scholarly journals An optimal medical therapy for efficient cardiovascular protection in subjects with coronary artery diseases in Japan: Comparisons and analyses by quasi-randomized controlled trials with propensity score matching and inverse probability treatment weighting methods for a Japanese cohort

2020 ◽  
Vol 4 (1) ◽  
pp. 7-15
Author(s):  
Tabito Kino ◽  
Tomoaki Ishigami ◽  
Hiroshi Doi ◽  
Lin Chen ◽  
Rie Nakashima-Sasaki ◽  
...  
Keyword(s):  
Coronary Artery ◽  
Propensity Score ◽  
Randomized Controlled Trials ◽  
Propensity Score Matching ◽  
Controlled Trials ◽  
Coronary Artery Diseases ◽  
Inverse Probability ◽  
Randomized Controlled ◽  
Japanese Cohort ◽  
Weighting Methods

Comparing Performance of Methods to Deal With Differential Attrition in Randomized Experimental Evaluations

2021 ◽  
Vol 45 (1-2) ◽  
pp. 70-104
Author(s):  
Kaitlin Anderson ◽  
Gema Zamarro ◽  
Jennifer Steele ◽  
Trey Miller
Keyword(s):  
Randomized Controlled Trials ◽  
Research Design ◽  
Treatment Effect ◽  
Controlled Trials ◽  
Probability Weighting ◽  
Attrition Rates ◽  
Inverse Probability ◽  
Randomized Controlled ◽  
Two Samples ◽  
Weighting Methods

Background: In randomized controlled trials, attrition rates often differ by treatment status, jeopardizing causal inference. Inverse probability weighting methods and estimation of treatment effect bounds have been used to adjust for this bias. Objectives: We compare the performance of various methods within two samples, both generated through lottery-based randomization: one with considerable differential attrition and an augmented dataset with less problematic attrition. Research Design: We assess the performance of various correction methods within the dataset with problematic attrition. In addition, we conduct simulation analyses. Results: Within the more problematic dataset, we find the correction methods often performed poorly. Simulation analyses indicate that deviations from the underlying assumptions for bounding approaches damage the performance of estimated bounds. Conclusions: We recommend the verification of the underlying assumptions in attrition correction methods whenever possible and, when verification is not possible, using these methods with caution.

scholarly journals Propensity score matching and inverse probability of treatment weighting to address confounding by indication in comparative effectiveness research of oral anticoagulants

2020 ◽  
Vol 9 (9) ◽  
pp. 603-614
Author(s):  
Victoria Allan ◽  
Sreeram V Ramagopalan ◽  
Jack Mardekian ◽  
Aaron Jenkins ◽  
Xiaoyan Li ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Comparative Effectiveness Research ◽  
Comparative Effectiveness ◽  
Controlled Trials ◽  
Effectiveness Research ◽  
Confounding By Indication ◽  
Inverse Probability ◽  
Randomized Controlled

After decades of warfarin being the only oral anticoagulant (OAC) widely available for stroke prevention in atrial fibrillation, four direct OACs (apixaban, dabigatran, edoxaban and rivaroxaban) were approved after demonstrating noninferior efficacy and safety versus warfarin in randomized controlled trials. Comparative effectiveness research of OACs based on real-world data provides complementary information to randomized controlled trials. Propensity score matching and inverse probability of treatment weighting are increasingly popular methods used to address confounding by indication potentially arising in comparative effectiveness research due to a lack of randomization in treatment assignment. This review describes the fundamentals of propensity score matching and inverse probability of treatment weighting, appraises differences between them and presents applied examples to elevate understanding of these methods within the atrial fibrillation field.

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