scholarly journals Applying Propensity Score Methods in Clinical Research in Neurology

Neurology ◽  
2021 ◽  
pp. 10.1212/WNL.0000000000012777
Author(s):  
Peter C. Austin ◽  
Amy Ying Xin Yu ◽  
Manav V. Vyas ◽  
Moira K. Kapral
Keyword(s):  
Propensity Score ◽  
Clinical Research ◽  
Propensity Score Matching ◽  
Observational Studies ◽  
Observational Research ◽  
Covariate Adjustment ◽  
Inverse Probability ◽  
Propensity Score Methods ◽  
Neurological Research ◽  
Propensity Score Stratification

Propensity score-based analysis is increasingly being used in observational studies to estimate the effects of treatments, interventions, and exposures. We introduce the concept of the propensity score and how it can be used in observational research. We describe four different ways of using the propensity score: matching on the propensity score, inverse probability of treatment weighting using the propensity score, stratification on the propensity score, and covariate adjustment on the propensity score (with a focus on the first two). We provide recommendations for the use and reporting of propensity score methods for the conduct of observational studies in neurological research.

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.

Multiple imputation analysis for propensity score matching with missing causes of failure: An application to hepatocellular carcinoma data

2021 ◽  
pp. 096228022110370
Author(s):  
Seungbong Han ◽  
Kam-Wah Tsui ◽  
Hui Zhang ◽  
Gi-Ae Kim ◽  
Young-Suk Lim ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Chronic Hepatitis ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Survival Data ◽  
Observational Studies ◽  
Imputation Method ◽  
Competing Risk ◽  
Data Imputation ◽  
Causes Of Failure

Propensity score matching is widely used to determine the effects of treatments in observational studies. Competing risk survival data are common to medical research. However, there is a paucity of propensity score matching studies related to competing risk survival data with missing causes of failure. In this study, we provide guidelines for estimating the treatment effect on the cumulative incidence function when using propensity score matching on competing risk survival data with missing causes of failure. We examined the performances of different methods for imputing the data with missing causes. We then evaluated the gain from the missing cause imputation in an extensive simulation study and applied the proposed data imputation method to the data from a study on the risk of hepatocellular carcinoma in patients with chronic hepatitis B and chronic hepatitis C.

scholarly journals The Association between the Availability of Sugar-Sweetened Beverage in School Vending Machines and Its Consumption among Adolescents in California: A Propensity Score Matching Approach

2010 ◽  
Vol 2010 ◽  
pp. 1-5 ◽  
Author(s):  
Lu Shi
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
California Health Interview Survey ◽  
Cross Sectional ◽  
Sugar Sweetened Beverage ◽  
Sweetened Beverage ◽  
Vending Machines ◽  
Adolescent Sample ◽  
Interview Survey ◽  
Propensity Score Stratification

There is controversy over to what degree banning sugar-sweetened beverage (SSB) sales at schools could decrease the SSB intake. This paper uses the adolescent sample of 2005 California Health Interview Survey to estimate the association between the availability of SSB from school vending machines and the amount of SSB consumption. Propensity score stratification and kernel-based propensity score matching are used to address the selection bias issue in cross-sectional data. Propensity score stratification shows that adolescents who had access to SSB through their school vending machines consumed 0.170 more drinks of SSB than those who did not (). Kernel-based propensity score matching shows the SSB consumption difference to be 0.158 on the prior day (). This paper strengthens the evidence for the association between SSB availability via school vending machines and the actual SSB consumption, while future studies are needed to explore changes in other beverages after SSB becomes less available.

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