Sentence examples for matching treated and from inspiring English sources

Although there are several methods to use the PS for analysis, matching treated and untreated patients by the PS is recommended by most researchers among other reasons because this allows assessing covariate balance before and after matching.

Rosenbaum and Rubin (28) examined the bias due to incomplete and inexact matching when matching treated and untreated subjects on a set of baseline covariates.

After matching treated and untreated clients by age group, education, HIV serostatus, history of multiple partners, current marital status and gender, we used a logistic regression to facilitate predictions of clients' probability of condom uptake while controlling for demographic and behavioural factors.

Indeed, as this variable strongly affects both the probability of being an internal trainer and the probability of working in a changing firm, and given that males and females may differ in terms of background characteristics, we match treated and non-treated individuals within each stratum defined by gender and then we compute the ATET on average.

Note that, although we applied non-parametric matching, the shares of the matched treated and the matched controls are close but not always exactly the same.

Finally, if any confounding bias has been sufficiently eliminated, the treatment effect can be estimated by comparing the effect variable Y of the matched treated and control units.

For example, 97% of the matched treated and controls work on a permanent contract and 3% work for a temporary work agency.

In total for all years we observe displaced workers due to firm bankruptcies, we have 18,663 observations of matched treated and 49,827 observations of matched controls between 35 and 54 years of age, leaving us with an average of 2.7 controls per treated.

Thus, just as matching models rest on the assumption that all available observable characteristics are used to match treated and control firms, treatment effects models are also predicated on an important assumption: unobserved factors drive divestiture decisions, and these unobserved factors are being captured by the Inverse Mills Ratio.

Mean APACHE II scores were 24.5 for the matched treated and 23.9 for the matched untreated group (P = 0.54).

Mean Acute Physiology And Chronic Health Evaluation II (APACHE II) scores were 24.5 for the matched treated and 23.9 for the matched untreated group (P = 0.54).