Calendar time-specific propensity scores and comparative effectiveness research for stage III colon cancer chemotherapy.
Christina D. Mack,Robert J. Glynn,M. Alan Brookhart,William R. Carpenter,Anne Marie Meyer,Robert S. Sandler,Til Stürmer +6 more
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TLDR
This work uses calendar time‐specific PSs to examine the effect of oxaliplatin during dissemination from off‐label to widespread use and finds no change in channeling experienced by innovative treatments.Abstract:
Purpose
Nonexperimental studies of treatment effectiveness provide an important complement to randomized trials by including heterogeneous populations. Propensity scores (PSs) are common in these studies but may not adequately capture changes in channeling experienced by innovative treatments. We use calendar time-specific (CTS) PSs to examine the effect of oxaliplatin during dissemination from off-label to widespread use.
Methods
Stage III colon cancer patients aged 65+ years initiating chemotherapy between 2003 and 2006 were examined using cancer registry data linked with Medicare claims. Two PS approaches for receipt of oxaliplatin versus 5-flourouricil were constructed using logistic models with key components of age, sex, substage, grade, census-level income, and comorbidities: (i) a conventional, year-adjusted PS and (ii) a CTS PS constructed and matched separately within 1-year intervals, then combined. We compared PS-matched hazard ratios (HRs) for mortality using Cox models.
Results
Oxaliplatin use increased significantly; 8% (n = 86) of patients received it in the first time period versus 52% (n = 386) in the last. Channeling by comorbidities, income, and age appeared to change over time. The CTS PS improved covariate balance within calendar time strata and yielded an attenuated estimated benefit of oxaliplatin (HR = 0.75) compared with the conventional PS (HR = 0.69).
Conclusion
In settings where prescribing patterns have changed and calendar time acts as a confounder, a CTS PS can characterize changes in treatment choices and estimating separate PSs within specific calendar time periods may result in enhanced confounding control. To increase validity of comparative effectiveness research, researchers should carefully consider drug lifecycles and effects of innovative treatment dissemination over time. Copyright © 2013 John Wiley & Sons, Ltd.read more
Citations
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Safety and effectiveness of dabigatran and warfarin in routine care of patients with atrial fibrillation
John D. Seeger,Katsiaryna Bykov,Dorothee B. Bartels,Krista F. Huybrechts,Kristina Zint,Sebastian Schneeweiss +5 more
TL;DR: In routine care of patients with non-valvular atrial fibrillation, dabigatran treatment resulted in improved health outcomes compared with warfarin, and no meaningful heterogeneity was identified across subgroups.
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Instrumental variable additive hazards models
TL;DR: A closed-form, two-stage estimator for the causal effect in the additive hazard model is developed and the resulting inferences are shown to perform well in simulation studies and in an application to a data set on the effectiveness of a novel chemotherapeutic agent for colon cancer.
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Proton pump inhibitors and risk of gastric cancer: population-based cohort study.
Devin Abrahami,Emily G. McDonald,Mireille E. Schnitzer,Alan N. Barkun,Samy Suissa,Laurent Azoulay +5 more
TL;DR: In this paper, the authors conducted a population-based cohort study using a new-user active comparator design to determine whether new users of proton pump inhibitors (PPIs) are at an increased risk of gastric cancer compared with new patients of histamine-2 receptor antagonists (H2RAs).
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Matching on the disease risk score in comparative effectiveness research of new treatments
Richard Wyss,Richard Wyss,Alan R. Ellis,M. Alan Brookhart,Michele Jonsson Funk,Cynthia J. Girman,Ross J. Simpson,Til Stürmer +7 more
TL;DR: This work uses simulations and an empirical example to evaluate the performance of disease risk score (DRS) matching compared with propensity score (PS) matching when controlling large numbers of covariates in settings involving newly introduced treatments.
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Matching with time-dependent treatments: A review and look forward
TL;DR: This tutorial defines a class of longitudinal matching methods that emulate this experiment and provides a review of existing variations, with guidance regarding study design, execution, and analysis.
References
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The central role of the propensity score in observational studies for causal effects
TL;DR: The authors discusses the central role of propensity scores and balancing scores in the analysis of observational studies and shows that adjustment for the scalar propensity score is sufficient to remove bias due to all observed covariates.
Journal ArticleDOI
Oxaliplatin, Fluorouracil, and Leucovorin as Adjuvant Treatment for Colon Cancer
Thierry André,Corrado Boni,Lamia Mounedji-Boudiaf,Matilde Navarro,Josep Tabernero,Tamas Hickish,C. Topham,Marta Zaninelli,Phillip Clingan,John Bridgewater,Isabelle Tabah-Fisch,Aimery de Gramont +11 more
TL;DR: Adding oxaliplatin to a regimen of fluorouracil and leucovorin improves the adjuvant treatment of colon cancer.
Journal ArticleDOI
Estimating Causal Effects from Large Data Sets Using Propensity Scores
TL;DR: Propensity score methods generalize subclassification in the presence of many confounding covariates, such as age, region of the country, and sex, in a study of smoking and mortality.
Journal ArticleDOI
Improved Overall Survival With Oxaliplatin, Fluorouracil, and Leucovorin As Adjuvant Treatment in Stage II or III Colon Cancer in the MOSAIC Trial
Thierry André,Corrado Boni,Matilde Navarro,Josep Tabernero,Tamas Hickish,C. Topham,Andrea Bonetti,Philip Clingan,John Bridgewater,Fernanado Rivera,Aimery de Gramont +10 more
TL;DR: Adding oxaliplatin to LV5FU2 significantly improved 5-year DFS and 6-year OS in the adjuvant treatment of stage II or III colon cancer and should be considered after surgery for patients with stage III disease.
Journal ArticleDOI
Variable Selection for Propensity Score Models
M. Alan Brookhart,Sebastian Schneeweiss,Kenneth J. Rothman,Kenneth J. Rothman,Robert J. Glynn,Jerry Avorn,Til Stürmer +6 more
TL;DR: The authors present the results of two simulation studies designed to help epidemiologists gain insight into the variable selection problem in a PS analysis, which suggest that standard model-building tools designed to create good predictive models of the exposure will not always lead to optimal PS models, particularly in small studies.