Simpson's paradox - aggregating and partitioning populations in health disparities of lung cancer patients

In this study, we use our original study data of EGFR pathway genetics in African American NSCLC as an example to illustrate that univariate analyses based on aggregation versus partition of data leads to contradictory results, in order to emphasize the importance of controlling statistical confounding. We further investigate analytic approaches in logistic regression for data with separation, as is the case in our example data set, and apply appropriate methods to identify predictors of EGFR mutation. Our simulation shows that with separated or nearly separated data, penalized maximum likelihood (PML) produces estimates with smallest bias and approximately maintains the nominal value with statistical power equal to or better than that from maximum likelihood and exact conditional likelihood methods. Application of the PML method in our example data set shows that race and EGFR-FISH are independently significant predictors of EGFR mutation.

Source: Statistical Methods in Medical Research - November 26, 2015 Category: Statistics Authors: Fu, P., Panneerselvam, A., Clifford, B., Dowlati, A., Ma, P., Zeng, G., Halmos, B., Leidner, R. Tags: Articles Source Type: research