Abstract: Statistical graphics play an important role in exploratory data analysis, model checking and diagnostics. The lineup protocol (Buja et. al 2009) enables statistical significance testing using visualizations, bridging the gap between exploratory and inferential statistics. We created an R-shiny App that facilitates the user to generate these lineups by using preloaded examples or by uploading their own data. The user can then act as a human judge to select the plot which he/she think has the real data and see if a correct choice is made. If a correct choice is made, it would be enough evidence to believe that the real plot is significantly different from the "null" plots. The app also calculates the "see"-value based on the selections made by multiple independent users which can be used to decide statistical significance. The app supports different types of analysis using continuous, binary or time-to-event response and continuous or categorical predictors.
Speaker: Hannah Diehl, Andy Stein, Niladri Roy Chowdhury and Tamara Broderick
Affiliation: MIT | Novartis
Title: The See Value App: Visual Decision Making for Drug Development
Abstract_Sanitzed: Statistical graphics play an important role in exploratory data analysis, model checking and diagnostics. The lineup protocol (Buja et. al 2009) enables statistical significance testing using visualizations, bridging the gap between exploratory and inferential statistics. We created an R-shiny App that facilitates the user to generate these lineups by using preloaded examples or by uploading their own data. The user can then act as a human judge to select the plot which he/she think has the real data and see if a correct choice is made. If a correct choice is made, it would be enough evidence to believe that the real plot is significantly different from the "null" plots. The app also calculates the "see"-value based on the selections made by multiple independent users which can be used to decide statistical significance. The app supports different types of analysis using continuous, binary or time-to-event response and continuous or categorical predictors.