Oversampling and replacement strategies in propensity score matching: a critical review focused on small sample size in clinical settings

Abstract Background Propensity score matching is a statistical method that is often used to make inferences on the treatment effects in observational studies. In recent years, there has been widespread use of the technique in the cardiothoracic surgery literature to evaluate to potential benefits of...

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Autores principales:	Daniele Bottigliengo, Ileana Baldi, Corrado Lanera, Giulia Lorenzoni, Jonida Bejko, Tomaso Bottio, Vincenzo Tarzia, Massimiliano Carrozzini, Gino Gerosa, Paola Berchialla, Dario Gregori
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Propensity score matching Oversampling Replacement Small samples Monte Carlo simulations Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/e3859e8096a64fae973d7ae5637332e0
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spelling	oai:doaj.org-article:e3859e8096a64fae973d7ae5637332e02021-11-28T12:38:44ZOversampling and replacement strategies in propensity score matching: a critical review focused on small sample size in clinical settings10.1186/s12874-021-01454-z1471-2288https://doaj.org/article/e3859e8096a64fae973d7ae5637332e02021-11-01T00:00:00Zhttps://doi.org/10.1186/s12874-021-01454-zhttps://doaj.org/toc/1471-2288Abstract Background Propensity score matching is a statistical method that is often used to make inferences on the treatment effects in observational studies. In recent years, there has been widespread use of the technique in the cardiothoracic surgery literature to evaluate to potential benefits of new surgical therapies or procedures. However, the small sample size and the strong dependence of the treatment assignment on the baseline covariates that often characterize these studies make such an evaluation challenging from a statistical point of view. In such settings, the use of propensity score matching in combination with oversampling and replacement may provide a solution to these issues by increasing the initial sample size of the study and thus improving the statistical power that is needed to detect the effect of interest. In this study, we review the use of propensity score matching in combination with oversampling and replacement in small sample size settings. Methods We performed a series of Monte Carlo simulations to evaluate how the sample size, the proportion of treated, and the assignment mechanism affect the performances of the proposed approaches. We assessed the performances with overall balance, relative bias, root mean squared error and nominal coverage. Moreover, we illustrate the methods using a real case study from the cardiac surgery literature. Results Matching without replacement produced estimates with lower bias and better nominal coverage than matching with replacement when 1:1 matching was considered. In contrast to that, matching with replacement showed better balance, relative bias, and root mean squared error than matching without replacement for increasing levels of oversampling. The best nominal coverage was obtained by using the estimator that accounts for uncertainty in the matching procedure on sets of units obtained after matching with replacement. Conclusions The use of replacement provides the most reliable treatment effect estimates and that no more than 1 or 2 units from the control group should be matched to each treated observation. Moreover, the variance estimator that accounts for the uncertainty in the matching procedure should be used to estimate the treatment effect.Daniele BottigliengoIleana BaldiCorrado LaneraGiulia LorenzoniJonida BejkoTomaso BottioVincenzo TarziaMassimiliano CarrozziniGino GerosaPaola BerchiallaDario GregoriBMCarticlePropensity score matchingOversamplingReplacementSmall samplesMonte Carlo simulationsMedicine (General)R5-920ENBMC Medical Research Methodology, Vol 21, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Propensity score matching Oversampling Replacement Small samples Monte Carlo simulations Medicine (General) R5-920
spellingShingle	Propensity score matching Oversampling Replacement Small samples Monte Carlo simulations Medicine (General) R5-920 Daniele Bottigliengo Ileana Baldi Corrado Lanera Giulia Lorenzoni Jonida Bejko Tomaso Bottio Vincenzo Tarzia Massimiliano Carrozzini Gino Gerosa Paola Berchialla Dario Gregori Oversampling and replacement strategies in propensity score matching: a critical review focused on small sample size in clinical settings
description	Abstract Background Propensity score matching is a statistical method that is often used to make inferences on the treatment effects in observational studies. In recent years, there has been widespread use of the technique in the cardiothoracic surgery literature to evaluate to potential benefits of new surgical therapies or procedures. However, the small sample size and the strong dependence of the treatment assignment on the baseline covariates that often characterize these studies make such an evaluation challenging from a statistical point of view. In such settings, the use of propensity score matching in combination with oversampling and replacement may provide a solution to these issues by increasing the initial sample size of the study and thus improving the statistical power that is needed to detect the effect of interest. In this study, we review the use of propensity score matching in combination with oversampling and replacement in small sample size settings. Methods We performed a series of Monte Carlo simulations to evaluate how the sample size, the proportion of treated, and the assignment mechanism affect the performances of the proposed approaches. We assessed the performances with overall balance, relative bias, root mean squared error and nominal coverage. Moreover, we illustrate the methods using a real case study from the cardiac surgery literature. Results Matching without replacement produced estimates with lower bias and better nominal coverage than matching with replacement when 1:1 matching was considered. In contrast to that, matching with replacement showed better balance, relative bias, and root mean squared error than matching without replacement for increasing levels of oversampling. The best nominal coverage was obtained by using the estimator that accounts for uncertainty in the matching procedure on sets of units obtained after matching with replacement. Conclusions The use of replacement provides the most reliable treatment effect estimates and that no more than 1 or 2 units from the control group should be matched to each treated observation. Moreover, the variance estimator that accounts for the uncertainty in the matching procedure should be used to estimate the treatment effect.
format	article
author	Daniele Bottigliengo Ileana Baldi Corrado Lanera Giulia Lorenzoni Jonida Bejko Tomaso Bottio Vincenzo Tarzia Massimiliano Carrozzini Gino Gerosa Paola Berchialla Dario Gregori
author_facet	Daniele Bottigliengo Ileana Baldi Corrado Lanera Giulia Lorenzoni Jonida Bejko Tomaso Bottio Vincenzo Tarzia Massimiliano Carrozzini Gino Gerosa Paola Berchialla Dario Gregori
author_sort	Daniele Bottigliengo
title	Oversampling and replacement strategies in propensity score matching: a critical review focused on small sample size in clinical settings
title_short	Oversampling and replacement strategies in propensity score matching: a critical review focused on small sample size in clinical settings
title_full	Oversampling and replacement strategies in propensity score matching: a critical review focused on small sample size in clinical settings
title_fullStr	Oversampling and replacement strategies in propensity score matching: a critical review focused on small sample size in clinical settings
title_full_unstemmed	Oversampling and replacement strategies in propensity score matching: a critical review focused on small sample size in clinical settings
title_sort	oversampling and replacement strategies in propensity score matching: a critical review focused on small sample size in clinical settings
publisher	BMC
publishDate	2021
url	https://doaj.org/article/e3859e8096a64fae973d7ae5637332e0
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Oversampling and replacement strategies in propensity score matching: a critical review focused on small sample size in clinical settings

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