Leveraging vibration of effects analysis for robust discovery in observational biomedical data science. Academic Article uri icon

Overview

abstract

  • 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). VoE is defined by variation in associations that often lead to contradictory results. Here, we present a computational tool capable of modeling VoE 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 associated with 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). We leveraged our tool for potential confounder identification, investigating what adjusting variables are responsible for conflicting models. 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.

authors

  • Tierney, Braden T
  • Anderson, Elizabeth
  • Tan, Yingxuan
  • Claypool, Kajal
  • Tangirala, Sivateja
  • Kostic, Aleksandar D
  • Manrai, Arjun K
  • Patel, Chirag J

publication date

  • September 23, 2021

Research

keywords

  • Data Science
  • Models, Statistical
  • Observational Studies as Topic

Identity

PubMed Central ID

  • PMC8510627

Scopus Document Identifier

  • 85116016211

Digital Object Identifier (DOI)

  • 10.3390/jcdd6020019

PubMed ID

  • 34555021

Additional Document Info

volume

  • 19

issue

  • 9