Matching members of a treatment group (cases) to members of a no treatment group (controls) is often used in observational studies to reduce bias and approximate a randomized trial. There is often a trade-off when matching cases to controls and two types of bias can be introduced. While trying to maximize exact matches, cases may be excluded due to incomplete matching. While trying to maximize ...

BACKGROUND Propensity score methods have become a popular tool for reducing selection bias in making causal inference from observational studies in medical research. Propensity score matching, a key component of propensity score methods, normally matches units based on the distance between point estimates of the propensity scores. The problem with this technique is that it is difficult to estab...

BACKGROUND Cohort matching and regression modeling are used in observational studies to control for confounding factors when estimating treatment effects. Our objective was to evaluate exact matching and propensity score methods by applying them in a 1-year pre-post historical database study to investigate asthma-related outcomes by treatment. METHODS We drew on longitudinal medical record da...

We examine the effect of mandatory sustainability reporting on corporate disclosure practices. Specifically, we examine regulations mandating the disclosure of environmental, social, and governance information in China, Denmark, Malaysia, and South Africa using differences-in-differences estimation with propensity score matched samples. We find significant heterogeneity in corporate disclosure ...

The employment of damage mitigation measures by individuals is an important component of integrated flood risk management. In order to promote efficient damage mitigation measures, accurate estimates of their damage mitigation potential are required. That is, for correctly assessing the damage mitigation measures’ effectiveness from survey data, one needs to control for sources of bias. A biase...

In many observational studies, researchers estimate treatment effects using propensity score matching techniques. Estimation of propensity scores are complicated when some values of the covariates are missing. We can use multiple imputation to create completed datasets, from which propensity scores can be computed; however, we may be sensitive to the accuracy of the imputation models. We propos...

Matching is an R package which provides functions for multivariate and propensity score matching and for finding optimal covariate balance based on a genetic search algorithm. A variety of univariate and multivariate metrics to determine if balance actually has been obtained are provided. The underlying matching algorithm is written in C++, makes extensive use of system BLAS and scales efficien...

Objectives This study tracked the behavior of male inmates housed in the general inmate populations of 70 different prison units from a large southern state. Each of the inmates studied engaged in violent misconduct at least once during the first 2 years of incarceration (n = 3,808). The goal of the study was to isolate the effect of exposure to short-term solitary confinement (SC) as a punishm...