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An evaluation of exact matching and propensity score methods as applied in a comparative effectiveness study of inhaled corticosteroids in asthma

Authors Burden A, Roche N, Miglio C, Hillyer EV, Postma DS, Herings RMC, Overbeek JA, Khalid JM, van Eickels D, Price DB

Received 17 September 2016

Accepted for publication 19 December 2016

Published 22 March 2017 Volume 2017:8 Pages 15—30

DOI https://doi.org/10.2147/POR.S122563

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Akshita Wason

Peer reviewer comments 3

Editor who approved publication: Professor Christoph Meier


Anne Burden,1 Nicolas Roche,2 Cristiana Miglio,1 Elizabeth V Hillyer,1 Dirkje S Postma,3 Ron MC Herings,4 Jetty A Overbeek,4 Javaria Mona Khalid,5 Daniela van Eickels,6 David B Price1,7

1Observational and Pragmatic Research Institute Pte Ltd, Singapore; 2University Paris Descartes (EA2511), Cochin Hospital Group (AP-HP), Paris, France; 3Department of Pulmonology, University Medical Center Groningen, University of Groningen, Groningen, 4PHARMO Institute for Drug Outcomes Research, Utrech, the Netherlands; 5Takeda Development Centre Europe Ltd, London, UK; 6Takeda Pharmaceuticals International GmbH, Zurich, Switzerland; 7Academic Primary Care, University of Aberdeen, Aberdeen, UK

Background: Cohort matching and regression modeling are used in observational studies to control for confounding factors when estimating treatment effects. Our objective was to evaluate exact matching and propensity score methods by applying them in a 1-year pre–post historical database study to investigate asthma-related outcomes by treatment.
Methods: We drew on longitudinal medical record data in the PHARMO database for asthma patients prescribed the treatments to be compared (ciclesonide and fine-particle inhaled corticosteroid [ICS]). Propensity score methods that we evaluated were propensity score matching (PSM) using two different algorithms, the inverse probability of treatment weighting (IPTW), covariate adjustment using the propensity score, and propensity score stratification. We defined balance, using standardized differences, as differences of <10% between cohorts.
Results: Of 4064 eligible patients, 1382 (34%) were prescribed ciclesonide and 2682 (66%) fine-particle ICS. The IPTW and propensity score-based methods retained more patients (96%–100%) than exact matching (90%); exact matching selected less severe patients. Standardized differences were >10% for four variables in the exact-matched dataset and <10% for both PSM algorithms and the weighted pseudo-dataset used in the IPTW method. With all methods, ciclesonide was associated with better 1-year asthma-related outcomes, at one-third the prescribed dose, than fine-particle ICS; results varied slightly by method, but direction and statistical significance remained the same.
Conclusion: We found that each method has its particular strengths, and we recommend at least two methods be applied for each matched cohort study to evaluate the robustness of the findings. Balance diagnostics should be applied with all methods to check the balance of confounders between treatment cohorts. If exact matching is used, the calculation of a propensity score could be useful to identify variables that require balancing, thereby informing the choice of matching criteria together with clinical considerations.

Keywords: asthma, exact matching, propensity score, observational

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