The method assures that balance improves on all covariate moments included in the reweighting. It also obviates the need for continual balance checking and iterative searching over propensity score models that may stochastically balance the covariate moments. We demonstrate the use of entropy balancing with Monte Carlo simulations and empirical applications.

Comparative effectiveness of adjuvant chemotherapy (AC) versus observation in patients with ≥ pT3 and/or pN+ bladder cancer (BCa).

2015 ◽  
Vol 33 (7_suppl) ◽  
pp. 292-292 ◽  
Author(s):  
Matt D. Galsky ◽  
Kristian Stensland ◽  
Erin L. Moshier ◽  
John Sfakianos ◽  
Russell Bailey McBride ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Overall Survival ◽  
Propensity Score ◽  
Survival Data ◽  
Comparative Effectiveness ◽  
Propensity Scores ◽  
Proportional Hazards ◽  
Private Insurance ◽  
Cox Proportional Hazards ◽  
The Impact

292 Background: Though Level I evidence supports the use of neoadjuvant chemotherapy (NAC) in BCa, the data supporting AC has been mixed. Experience suggests an adequately powered trial to definitively assess the role of AC is unlikely to be completed. Alternative approaches to evidence development are necessary. Methods: Patients who underwent cystectomy for ≥pT3 and/or pN+ M0 BCa were identified from the National Cancer Database. Patients who received NAC and/or diagnosed after 2006 (most recent year with survival data) were excluded. Logistic regression was used to calculate propensity scores representing the predicted probabilities of assignment to AC versus observation based on: age, demographics, year of diagnosis, pT stage, margin status, lymph node density, distance to hospital, hospital cystectomy volume, and hospital type and location. A propensity score-matched cohort of AC versus observation (1:2) patients was created. Stratified Cox proportional hazards regression was used to estimate the hazard ratio (HR) for overall survival for the matched sample while propensity score adjusted and inverse probability of treatment weighted proportional hazards models were used to estimate adjusted HR for the full sample. A sensitivity analysis examined the impact of comorbidities. Results: 3,294 patients undergoing cystectomy alone and 937 patients undergoing cystectomy plus AC met inclusion criteria.Patients receiving AC were significantly more likely to: be younger, have more lymph nodes examined and involved, have higher pT stage, have positive margins, reside in the Northeast and closer to the hospital, and have private insurance. AC was associated with improved overall survival (Table). The results were robust to sensitivity analysis for comorbidities. Conclusions: AC was associated with improved survival in patients with ≥pT3 and/or pN+ BCa in this large comparative effectiveness analysis. [Table: see text]

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