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 Approximate Bayesian Bootstrap procedures to estimate multilevel treatment effects in observational studies with application to type 2 diabetes treatment regimens

2020 ◽  
Vol 29 (11) ◽  
pp. 3362-3380
Author(s):  
Anthony D Scotina ◽  
Andrew R Zullo ◽  
Robert J Smith ◽  
Roee Gutman
Keyword(s):  
Type 2 Diabetes ◽  
Observational Studies ◽  
Treatment Effects ◽  
Propensity Scores ◽  
Randomized Clinical Trials ◽  
Bayesian Bootstrap ◽  
Interval Estimates ◽  
Treatment Regimens ◽  
Approximate Bayesian

Randomized clinical trials are considered as the gold standard for estimating causal effects. Nevertheless, in studies that are aimed at examining adverse effects of interventions, randomized trials are often impractical because of ethical and financial considerations. In observational studies, matching on the generalized propensity scores was proposed as a possible solution to estimate the treatment effects of multiple interventions. However, the derivation of point and interval estimates for these matching procedures can become complex with non-continuous or censored outcomes. We propose a novel Approximate Bayesian Bootstrap algorithm that results in statistically valid point and interval estimates of the treatment effects with categorical outcomes. The procedure relies on the estimated generalized propensity scores and multiply imputes the unobserved potential outcomes for each unit. In addition, we describe a corresponding interpretable sensitivity analysis to examine the unconfoundedness assumption. We apply this approach to examine the cardiovascular safety of common, real-world anti-diabetic treatment regimens for type 2 diabetes mellitus in a large observational database.

P6559Long-term follow-up and outcomes of patients with discontinuation of oral anticoagulant therapy after successful ablation procedures for atrial fibrillation

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
T Fujino ◽  
H Yuzawa ◽  
T Kinoshita ◽  
M Shinohara ◽  
H Koike ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Anticoagulant Therapy ◽  
Major Bleeding ◽  
Oral Anticoagulant ◽  
Oral Anticoagulant Therapy ◽  
Low Risk ◽  
Risk Scores ◽  
Af Ablation ◽  
Successful Ablation

Abstract Background Oral anticoagulant therapy (OAT) is effective for preventing strokes in atrial fibrillation (AF) patients. Currently, there is controversy regarding the discontinuation of OATs in patients with ablation procedures to eliminate AF. Aim We investigated the incidence of major bleeding and ischemic strokes/systemic embolisms in low-risk patients that discontinued OATs after successful AF ablation procedures. Methods Of 330 consecutive patients that underwent AF ablation procedures and were prescribed one of the direct oral anticoagulants or warfarin, 207 AF patients (158 men, mean age 61±11 years) who discontinued OATs three months after the procedure were enrolled. The average CHADS2 and HAS-BLED scores were 1.0±0.9 and 1.2±1.0, respectively, which meant that most patients had a low risk for strokes. Results During follow-up, 31 patients (15%) had recurrences of AF. Those patients underwent a re-ablation procedure and then re-discontinued their OATs three months after the session. During a 60±13 months follow-up, major bleeding was observed in five patients (2.4%) and was associated with a higher HAS-BLED score (2.2±0.4 vs. 1.1±1.0, P=0.027). In contrast, none of the patients experienced ischemic strokes/systemic embolisms. Conclusions This prospective study demonstrated that in patients with successful ablation procedures and low risk scores for AF management, OATs could be discontinued three months after the procedure. Unnecessary continuation of OATs may increase the incidence of major bleeding during the follow-up.

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 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.

scholarly journals Reappraisal of Non-vitamin K Antagonist Oral Anticoagulants in Atrial Fibrillation Patients: A Systematic Review and Meta-Analysis

2021 ◽  
Vol 8 ◽  
Author(s):  
Fuwei Liu ◽  
Yunyao Yang ◽  
Winglam Cheng ◽  
Jianyong Ma ◽  
Wengen Zhu
Keyword(s):  
Atrial Fibrillation ◽  
Systematic Review ◽  
Propensity Score ◽  
Vitamin K ◽  
Major Bleeding ◽  
Observational Studies ◽  
Meta Analysis ◽  
Oral Anticoagulants ◽  
Vitamin K Antagonist ◽  
Systemic Embolism

Background: Recent observational studies have compared effectiveness and safety profiles between non-vitamin K antagonist oral anticoagulants (NOACs) and warfarin in patients with atrial fibrillation (AF). Nevertheless, the confounders may exist due to the nature of clinical practice-based data, thus potentially influencing the reliability of results. This systematic review and meta-analysis were conducted to compare the effect of NOACs with warfarin based on the propensity score-based observational studies vs. randomized clinical trials (RCTs).Methods: Articles included were systematically searched from the PubMed and EMBASE databases until March 2021 to obtain relevant studies. The primary outcomes were stroke or systemic embolism (SSE) and major bleeding. Hazard ratios (HRs) and 95% confidence intervals (CIs) of the outcomes were extracted and then pooled by the random-effects model.Results: A total of 20 propensity score-based observational studies and 4 RCTs were included. Compared with warfarin, dabigatran (HR, 0.82 [95% CI, 0.71–0.96]), rivaroxaban (HR, 0.80 [95% CI, 0.75–0.85]), apixaban (HR, 0.75 [95% CI, 0.65–0.86]), and edoxaban (HR, 0.71 [95% CI, 0.60–0.83]) were associated with a reduced risk of stroke or systemic embolism, whereas dabigatran (HR, 0.76 [95% CI, 0.65–0.87]), apixaban (HR, 0.61 [95% CI, 0.56–0.67]), and edoxaban (HR, 0.58 [95% CI, 0.45–0.74]) but not rivaroxaban (HR, 0.92 [95% CI, 0.84–1.00]) were significantly associated with a decreased risk of major bleeding based on the observational studies. Furthermore, the risk of major bleeding with dabigatran 150 mg was significantly lower in observational studies than that in the RE-LY trial, whereas the pooled results of observational studies were similar to the data from the corresponding RCTs in other comparisons.Conclusion: Data from propensity score-based observational studies and NOAC trials consistently suggest that the use of four individual NOACs is non-inferior to warfarin for stroke prevention in AF patients.

scholarly journals A patient with atrial fibrillation and diabetes: selecting the optimal anticoagulant therapy regimen

2021 ◽  
Vol 26 (5) ◽  
pp. 4508
Author(s):  
A. G. Obrezan ◽  
A. E. Filippov ◽  
A. A. Obrezan
Keyword(s):  
Atrial Fibrillation ◽  
Anticoagulant Therapy ◽  
Risk Scores ◽  
Bleeding Events ◽  
Therapy Regimen ◽  
Severe Diabetes ◽  
Increased Risk ◽  
Patients With Diabetes ◽  
Artery Disease

Atrial fibrillation (AF) is a common arrhythmia in patients with type 2 diabetes (T2D). Patients with diabetes are at higher risk of AF than those without it. There is an increased risk of dysglycemia in AF. Patients with AF and concomitant diabetes are more likely to have coronary artery disease, hypertension, heart failure, while strokes in patients with AF and diabetes are more severe. Diabetes, in turn, causes the angiopathies and cardiopathy. There is a higher risk of both thrombotic and bleeding events in patients with AF and T2D. The article discusses the mutual burden of T2D and AF, as well as the risk scores for thrombotic, thromboembolic, and bleeding events. Anticoagulant therapy takes a special place in improving the prognosis in AF patients. Numerous studies and actual clinical practice have demonstrated the effectiveness of anticoagulants in the prevention of stroke and other comorbidities.

scholarly journals Evaluating Glaucoma Treatment Effect on Intraocular Pressure Reduction Using Propensity Score Weighted Regression

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Mengfei Wu ◽  
Mengling Liu ◽  
Joel S. Schuman ◽  
Yuyan Wang ◽  
Katie A. Lucy ◽  
...  
Keyword(s):  
Intraocular Pressure ◽  
Propensity Score ◽  
Selection Bias ◽  
Treatment Effect ◽  
Laser Surgery ◽  
Observational Studies ◽  
Combination Therapies ◽  
Treatment Groups ◽  
Valid Assessment ◽  
Important Evidence

Abstract Observational studies in glaucoma patients can provide important evidence on treatment effects, especially for combination therapies which are often used in reality. But the success relies on the reduction of selection bias through methods such as propensity score (PS) weighting. The objective of this study was to assess the effects of five glaucoma treatments (medication, laser, non-laser surgery (NLS), laser + medication, and NLS + medication) on 1-year intraocular pressure (IOP) change. Data were collected from 90 glaucoma subjects who underwent a single laser, or NLS intervention, and/or took the same medication for at least 6 months, and had IOP measures before the treatment and 12-months after. Baseline IOP was significantly different across groups (p = 0.007) and this unbalance was successfully corrected by the PS weighting (p = 0.81). All groups showed statistically significant PS-weighted IOP reductions, with the largest reduction in NLS group (−6.78 mmHg). Baseline IOP significantly interacted with treatments (p = 0.03), and at high baseline IOP medication was less effective than other treatments. Our findings showed that the 1-year IOP reduction differed across treatment groups and was dependent on baseline IOP. The use of PS-weighted methods reduced treatment selection bias at baseline and allowed valid assessment of the treatment effect in an observational study.

scholarly journals Estimation of Average Treatment Effects Based on Propensity Scores

2002 ◽  
Vol 2 (4) ◽  
pp. 358-377 ◽  
Author(s):  
Sascha O. Becker ◽  
Andrea Ichino
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Labor Economics ◽  
Treatment Effects ◽  
Propensity Scores ◽  
Short Overview ◽  
Average Treatment ◽  
Matching Estimators ◽  
Average Treatment Effects ◽  
Supported Work

In this paper, we give a short overview of some propensity score matching estimators suggested in the evaluation literature, and we provide a set of Stata programs, which we illustrate using the National Supported Work (NSW) demonstration widely known in labor economics.

scholarly journals Minimizing bias in massive multi-arm observational studies with BCAUS: balancing covariates automatically using supervision

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chinmay Belthangady ◽  
Will Stedden ◽  
Beau Norgeot
Keyword(s):  
Propensity Score ◽  
Causal Inference ◽  
Real World ◽  
Observational Studies ◽  
Treatment Effects ◽  
Infant Health ◽  
Development Program ◽  
Health Condition ◽  
Simultaneous Intervention ◽  
Inference Methods

Abstract Background Observational studies are increasingly being used to provide supplementary evidence in addition to Randomized Control Trials (RCTs) because they provide a scale and diversity of participants and outcomes that would be infeasible in an RCT. Additionally, they more closely reflect the settings in which the studied interventions will be applied in the future. Well-established propensity-score-based methods exist to overcome the challenges of working with observational data to estimate causal effects. These methods also provide quality assurance diagnostics to evaluate the degree to which bias has been removed and the estimates can be trusted. In large medical datasets it is common to find the same underlying health condition being treated with a variety of distinct drugs or drug combinations. Conventional methods require a manual iterative workflow, making them scale poorly to studies with many intervention arms. In such situations, automated causal inference methods that are compatible with traditional propensity-score-based workflows are highly desirable. Methods We introduce an automated causal inference method BCAUS, that features a deep-neural-network-based propensity model that is trained with a loss which penalizes both the incorrect prediction of the assigned treatment as well as the degree of imbalance between the inverse probability weighted covariates. The network is trained end-to-end by dynamically adjusting the loss term for each training batch such that the relative contributions from the two loss components are held fixed. Trained BCAUS models can be used in conjunction with traditional propensity-score-based methods to estimate causal treatment effects. Results We tested BCAUS on the semi-synthetic Infant Health & Development Program dataset with a single intervention arm, and a real-world observational study of diabetes interventions with over 100,000 individuals spread across more than a hundred intervention arms. When compared against other recently proposed automated causal inference methods, BCAUS had competitive accuracy for estimating synthetic treatment effects and provided highly concordant estimates on the real-world dataset but was an order-of-magnitude faster. Conclusions BCAUS is directly compatible with trusted protocols to estimate treatment effects and diagnose the quality of those estimates, while making the established approaches automatically scalable to an arbitrary number of simultaneous intervention arms without any need for manual iteration.

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