PSAgraphics: An R Package to Support Propensity Score Analysis

Propensity score analysis is a technique for adjusting for selection bias in observational data. Estimated propensity scores (probability of treatment given observed covariates) are used for stratification of observations. Within strata covariates should be more balanced between the two treatments than without the stratification. PSAgraphics is an R package that provides flexible graphical tools to assess within strata balance between treatment groups, as well as how covariate distributions differ across strata. Additional graphical tools facilitate estimation of treatment effects having adjusted for covariate differences. Several new and conventional numerical measures of balance are also provided.

PSAgraphics: An R Package to Support Propensity Score Analysis

Propensity score analysis is a technique for adjusting for selection bias in observational data. Estimated propensity scores (probability of treatment given observed covariates) are used for stratification of observations. Within strata covariates should be more balanced between the two treatments than without the stratification. PSAgraphics is an R package that provides flexible graphical tools to assess within strata balance between treatment groups, as well as how covariate distributions differ across strata. Additional graphical tools facilitate estimation of treatment effects having adjusted for covariate differences. Several new and conventional numerical measures of balance are also provided

The Effects of Linguistic Features and Evaluation Perspective on Perceived Difficulty of Medical Text

Millions of laypersons need more medical information than they are customarily provided during their doctor’s visit. Health websites can help fill this knowledge gap, but the text is believed to be too difficult to understand for many laypersons. To help write text that is not perceived as too difficult and leads to better comprehension (actual difficulty), we study how linguistic structures influence text difficulty. Since perceived difficulty has been shown to be a barrier to self-education, evaluating perceived difficulty is an essential first step to take. In this study, we evaluated the impact of noun phrase complexity and of function word density in four sentence structures (active, passive, sentential or extraposed subject). Complex noun phrases significantly increased perceived difficulty while using more function words significantly decreased perceived difficulty. Furthermore, laypersons judge text differently when they perform the evaluation on behalf of themselves compared to evaluating on behalf of other readers

Evaluating Online Health Information: Beyond Readability Formulas

Although understanding health information is important, the texts provided are often difficult to understand. There are formulas to measure readability levels, but there is little understanding of how linguistic structures contribute to these difficulties. We are developing a toolkit of linguistic metrics that are validated with representative users and can be measured automatically. In this study, we provide an overview of our corpus and how readability differs by topic and source. We compare two documents for three groups of linguistic metrics. We report on a user study evaluating one of the differentiating metrics: the percentage of function words in a sentence. Our results show that this percentage correlates significantly with ease of understanding as indicated by users but not with the readability formula levels commonly used. Our study is the first to propose a user validated metric, different from readability formulas