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Accepted for/Published in: JMIR Research Protocols

Date Submitted: May 9, 2022
Date Accepted: Jun 21, 2022
Date Submitted to PubMed: Jun 23, 2022

The final, peer-reviewed published version of this preprint can be found here:

The Roles of a Secondary Data Analytics Tool and Experience in Scientific Hypothesis Generation in Clinical Research: Protocol for a Mixed Methods Study

Jing X, Patel VL, Cimino JJ, Shubrook JH, Zhou Y, Liu C, De Lacalle S

The Roles of a Secondary Data Analytics Tool and Experience in Scientific Hypothesis Generation in Clinical Research: Protocol for a Mixed Methods Study

JMIR Res Protoc 2022;11(7):e39414

DOI: 10.2196/39414

PMID: 35736798

PMCID: 9345027

The Roles of a Secondary Data Analytic Tool and Experience in Scientific Hypothesis Generation in Clinical Research: A Study Design

  • Xia Jing; 
  • Vimla L. Patel; 
  • James J. Cimino; 
  • Jay H. Shubrook; 
  • Yuchun Zhou; 
  • Chang Liu; 
  • Sonsoles De Lacalle

ABSTRACT

Background:

Scientific hypothesis generation is a critical step in scientific research that determines the direction and impact of any investigation. Despite its vital role, we have limited knowledge of the process itself, hindering our ability to address some critical questions.

Objective:

To what extent can secondary data analytic tools facilitate scientific hypothesis generation during clinical research? Are the processes similar in developing clinical diagnoses during clinical practice and developing scientific hypotheses for clinical research projects? We explore the process of scientific hypothesis generation in the context of clinical research. The study is designed to compare the role of VIADS, our web-based interactive secondary data analysis tool, and the experience levels of study participants during their scientific hypothesis generation processes.

Methods:

Inexperienced and experienced clinical researchers are recruited. In this 2×2 study design, all participants use the same data sets during scientific hypothesis-generation sessions, following pre-determined scripts. The inexperienced and experienced clinical researchers are randomly assigned into groups with and without using VIADS. The study sessions, screen activities, and audio recordings of participants are captured. Participants use the think-aloud protocol during the study sessions. After each study session, every participant is given a follow-up survey, with participants using VIADS completing an additional modified System Usability Scale (SUS) survey. A panel of clinical research experts will assess the scientific hypotheses generated based on pre-developed metrics. All data will be anonymized, transcribed, aggregated, and analyzed.

Results:

This study is currently underway. Recruitment is ongoing via a brief online survey 1. The preliminary results show that study participants can generate a few to over a dozen scientific hypotheses during a 2-hour study session, regardless of whether they use VIADS or other analytic tools. A metric to assess scientific hypotheses within a clinical research context more accurately, comprehensively, and consistently has also been developed.

Conclusions:

The scientific hypothesis-generation process is an advanced cognitive activity and a complex process. Clinical researchers can quickly generate initial scientific hypotheses based on data sets and prior experience based on our current results. However, refining these scientific hypotheses is much more time-consuming. To uncover the fundamental mechanisms of generating scientific hypotheses, we need breakthroughs that capture thinking processes more precisely. Clinical Trial: N/A


 Citation

Please cite as:

Jing X, Patel VL, Cimino JJ, Shubrook JH, Zhou Y, Liu C, De Lacalle S

The Roles of a Secondary Data Analytics Tool and Experience in Scientific Hypothesis Generation in Clinical Research: Protocol for a Mixed Methods Study

JMIR Res Protoc 2022;11(7):e39414

DOI: 10.2196/39414

PMID: 35736798

PMCID: 9345027

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