These are the model outputs for the manuscript describing our software tool, quantvoe. The abstract is reproduced below:
Hypothesis generation in observational, biomedical data science often starts with computing an association, or identifying the statistical relationship between a dependent and an independent variable. However, the outcome of this process depends fundamentally on modeling strategy, with differing strategies generating what can be called “Vibration of Effects” (VoE), which is defined by variation in, and even conflicting, results. We present a computational tool capable of modeling vibration of effects in biomedical data by fitting millions of different models and comparing their output. We execute a VoE analysis on a series of widely reported associations (e.g. carrot intake and eyesight) with an extended additional focus on lifestyle exposures (e.g. physical activity) and components of the Framingham Risk Score for cardiovascular health (e.g. blood pressure). Of the 44 distinct relationships we queried, 77.3% reported greater than 5% of all models generating conflicting (i.e. positive vs. negative) results. We leverage our tool to holistically investigate the adjusting variables that engendered these changes in association direction. We propose modeling VoE as a critical step in navigating discovery in observational data, discerning robust associations, and cataloging adjusting variables that impact model output.