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

<h4>Background</h4>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...

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Main Authors: Stephanie L Mayne, Brian K Lee, Amy H Auchincloss
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2015-01-01
Series:PLoS ONE
Online Access:https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0144962&type=printable
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author Stephanie L Mayne
Brian K Lee
Amy H Auchincloss
author_facet Stephanie L Mayne
Brian K Lee
Amy H Auchincloss
author_sort Stephanie L Mayne
collection DOAJ
description <h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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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spelling doaj-art-b2d3327bb38f44669e28dcae148ae2e62025-08-20T03:10:58ZengPublic Library of Science (PLoS)PLoS ONE1932-62032015-01-011012e014496210.1371/journal.pone.0144962Evaluating Propensity Score Methods in a Quasi-Experimental Study of the Impact of Menu-Labeling.Stephanie L MayneBrian K LeeAmy H Auchincloss<h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0144962&type=printable
spellingShingle Stephanie L Mayne
Brian K Lee
Amy H Auchincloss
Evaluating Propensity Score Methods in a Quasi-Experimental Study of the Impact of Menu-Labeling.
PLoS ONE
title Evaluating Propensity Score Methods in a Quasi-Experimental Study of the Impact of Menu-Labeling.
title_full Evaluating Propensity Score Methods in a Quasi-Experimental Study of the Impact of Menu-Labeling.
title_fullStr Evaluating Propensity Score Methods in a Quasi-Experimental Study of the Impact of Menu-Labeling.
title_full_unstemmed Evaluating Propensity Score Methods in a Quasi-Experimental Study of the Impact of Menu-Labeling.
title_short Evaluating Propensity Score Methods in a Quasi-Experimental Study of the Impact of Menu-Labeling.
title_sort evaluating propensity score methods in a quasi experimental study of the impact of menu labeling
url https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0144962&type=printable
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AT brianklee evaluatingpropensityscoremethodsinaquasiexperimentalstudyoftheimpactofmenulabeling
AT amyhauchincloss evaluatingpropensityscoremethodsinaquasiexperimentalstudyoftheimpactofmenulabeling