Evidence map›Paper›PMID 37588480›Full record

ReviewDigital biomarkers

Clinical Validation of Novel Digital Measures: Statistical Methods for Reliability Evaluation.

Bohdana Ratitch, Andrew Trigg, Madhurima Majumder, Vanja Vlajnic, Nicole Rethemeier, Richard Nkulikiyinka

Open access · goldAbstract readReview
In one paragraph

Review in Digital biomarkers. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
3.2field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Review
  9. Generative AI - Assisted Adaptive Cancer Therapy.Cancer control : journal of the Moffitt Cancer Center
    Review
  10. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 4 institutions in 4 countries.

Bohdana RatitchStatistics and Data Insights, Bayer Inc., Mississauga, ON, Canada.
Andrew TriggMedical Affairs Statistics, Bayer plc, Reading, UK.
Madhurima MajumderStatistics and Data Insights, Bayer Corporation, Whippany, NJ, USA.
Vanja VlajnicStatistics and Data Insights, Bayer Corporation, Whippany, NJ, USA.
Nicole RethemeierStatistics and Data Insights, Bayer AG, Wuppertal, Germany.
Richard NkulikiyinkaClinical Development and Operations, Bayer AG, Berlin, Germany.
Bayer (Germany) · DEBayer (United States) · USBayer (Canada) · CABayer (United Kingdom) · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Assessment of reliability is one of the key components of the validation process designed to demonstrate that a novel clinical measure assessed by a digital health technology tool is fit-for-purpose in clinical research, care, and decision-making. Reliability assessment contributes to characterization of the signal-to-noise ratio and measurement error and is the first indicator of potential usefulness of the proposed clinical measure. Summary: Methodologies for reliability analyses are scattered across literature on validation of PROs, wet biomarkers, etc., yet are equally useful for digital clinical measures. We review a general modeling framework and statistical metrics typically used for reliability assessments as part of the clinical validation. We also present methods for the assessment of agreement and measurement error, alongside modified approaches for categorical measures. We illustrate the discussed techniques using physical activity data from a wearable device with an accelerometer sensor collected in clinical trial participants. Key Messages: This paper provides statisticians and data scientists, involved in development and validation of novel digital clinical measures, an overview of the statistical methodologies and analytical tools for reliability assessment.

Indexed as

Clinical validationDigital health technologyReliabilityStatistical methods

Identifiers

PMID37588480
PMCPMC10425717
OpenAlexW4385694112

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.