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Vera Ehrenstein

Clinical epidemiology in the era of big data: new opportunities, familiar challenges

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Clinical epidemiology in the era of big data : new opportunities, familiar challenges. / Ehrenstein, Vera; Nielsen, Henrik; Pedersen, Alma Becic; Johnsen, Søren Paaske; Pedersen, Lars.

In: Clinical epidemiology, Vol. 9, 2017, p. 245-250.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

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Ehrenstein, Vera ; Nielsen, Henrik ; Pedersen, Alma Becic ; Johnsen, Søren Paaske ; Pedersen, Lars. / Clinical epidemiology in the era of big data : new opportunities, familiar challenges. In: Clinical epidemiology. 2017 ; Vol. 9. pp. 245-250.

Bibtex

@article{52c66e7911e24b01b5a57a5ae364c678,
title = "Clinical epidemiology in the era of big data: new opportunities, familiar challenges",
abstract = "Routinely recorded health data have evolved from mere by-products of health care delivery or billing into a powerful research tool for studying and improving patient care through clinical epidemiologic research. Big data in the context of epidemiologic research means large interlinkable data sets within a single country or networks of multinational databases. Several Nordic, European, and other multinational collaborations are now well established. Advantages of big data for clinical epidemiology include improved precision of estimates, which is especially important for reassuring ({"}null{"}) findings; ability to conduct meaningful analyses in subgroup of patients; and rapid detection of safety signals. Big data will also provide new possibilities for research by enabling access to linked information from biobanks, electronic medical records, patient-reported outcome measures, automatic and semiautomatic electronic monitoring devices, and social media. The sheer amount of data, however, does not eliminate and may even amplify systematic error. Therefore, methodologies addressing systematic error, clinical knowledge, and underlying hypotheses are more important than ever to ensure that the signal is discernable behind the noise.",
keywords = "Journal Article",
author = "Vera Ehrenstein and Henrik Nielsen and Pedersen, {Alma Becic} and Johnsen, {S{\o}ren Paaske} and Lars Pedersen",
year = "2017",
doi = "10.2147/CLEP.S129779",
language = "English",
volume = "9",
pages = "245--250",
journal = "Clinical Epidemiology",
issn = "1179-1349",
publisher = "Dove Medical Press Ltd.(Dovepress)",

}

RIS

TY - JOUR

T1 - Clinical epidemiology in the era of big data

T2 - new opportunities, familiar challenges

AU - Ehrenstein, Vera

AU - Nielsen, Henrik

AU - Pedersen, Alma Becic

AU - Johnsen, Søren Paaske

AU - Pedersen, Lars

PY - 2017

Y1 - 2017

N2 - Routinely recorded health data have evolved from mere by-products of health care delivery or billing into a powerful research tool for studying and improving patient care through clinical epidemiologic research. Big data in the context of epidemiologic research means large interlinkable data sets within a single country or networks of multinational databases. Several Nordic, European, and other multinational collaborations are now well established. Advantages of big data for clinical epidemiology include improved precision of estimates, which is especially important for reassuring ("null") findings; ability to conduct meaningful analyses in subgroup of patients; and rapid detection of safety signals. Big data will also provide new possibilities for research by enabling access to linked information from biobanks, electronic medical records, patient-reported outcome measures, automatic and semiautomatic electronic monitoring devices, and social media. The sheer amount of data, however, does not eliminate and may even amplify systematic error. Therefore, methodologies addressing systematic error, clinical knowledge, and underlying hypotheses are more important than ever to ensure that the signal is discernable behind the noise.

AB - Routinely recorded health data have evolved from mere by-products of health care delivery or billing into a powerful research tool for studying and improving patient care through clinical epidemiologic research. Big data in the context of epidemiologic research means large interlinkable data sets within a single country or networks of multinational databases. Several Nordic, European, and other multinational collaborations are now well established. Advantages of big data for clinical epidemiology include improved precision of estimates, which is especially important for reassuring ("null") findings; ability to conduct meaningful analyses in subgroup of patients; and rapid detection of safety signals. Big data will also provide new possibilities for research by enabling access to linked information from biobanks, electronic medical records, patient-reported outcome measures, automatic and semiautomatic electronic monitoring devices, and social media. The sheer amount of data, however, does not eliminate and may even amplify systematic error. Therefore, methodologies addressing systematic error, clinical knowledge, and underlying hypotheses are more important than ever to ensure that the signal is discernable behind the noise.

KW - Journal Article

U2 - 10.2147/CLEP.S129779

DO - 10.2147/CLEP.S129779

M3 - Journal article

VL - 9

SP - 245

EP - 250

JO - Clinical Epidemiology

JF - Clinical Epidemiology

SN - 1179-1349

ER -