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Evaluating Propensity Score Methods in a Quasi-Experimental Study of the Impact of Menu-Labeling

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  • Stephanie L Mayne
  • Brian K Lee
  • Amy H Auchincloss

Abstract

Background: Quasi-experimental studies of menu labeling have found mixed results for improving diet. Differences between experimental groups can hinder interpretation. Propensity scores are an increasingly common method to improve covariate balance, but multiple methods exist and the improvements associated with each method have rarely been compared. In this re-analysis of the impact of menu labeling, we compare multiple propensity score methods to determine which methods optimize balance between experimental groups. Methods: Study participants included adult customers who visited full-service restaurants with menu labeling (treatment) and without (control). We compared the balance between treatment groups obtained by four propensity score methods: 1) 1:1 nearest neighbor matching (NN), 2) augmented 1:1 NN (using caliper of 0.2 and an exact match on an imbalanced covariate), 3) full matching, and 4) inverse probability weighting (IPW). We then evaluated the treatment effect on differences in nutrients purchased across the different methods. Results: 1:1 NN resulted in worse balance than the original unmatched sample (average standardized absolute mean distance [ASAM]: 0.185 compared to 0.171). Augmented 1:1 NN improved balance (ASAM: 0.038) but resulted in a large reduction in sample size. Full matching and IPW improved balance over the unmatched sample without a reduction in sample size (ASAM: 0.049 and 0.031, respectively). Menu labeling was associated with decreased calories, fat, sodium and carbohydrates in the unmatched analysis. Results were qualitatively similar in the propensity score matched/weighted models. Conclusions: While propensity scores offer an increasingly popular tool to improve causal inference, choosing the correct method can be challenging. Our results emphasize the benefit of examining multiple methods to ensure results are consistent, and considering approaches beyond the most popular method of 1:1 NN matching.

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  • Stephanie L Mayne & Brian K Lee & Amy H Auchincloss, 2015. "Evaluating Propensity Score Methods in a Quasi-Experimental Study of the Impact of Menu-Labeling," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-12, December.
    • Handle: RePEc:plo:pone00:0144962
    DOI: 10.1371/journal.pone.0144962
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    References listed on IDEAS

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    1. Denis Conniffe & Vanessa Gash & Philip J. O'Connell, 2000. "Evaluating State Programmes - “Natural Experiments” and Propensity Scores," The Economic and Social Review, Economic and Social Studies, vol. 31(4), pages 283-308.
    2. Peter C. Austin, 2009. "The Relative Ability of Different Propensity Score Methods to Balance Measured Covariates Between Treated and Untreated Subjects in Observational Studies," Medical Decision Making, , vol. 29(6), pages 661-677, November.
    3. Ben B. Hansen, 2004. "Full Matching in an Observational Study of Coaching for the SAT," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 609-618, January.
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    2. Yahui Wang & Liangjie Xin & Xiubin Li & Jianzhong Yan, 2016. "Impact of Land Use Rights Transfer on Household Labor Productivity: A Study Applying Propensity Score Matching in Chongqing, China," Sustainability, MDPI, vol. 9(1), pages 1-18, December.
    3. Hebe N Gouda & Andrew Hodge & Raoul Bermejo III & Willibald Zeck & Eliana Jimenez-Soto, 2016. "The Impact of Healthcare Insurance on the Utilisation of Facility-Based Delivery for Childbirth in the Philippines," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-15, December.

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