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Khader, S.

Publications and source records attributed to Khader, S..

2 recordsLinked to original sources

fragilityindex: An R Package for Statistical Fragility Estimates in Biomedicine

Poor reproducibility is a growing crisis in biomedical research. The fragility index was introduced as a convenient measure to estimate how fragile statistical results in clinical trials are to small perturbations in event outcome counts. There is currently no freely available R package to produce this calculation. Furthermore, the original definition of the method is applicable only to 2x2 contingency tables. As such, we developed an R package to calculate fragility index. We have also extended the concept of a statistical fragility index to two of the most commonly used methods in clinical research, survival analysis via weighted log-rank tests and logistic regression, and implemented these technique sin this R package. We describe example applications of these methods to existing publically available datasets. This R package is freely available under the AGPL license on CRAN (https://cran.r-project.org/web/packages/fragilityindex/index.html). The most recent versions may be downloaded and installed via Github (https://github.com/kippjohnson/fragilityindex).

bioinformatics

Predictive modeling of susceptibility to substance abuse, mortality and drug-drug interactions in opioid patients

Opioid addiction causes high degree of morbidity and mortality. Preemptive identification of patients at risk of opioid dependence and developing intelligent clinical decisions to deprescribe opioids to the vulnerable patient population may help in reducing the burden. Identifying patients susceptible to mortality due to opioid-induced side effects and understanding the landscape of drug-drug interaction pairs aggravating opioid usage are significant, yet, unexplored research questions. In this study, we present a collection of predictive models to identify patients at risk of opioid abuse, mortality and drug-drug interactions in the context of opioid usage. Using publicly available dataset from MIMIC-III, we developed predictive models (opioid abuse models a=Logistic Regression; b=Extreme Gradient Boosting and mortality model= Extreme Gradient Boosting) and identified potential drug-drug interaction patterns. To enable the translational value of our work, the predictive model and all associated software code is provided. This repository could be used to build clinical decision aids and thus improve the optimization of prescription rates for vulnerable population.

epidemiology