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Methods for Analytical Validation of Novel Digital Clinical Measures: Implementation Feasibility Evaluation Using

Simon Turner1, Lysbeth Floden2, Leif Simmatis3

  • 1Digital Medicine Society, Boston, MA, United States.

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PubMed
Summary
This summary is machine-generated.

Sensor-based digital health technologies (sDHTs) generate digital measures (DMs) for drug development. Confirmatory factor analysis (CFA) effectively validates novel DMs against reference measures, guiding study design for reliable results.

Keywords:
analytical validationconfirmatory factor analysisdigital health technologiesdigital medicinenovel digital clinical measures

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Area of Science:

  • Biostatistics
  • Digital Health Technologies
  • Drug Development

Background:

  • Sensor-based digital health technologies (sDHTs) generate digital measures (DMs) crucial for scientific and clinical decisions.
  • DMs can accelerate drug development, reduce trial costs, and improve care access.
  • Analytical validation (AV) of novel DMs is challenging due to the lack of established reference measures (RMs).

Purpose of the Study:

  • To assess the feasibility of implementing statistical methods for analytical validation (AV) using real-world data.
  • To examine how study design factors impact the estimation of relationships between DMs and RMs.
  • To provide guidance on standardizing AV for novel DMs.

Main Methods:

  • Utilized four real-world datasets (Urban Poor, STAGES, mPower, Brighten) to simulate AV studies.
  • Assessed study design properties: temporal coherence, construct coherence, and data completeness.
  • Compared statistical methods: Pearson correlation coefficient (PCC), simple linear regression (SLR), multiple linear regression (MLR), and confirmatory factor analysis (CFA).

Main Results:

  • Confirmatory factor analysis (CFA) models generally showed acceptable fit and estimated factor correlations.
  • CFA-derived factor correlations were consistently stronger than Pearson correlation coefficients (PCC).
  • Strongest correlations were observed in hypothetical studies with high temporal and construct coherence.

Conclusions:

  • The evaluated statistical methods are feasible for real-world data in AV of DMs.
  • Confirmatory factor analysis (CFA) is recommended for assessing novel DM-RM relationships.
  • Findings offer practical recommendations for AV study design, promoting standardized validation of sDHTs.