In the safety analysis of clinical trials, the forest plot plays an important role. Currently, most of the forest plots are static, which makes them non-reader-friendly to Data Monitoring Committee (DMC). In this project, we propose an R package - forestly - to realize interactive forest plots. There are five fancy interactive features. First, users can apply filters to filter out the adverse event (AE) category of interest, for example, the serious AE, drug-related AE, AE with toxicity grade 3-5, etc... Second, the filtered AE summary tables can be downloaded as .rft files. Third, subject-level details can be drilled down by clicking the downward triangle bottom. Forth, labels can be revealed by hovering the mouse over a point. Fifth, search bars are embedded for users to quickly find an AE of interest.
Yujie Zhao | Xuan Deng | Benjamin Wang | Yilong Zhang
Merck | Merck | Merck | Merck
Interactive Forest Plot for DMC Safety Monitoring in Clinical Trials
In the safety analysis of clinical trials, the forest plot plays an important role. Currently, most of the forest plots are static, which makes them non-reader-friendly to Data Monitoring Committee (DMC). In this project, we propose an R package - forestly - to realize interactive forest plots. There are five fancy interactive features. First, users can apply filters to filter out the adverse event (AE) category of interest, for example, the serious AE, drug-related AE, AE with toxicity grade 3-5, etc... Second, the filtered AE summary tables can be downloaded as .rft files. Third, subject-level details can be drilled down by clicking the downward triangle bottom. Forth, labels can be revealed by hovering the mouse over a point. Fifth, search bars are embedded for users to quickly find an AE of interest.