Value of Propensity Score Matching for Equalizing Comparator Groups in Observational Database Studies: A Case Study in Anti-infectives

2016 ◽  
Vol 38 (12) ◽  
pp. 2676-2681 ◽  
Author(s):  
C. Daniel Mullins ◽  
Frank R. Ernst ◽  
Michelle R. Krukas ◽  
Joseph Solomkin ◽  
Christian Eckmann ◽  
...  
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Observational Database

scholarly journals A Case Study About Why It Can Be Difficult To Test Whether Propensity Score Analysis Works in Field Experiments

2013 ◽  
Vol 3 (2) ◽  
pp. 1 ◽  
Author(s):  
William R. Shadish ◽  
Peter M. Steiner ◽  
Thomas D. Cook
Keyword(s):  
Experimental Data ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Field Experiments ◽  
Propensity Score Analysis ◽  
Randomized Experiment ◽  
Score Analysis ◽  
Quasi Experimental ◽  
Do So

Peikes, Moreno and Orzol (2008) sensibly caution researchers that propensity score analysis may not lead to valid causal inference in field applications. But at the same time, they made the far stronger claim to have performed an ideal test of whether propensity score matching in quasi-experimental data is capable of approximating the results of a randomized experiment in their dataset, and that this ideal test showed that such matching could not do so. In this article we show that their study does not support that conclusion because it failed to meet a number of basic criteria for an ideal test. By implication, many other purported tests of the effectiveness of propensity score analysis probably also fail to meet these criteria, and are therefore questionable contributions to the literature on the effects of propensity score analysis. DOI:10.2458/azu_jmmss_v3i2_shadish

scholarly journals Perbandingan Analisis Regresi Logistik dengan Analisis Propensity Score Matching pada Studi Kasus Imunisasi Bayi

2008 ◽  
Vol 2 (6) ◽  
pp. 282
Author(s):  
Waras Budi Utomo
Keyword(s):  
Logistic Regression ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Secondary Data ◽  
Propensity Score Matching Analysis ◽  
Factor Effect ◽  
Multivariable Analyses ◽  
Ideal Method ◽  
The Ideal

Analisis multivariat konvensioanal tidak selalu merupakan metode ideal untuk memprediksi efek pajanan pada studi-studi observasional. Ketika distribusi kovariat antara kelompok pajanan berbeda besar, penyesuaan dengan teknik multivariat konvensioanl tidak cukup menyeimbangkan kelompok tersebut. Bias yang tersisa dapat menghambat penarikan kesimpulan yang valid. Tujuan penelitian ini adalah membandingkan hasil analisis multivariat konvensional dengananalisis metoda propensity score matching pada studi kasus data sekunder imunisasi bayi ASUH KAP2 2003. Penelitian ini menemukan nilai OR metoda regresi logistik (0,99) berbeda dengan metoda propensity score matching (0,96). Metoda propensity score matching berhasil menjodohkan 574 subjek(68,27%). Untuk evaluasi pengaruh faktor risiko disarankan menggunakan model PSM karena mengurangi bias seleksi, tetapi untuk analisis faktor determinan yang banyak variabel independent, gunakan matching kerena variabel tersebut mempunyai posisi yang sama.Kata kunci : Regresi logistik, propensity score matching.AbstractConventional multivariable analyses may not always be the ideal method for estimating exposure effects in observational studies. Where there are large differences in the distribution of covariates between expose groups, adjusting with conventional multivariable techniques may not adequately balance the groups, and the remaining bias may limit valid causal inference. The objective of this research is to compare the result of convensional multiariate analysis versus propensity score matching analysis in case study of infant immunization using secondary data of ASUH KAP2 2003. Model will be compared without interaction variable. The results show that the OR from logistic regression (0,99) differs to propensity score matching (0,96). Propensity score matching is successfulin matching 574 subjects (68,27%). It is recommended to evaluate risk factor effect using PSM model, but to use logistic regression analysis for determinat factor analysis with many independent variables because the variables have the same position.Keywords: Logistic regression, propensity score matching.

scholarly journals AES Impact Evaluation With Integrated Farm Data: Combining Statistical Matching and Propensity Score Matching

2018 ◽  
Vol 10 (11) ◽  
pp. 4320 ◽  
Author(s):  
Riccardo D’Alberto ◽  
Matteo Zavalloni ◽  
Meri Raggi ◽  
Davide Viaggi
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Data Availability ◽  
Experimental Conditions ◽  
Additional Information ◽  
Statistical Matching ◽  
Combined Use ◽  
Wide Range ◽  
Environmental Public Goods

A large share of the Common Agricultural Policy (CAP) is allocated to agri-environmental schemes (AESs), whose goal is to foster the provision of a wide range of environmental public goods. Despite this effort, little is known on the actual environmental and economic impact of the AESs, due to the non-experimental conditions of the assessment exercise and several data availability issues. The main objective of the paper is to explore the feasibility of combining the non-parametric statistical matching (SM) method and propensity score matching (PSM) counterfactual approach analysis and to test its usefulness and practicability on a case study represented by selected impacts of the AESs in Emilia-Romagna. The work hints at the potentialities of the combined use of SM and PSM as well as of the systematic collection of additional information to be included in EU-financed project surveys in order to enrich and complete data collected in the official statistics. The results show that the combination of the two methods enables us to enlarge and deepen the scope of counterfactual analysis applied to AESs. In a specific case study, AESs seem to reduce the amount of rent-in land and decrease the crop mix diversity.

Fairtrade and Workers’ Welfare: A Case Study of the Soccer Industry in Pakistan

2019 ◽  
Vol 55 (4) ◽  
pp. 536-551
Author(s):  
Javeria Younas Abbasi ◽  
Musleh Ud Din
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Treatment Effect ◽  
Labor Laws ◽  
The South ◽  
Football Industry ◽  
The Impact

The resurgence of the debate on standardization of labor laws via trade has brought into focus the importance of labeling programs, which are seen as a desirable avenue to remedy weak labor laws in the “South.” This study aims to quantify the impact of the Fairtrade labeling program on the welfare position of workers in the football industry. Propensity Score Matching (PSM) is used to calculate the treatment effect of the program. The study finds no treatment effect of labeling upon the welfare condition of workers. It concludes that the overall program has failed to make an impact upon the lives of workers.

scholarly journals A Case Study About Why It Can Be Difficult To Test Whether Propensity Score Analysis Works in Field Experiments

2013 ◽  
Vol 3 (2) ◽  
pp. 1 ◽  
Author(s):  
William R. Shadish ◽  
Peter M. Steiner ◽  
Thomas D. Cook
Keyword(s):  
Experimental Data ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Field Experiments ◽  
Propensity Score Analysis ◽  
Randomized Experiment ◽  
Score Analysis ◽  
Quasi Experimental ◽  
Do So

Peikes, Moreno and Orzol (2008) sensibly caution researchers that propensity score analysis may not lead to valid causal inference in field applications. But at the same time, they made the far stronger claim to have performed an ideal test of whether propensity score matching in quasi-experimental data is capable of approximating the results of a randomized experiment in their dataset, and that this ideal test showed that such matching could not do so. In this article we show that their study does not support that conclusion because it failed to meet a number of basic criteria for an ideal test. By implication, many other purported tests of the effectiveness of propensity score analysis probably also fail to meet these criteria, and are therefore questionable contributions to the literature on the effects of propensity score analysis. DOI:10.2458/azu_jmmss_v3i2_shadish

The Relative Ability of Different Propensity Score Methods to Balance Measured Covariates Between Treated and Untreated Subjects in Observational Studies

2009 ◽  
Vol 29 (6) ◽  
pp. 661-677 ◽  
Author(s):  
Peter C. Austin
Keyword(s):  
Monte Carlo ◽  
Propensity Score ◽  
Monte Carlo Simulations ◽  
Propensity Score Matching ◽  
Superior Performance ◽  
Covariate Adjustment ◽  
Inverse Probability ◽  
Relative Ability ◽  
Baseline Covariates

The propensity score is a balancing score: conditional on the propensity score, treated and untreated subjects have the same distribution of observed baseline characteristics. Four methods of using the propensity score have been described in the literature: stratification on the propensity score, propensity score matching, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. However, the relative ability of these methods to reduce systematic differences between treated and untreated subjects has not been examined. The authors used an empirical case study and Monte Carlo simulations to examine the relative ability of the 4 methods to balance baseline covariates between treated and untreated subjects. They used standardized differences in the propensity score matched sample and in the weighted sample. For stratification on the propensity score, within-quintile standardized differences were computed comparing the distribution of baseline covariates between treated and untreated subjects within the same quintile of the propensity score. These quintile-specific standardized differences were then averaged across the quintiles. For covariate adjustment, the authors used the weighted conditional standardized absolute difference to compare balance between treated and untreated subjects. In both the empirical case study and in the Monte Carlo simulations, they found that matching on the propensity score and weighting using the inverse probability of treatment eliminated a greater degree of the systematic differences between treated and untreated subjects compared with the other 2 methods. In the Monte Carlo simulations, propensity score matching tended to have either comparable or marginally superior performance compared with propensity-score weighting.

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