Data-driven hypothesis generation among inexperienced clinical researchers: A comparison of secondary data analyses with visualization (VIADS) and other tools

Jing, Xia, James J. Cimino, Vimla L Patel, Yuchun Zhou, Jay H. Shubrook, Brooke N. Draghi, Sonsoles De Lacalle, et al. 2024. “Data-Driven Hypothesis Generation Among Inexperienced Clinical Researchers: A Comparison of Secondary Data Analyses With Visualization (VIADS) and Other Tools”. Clinical and Translational Science.

Abstract

Objectives: To compare how clinical researchers generate data-driven hypotheses with a visualinteractive analytic tool (VIADS, a visual interactive analysis tool for filtering and summarizinglarge data sets coded with hierarchical terminologies) or other tools.

Methods: We recruited clinical researchers and separated them into “experienced” and“inexperienced” groups. Participants were randomly assigned to a VIADS or control groupwithin the groups. Each participant conducted a remote 2-hour study session for hypothesisgeneration with the same study facilitator on the same datasets by following a think-aloudprotocol. Screen activities and audio were recorded, transcribed, coded, and analyzed.Hypotheses were evaluated by seven experts on their validity, significance, and feasibility. Weconducted multilevel random effect modeling for statistical tests.

Results: Eighteen participants generated 227 hypotheses, of which 147 (65%) were valid. TheVIADS and control groups generated a similar number of hypotheses. The VIADS group took asignificantly shorter time to generate one hypothesis (e.g., among inexperienced clinicalresearchers, 258 seconds versus 379 seconds, p = 0.046, power = 0.437, ICC = 0.15). TheVIADS group received significantly lower ratings than the control group on feasibility and thecombination rating of validity, significance, and feasibility.

Conclusion: The role of VIADS in hypothesis generation seems inconclusive. The VIADSgroup took a significantly shorter time to generate each hypothesis. However, the combinedvalidity, significance, and feasibility ratings of their hypotheses were significantly lower. Furthercharacterization of hypotheses, including specifics on how they might be improved, could guidefuture tool development.

Keywords: scientific hypothesis generation; clinical research; VIADS; utility study; secondarydata analysis tools
(PDF) Data-driven hypothesis generation among inexperienced clinical researchers: A comparison of secondary data analyses with visualization (VIADS) and other tools. Available from: https://www.researchgate.net/publication/377170726_Data-driven_hypothesis_generation_among_inexperienced_clinical_researchers_A_comparison_of_secondary_data_analyses_with_visualization_VIADS_and_other_tools [accessed Jan 18 2024].

Last updated on 01/18/2024