Evidence map›Paper›PMID 31578038›Full record

ArticlePediatric research2020

Privacy-protecting multivariable-adjusted distributed regression analysis for multi-center pediatric study.

Sengwee Toh, Sheryl L Rifas-Shiman, Pi-I D Lin, L Charles Bailey, Christopher B Forrest, Casie E Horgan, Douglas Lunsford, Erick Moyneur, Jessica L Sturtevant, Jessica G Young and 2 more

Abstract readComparative StudyMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Pediatric research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Review of Clinical Research Informatics.Yearbook of medical informatics · 2020
    Review
  4. Article
  5. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Sengwee TohTherapeutics Research and Infectious Disease Epidemiology Group, Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA, USA. darren_toh@harvardpilgrim.org.
Sheryl L Rifas-ShimanDivision of Chronic Disease Research Across the Lifecourse (CoRAL), Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA, USA.
Pi-I D LinDivision of Chronic Disease Research Across the Lifecourse (CoRAL), Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA, USA.
L Charles BaileyApplied Clinical Research Center, Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Christopher B ForrestApplied Clinical Research Center, Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Casie E HorganTherapeutics Research and Infectious Disease Epidemiology Group, Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA, USA.
Douglas LunsfordNorth Fork Local School District, Utica, OH, USA.
Erick MoyneurStatLog Econometric Inc., Quebec City, QC, Canada.
Jessica L SturtevantTherapeutics Research and Infectious Disease Epidemiology Group, Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA, USA.
Jessica G YoungTherapeutics Research and Infectious Disease Epidemiology Group, Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA, USA.
Jason P BlockDivision of Chronic Disease Research Across the Lifecourse (CoRAL), Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA, USA.
PCORnet Antibiotics and Childhood Growth Study Group

Funding

Patient-Centered Outcomes Research Institute OBS-1505-30699
6 · The paper itself

Abstract

backgroundPrivacy-protecting analytic approaches without centralized pooling of individual-level data, such as distributed regression, are particularly important for vulnerable populations, such as children, but these methods have not yet been tested in multi-center pediatric studies.

methodsUsing the electronic health data from 34 healthcare institutions in the National Patient-Centered Clinical Research Network (PCORnet), we fit 12 multivariable-adjusted linear regression models to assess the associations of antibiotic use <24 months of age with body mass index z-score at 48 to <72 months of age. We ran these models using pooled individual-level data and conventional multivariable-adjusted regression (reference method), as well as using the more privacy-protecting pooled summary-level intermediate statistics and distributed regression technique. We compared the results from these two methods.

resultsPooled individual-level and distributed linear regression analyses produced virtually identical parameter estimates and standard errors. Across all 12 models, the maximum difference in any of the parameter estimates or standard errors was 4.4833 × 10

conclusionsWe demonstrated empirically the feasibility and validity of distributed linear regression analysis using only summary-level information within a large multi-center study of children. This approach could enable expanded opportunities for multi-center pediatric research, especially when sharing of granular individual-level data is challenging.

Indexed as

Computer SecurityConfidentialityElectronic Health RecordsInformation DisseminationPrivacyAge FactorsAnti-Bacterial AgentsBody Mass IndexChild, PreschoolDatabases, FactualFeasibility StudiesFemaleHumansInfantInfant, NewbornLinear ModelsAnti-Bacterial Agents

Identifiers

PMID31578038
PMCPMC7113085

What OpenQuestion holds

Textmetadata
LicenceTDM
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.