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Using propensity score with Receiver Operating Characteristics (ROC) and bootstrap to evaluate effect size in observational studies

Górkiewicz M.

Biocybernetics and Biomedical Engineering

2009

Vol. 29, no. 4

41-61

In non-randomised studies, prioritisation of patients who are most likely to benefit from more expensive and more effective treatments usually take place and/or patients select themselves to treatments. Propensity score methods have been considered as means to reduce the effect of selection bias. In this study it was shown that use of receiver operating characteristics (ROC) and area under ROC (AUC) provides an additional insight into analysis of non-randomised studies. The estimates of mean effect obtained with five different techniques were compared and nonparametric bootstrap was recommended as superior tool for propensity score analyses.

Verification of ideological classifications - a statistical approach

Mazurkiewicz M., Mercik J., Dobrowolski W.

Control and Cybernetics

2001

Vol. 30, no 4

451-463

The paper presents a statistical method of verifying ideological classifications of votes. Parliamentary votes, preclassified by an expert (on a chosen subset), are verified at an assumed significance level by seeking the most likely match with the actual vote results. Classifications that do not meet the requirements defined are rejected. The results obtained can be applied in the ideological dimensioning algorithms, enabling ideological identification of dimensions obtained.