Evidence map›Paper›PMID 25584364›Full record

ArticleInformation security and computer fraud

The DISTANCE model for collaborative research: distributing analytic effort using scrambled data sets.

Howard H Moffet, E Margaret Warton, Melissa M Parker, Jennifer Y Liu, Courtney R Lyles, Andrew J Karter

Open access · diamondAbstract read
In one paragraph

Article in Information security and computer fraud. 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, top 100% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

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

6 authors at 2 institutions in 1 country.

Howard H MoffetDivision of Research, Kaiser Permanente Northern California, 2000 Broadway, Oakland, CA 94612.
E Margaret WartonCenter for Vulnerable Populations, University of California San Francisco, 1001 Potrero Ave., San Francisco, CA 94110.
Melissa M Parker
Jennifer Y Liu
Courtney R Lyles
Andrew J Karter
Kaiser Permanente · USUniversity of California, San Francisco · US

Funding

Translational Research Core - Health Engagement & Action Translational (HEAT)P30DK092924 · NIDDK · KAISER FOUNDATION RESEARCH INSTITUTE · PI Alyce Sophia Adams, HILARY Kessler SELIGMAN · 2011 to 2026
$9.2M
Ethnic Disparities in Diabetes ComplicationsR01DK065664 · NIDDK · KAISER FOUNDATION RESEARCH INSTITUTE · PI KARTER, ANDREW JOHN · 2004 to 2008
$3.3M
Neighborhood Effects on Weight Change and Diabetes Risk FactorsR01DK080744 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI LARAIA, BARBARA A · 2009 to 2013
$3.0M
Diabetes and Aging in a Multi-Ethnic PopulationR01DK081796 · NIDDK · KAISER FOUNDATION RESEARCH INSTITUTE · PI HUANG, ELBERT S., KARTER, ANDREW JOHN · 2009 to 2013
$2.9M
Educational Disparities in Diabetes ComplicationsR01HD046113 · NICHD · KAISER FOUNDATION RESEARCH INSTITUTE · PI KARTER, ANDREW JOHN · 2003 to 2006
$1.6M
Medication Adherence and Social Disparities in DiabetesR01DK080726 · NIDDK · KAISER FOUNDATION RESEARCH INSTITUTE · PI KARTER, ANDREW JOHN · 2009 to 2012
$1.5M
Effective Care of Latinos with Diabetes and Limited English ProficiencyR01DK090272 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI FERNANDEZ, ALICIA · 2011 to 2013
$1.2M
Failure to Utilize Diabetes Health Services Following a ReferralRC1DK086178 · NIDDK · KAISER FOUNDATION RESEARCH INSTITUTE · PI KARTER, ANDREW JOHN · 2009 to 2010
$497k
NICHD NIH HHS R01 HD046113NIDDK NIH HHS P30 DK092924NIDDK NIH HHS R01 DK065664NIDDK NIH HHS R01 DK080726NIDDK NIH HHS R01 DK080744NIDDK NIH HHS R01 DK081796NIDDK NIH HHS R01 DK090272NIDDK NIH HHS RC1 DK086178
6 · The paper itself

Abstract

backgroundData-sharing is encouraged to fulfill the ethical responsibility to transform research data into public health knowledge, but data sharing carries risks of improper disclosure and potential harm from release of individually identifiable data.

methodsThe study objective was to develop and implement a novel method for scientific collaboration and data sharing which distributes the analytic burden while protecting patient privacy. A procedure was developed where in an investigator who is external to an analytic coordinating center (ACC) can conduct original research following a protocol governed by a Publications and Presentations (P&P) Committee. The collaborating investigator submits a study proposal and, if approved, develops the analytic specifications using existing data dictionaries and templates. An original data set is prepared according to the specifications and the external investigator is provided with a complete but de-identified and shuffled data set which retains all key data fields but which obfuscates individually identifiable data and patterns; this" scrambled data set" provides a "sandbox" for the external investigator to develop and test analytic code for analyses. The analytic code is then run against the original data at the ACC to generate output which is used by the external investigator in preparing a manuscript for journal submission.

resultsThe method has been successfully used with collaborators to produce many published papers and conference reports.

conclusionBy distributing the analytic burden, this method can facilitate collaboration and expand analytic capacity, resulting in more science for less money.

Indexed as

cohort studiescollaborationdata sharingde-identificationepidemiologyinformation disseminationprivacy rule

Identifiers

PMID25584364
PMCPMC4288766
OpenAlexW2154027765

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.