Evidence map›Paper›PMID 39938700›Full record

Trial reportJournal of clinical epidemiology2025

Endpoint assessment via routinely collected data generates estimates comparable to randomized controlled trial data: analysis of a cluster-randomized trial on fall injury prevention.

David A Ganz, Erich J Greene, Nancy K Latham, Michael Kane, Lillian C Min, Thomas M Gill, David B Reuben, Peter Peduzzi, Denise Esserman

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Journal of clinical epidemiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

David A GanzDepartment of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; Geriatric Research, Education and Clinical Center, Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, USA; Health Care Division, RAND, Santa Monica, CA, USA. Electronic address: dganz@mednet.ucla.edu.
Erich J GreeneDepartment of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
Nancy K LathamBoston Claude D. Pepper Older Americans Independence Center, Research Program in Men's Health: Aging and Metabolism, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Michael KaneDepartment of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
Lillian C MinDivision of Geriatric and Palliative Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA; Ann Arbor VA Medical Center, Center for Clinical Management Research and Geriatric Research Education Clinical Center (GRECC), Ann Arbor, MI, USA.
Thomas M GillDepartment of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
David B ReubenDepartment of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
Peter PeduzziDepartment of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
Denise EssermanDepartment of Biostatistics, Yale School of Public Health, New Haven, CT, USA.

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Yale Study Support Suite (YES3): Dashboard and Web Portal Software Supporting Research Workflow through integrated, customizable REDCap External ModulesP30AG021342 · NIA · YALE UNIVERSITY · PI Lauren Ferrante · 2002 to 2026
$37.9M
Randomized Trial of a Multifactorial Fall Injury Prevention Strategy-SupplementU01AG048270 · NIA · BRIGHAM AND WOMEN'S HOSPITAL · PI BHASIN, SHALENDER, GILL, THOMAS MICHAEL · 2014 to 2019
$34.8M
Enhancing the Efficiency of Pragmatic Clinical Trials Using Administrative Data: Analysis of the STRIDE StudyR01AG071528 · NIA · YALE UNIVERSITY · PI ESSERMAN, DENISE, GANZ, DAVID · 2022 to 2025
$2.0M
NCATS NIH HHS UL1 TR001863NIA NIH HHS P30 AG021342NIA NIH HHS R01 AG071528NIA NIH HHS U01 AG048270
6 · The paper itself

Abstract

BACKGROUND AND

objectivesRoutinely collected data (RCD) from healthcare claims and encounters are increasingly used for outcomes in randomized trials; however, methods for estimating the validity and relative precision of RCD-derived outcomes compared to those from conventional outcome ascertainment are limited. We developed an approach to measuring validity and relative precision of RCD and quantifying uncertainty.

methodsWe reanalyzed data from the Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) cluster-randomized, controlled trial. Eighty-six primary care practices in 10 US healthcare systems were randomized to either a multifactorial intervention delivered by nurse falls care managers, or enhanced usual care, with 5451 persons age ≥ 70 at increased fall injury risk enrolled in the study. We estimated the hazard ratio (HR) and confidence interval (CI) for STRIDE's primary outcome (time to first serious fall injury) using original study data and RCD. The ratio of the RCD HR to original HR ("ratio of HRs") measured validity. The confidence limit ratio (CLR; upper divided by lower confidence limits of CI) measured precision, with the ratio of the CLR with RCD to the CLR from the original study data ("ratio of CLRs"), measuring relative precision. We estimated uncertainty around the ratio of HRs and ratio of CLRs using bootstrapped 95% CIs and performed sensitivity analyses to assess the effects of adaptations needed to use RCD.

resultsAmong the original sample of 5451 study participants, 5036 (92%) were linked to RCD. The intervention to control HR was 0.91 (95% CI: 0.78-1.07) in RCD, compared to 0.92 (95% CI: 0.80-1.06) in the original data. Using all RCD through STRIDE's administrative end date, the ratio of HRs was 1.00 (95% CI: 0.89-1.11) and ratio of CLRs was 1.03 (95% CI: 0.96-1.06). The CI around ratio of HRs was about three-fold wider for RCD than for the original STRIDE data in individuals who linked to RCD. Relative precision of RCD improved with increased length of follow-up.

conclusionRelying solely on RCD to ascertain the primary outcome in STRIDE would have resulted in similar point estimates and confidence limits for the treatment effect as in the original data. However, there was meaningful uncertainty around the estimate of validity. Efforts to validate RCD-derived outcomes for use as clinical trial endpoints should include measurement of uncertainty around validity estimates.

Indexed as

Accidental FallsEndpoint DeterminationRandomized Controlled Trials as TopicWounds and InjuriesAgedAged, 80 and overFemaleHumansMalePrimary Health CareReproducibility of ResultsUnited StatesCluster randomized trialsFall injuriesMedicare dataPrecisionRoutinely collected dataValidity

Identifiers

PMID39938700
PMCPMC12103980

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Registered trials

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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.