High-dimensional propensity score algorithm in comparative effectiveness research with time-varying interventions

2015 ◽  
Vol 34 (5) ◽  
pp. 753-781 ◽  
Author(s):  
Romain Neugebauer ◽  
Julie A. Schmittdiel ◽  
Zheng Zhu ◽  
Jeremy A. Rassen ◽  
John D. Seeger ◽  
...  
Keyword(s):  
Propensity Score ◽  
Comparative Effectiveness Research ◽  
Comparative Effectiveness ◽  
High Dimensional ◽  
Effectiveness Research ◽  
Time Varying

Targeted learning in real-world comparative effectiveness research with time-varying interventions

2014 ◽  
Vol 33 (14) ◽  
pp. 2480-2520 ◽  
Author(s):  
Romain Neugebauer ◽  
Julie A. Schmittdiel ◽  
Mark J. van der Laan
Keyword(s):  
Comparative Effectiveness Research ◽  
Real World ◽  
Comparative Effectiveness ◽  
Effectiveness Research ◽  
Time Varying

scholarly journals Validity of using cancer registry data for comparative effectiveness research

2019 ◽  
Author(s):  
Abhishek Kumar ◽  
Zachary D Guss ◽  
Patrick T Courtney ◽  
Vinit Nalawade ◽  
Paige Sheridan ◽  
...  
Keyword(s):  
Propensity Score ◽  
Cancer Registry ◽  
Comparative Effectiveness Research ◽  
Comparative Effectiveness ◽  
Randomized Trials ◽  
Randomized Clinical Trials ◽  
Registry Data ◽  
Effectiveness Research ◽  
Cancer Registry Data ◽  
Multivariable Analyses

AbstractPurposeResearchers often analyze cancer registry data to assess for differences in survival among cancer treatments. However, the retrospective non-random design of these analyses raise questions about study validity. The purpose of this study was to determine the extent to which comparative effectiveness analyses using cancer registry data produces results concordant with randomized clinical trials.MethodsWe identified 141 randomized clinical trials referenced in the National Comprehensive Cancer Network Clinical Practice Guidelines for 8 common solid tumor types. For each trial we identified subjects from the National Cancer Database (NCDB) matching the eligibility criteria of the randomized trial. With each trial we used three Cox regression models to determine hazard ratios (HRs) for overall survival including univariable, multivariable, and propensity score adjusted models. Multivariable and propensity score analyses controlled for potential confounders including demographic, comorbidity, clinical, treatment, and tumor-related variables. Each NCDB survival analysis was defined as discordant if the HR for the NCDB analysis fell outside the 95% confidence interval of the corresponding randomized trial.ResultsNCDB analyses produced HRs for survival discordant with randomized trials in 62 (44%) univariable analyses, 43 (30%) multivariable analyses, and 51 (36%) propensity score models. NCDB analyses produced p-values discordant with randomized trials in 83 (59%) univariable analyses, 76 (54%) multivariable analyses, and 78 (55%) propensity score models. We did not identify any clinical trial characteristic associated with discordance between NCDB analyses and randomized trials including disease site, type of clinical intervention, or severity of cancer.ConclusionComparative effectiveness research with cancer registry data often produces survival outcomes discordant to randomized clinical data. These findings help provide context for providers interpreting observational comparative effectiveness research in oncology.

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.

scholarly journals Propensity Score–Based Methods in Comparative Effectiveness Research on Coronary Artery Disease

2017 ◽  
Vol 187 (5) ◽  
pp. 1064-1078 ◽  
Author(s):  
Alexandra G Ellis ◽  
Thomas A Trikalinos ◽  
Benjamin S Wessler ◽  
John B Wong ◽  
Issa J Dahabreh
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Propensity Score ◽  
Comparative Effectiveness Research ◽  
Comparative Effectiveness ◽  
Effectiveness Research ◽  
Artery Disease
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