Post on 08-Aug-2020
REGISTER UND ROUTINEDATEN FÜR BESSERE FORSCHUNG UND PATIENTENVERSORGUNG
Lars G. HemkensFreiburg, 1. Februar 2019
online version
RegisterRoutinedaten
Register-StudienRoutinedaten-Studien
Studien-Register
?
Routinedaten...Daten, die nicht unmittelbar für eine konkrete Forschungsfrage gesammelt werden
Routinedaten
Register„eine systematische Sammlung von Informationen über eine Gruppe von Objekten,häufig, aber nicht immer sprachlich synonym gebraucht mit
Verzeichnis“
https://de.wikipedia.org/wiki/Register
Wissenstransfer
Zuverlässigere Evidenz
Wissenstransfer
Zuverlässigere Evidenz
Wissenstransfer
Zuverlässigere Evidenz
Studien-Register
“...there are known knowns; there are things we know we know.
We also know there are known unknowns; that is to say we know there are somethings we do not know.
But there are also unknown unknowns—theones we don't know we don't know. “
Donald Rumsfeld, 12 February 2002; Defense.gov News Transcript: DoD News Briefing – Secretary Rumsfeld and Gen. Myers, United States Department of Defense (defense.gov)". http://archive.defense.gov/Transcripts/Transcript.aspx?TranscriptID=2636
Wissenstransfer oder Biastransfer
The cumulative effect of reporting and citation biases on the apparent efficacy of treatments: the case of depressionde Vries YA et al. Psychol Med. 2018 Nov;48(15):2453-2455. URL: https://doi.org/10.1017/S0033291718001873 CC-BY 4.0: http://creativecommons.org/licenses/by/4.0/ No changes were made
Wissenstransfer oder Biastransfer
The cumulative effect of reporting and citation biases on the apparent efficacy of treatments: the case of depressionde Vries YA et al. Psychol Med. 2018 Nov;48(15):2453-2455. URL: https://doi.org/10.1017/S0033291718001873 CC-BY 4.0: http://creativecommons.org/licenses/by/4.0/ No changes were made
Register-Studien
BIG DATAROUTINELY COLLECTED DATA
Elektronische Krankenakten Versicherungsdaten
RegisterMicrosoft
BingGoogleTwitter
AmazonFacebookWhatsapp
NetflixLinkedIn
WearablesFitbit
Biobanken / GenomicsIoT (Internet of Things)
Nationwide Big Data
From www.tylervigen.com/spurious-correlationsThanks to Tyler Vigen for sharing (accessed 8 March 2017)
Nationwide Big Data
From www.tylervigen.com/spurious-correlationsThanks to Tyler Vigen for sharing (accessed 8 March 2017)
CONFOUNDING
Byberg et al. Clinical and Translational Allergy 2016;6:33; Figure S1URL: https://ctajournal.biomedcentral.com/articles/10.1186/s13601-016-0124-9CC-BY 4.0: http://creativecommons.org/licenses/by/4.0/ No changes were made
Confounder BeziehungenKörperliche Aktivität und Atopie bei Kindern
§ Väterliche Faktoren?§ Genom? Ethnie? Herkunft?§ Resp. Infektionen?§ Umwelt?§ Sozioökonomie?§ Ernährung?§ Mikrobiom?
Hemkens et al. 2018
§ 57% diskutieren Confounding§ 3% schränken Schlussfolgerungen
in irgendeiner Form ein
16 klinische Fragen / 16 Routinedaten-Studien36 spätere RCTs 17'275 Patienten835 TodesfällePropensity ScoresMortalität
Hemkens et al. BMJ 2016;352:i493URL: http://www.bmj.com/content/bmj/352/bmj.i493.full.pdfCC-BY 4.0: http://creativecommons.org/licenses/by/4.0/ No changes were made
RCD Studien finden 31% grössere Vorteile als spätere RCTs
Hemkens et al. BMJ 2016;352:i493URL: http://www.bmj.com/content/bmj/352/bmj.i493.full.pdfCC-BY 4.0: http://creativecommons.org/licenses/by/4.0/ No changes were made
68.5% (231 von 337) Routinedaten-Studienuntersuchen Behandlungen, die bereits in RCTs untersucht wurden
Hemkens et al. CMAJ Open 2016
RCTs zur selben Fragestellung wärenschwierig:unethisch:
Hemkens et al. CMAJ Open 2016
RCTs zur selben Fragestellung wärenschwierig: 5.4% (18/337)unethisch: 1.8% (6/337)
Hemkens et al. CMAJ Open 2016
We need less research, better research, and research done for
the right reasons
Douglas G AltmanThe scandal of poor medical researchBMJ 1994; 308
Safeguards of clinical trial research often lacking
ProtokolleRegistrierungForschungsnetzwerkeProspektive Analysen...
Safeguards of clinical trial research often lacking
ProtokolleRegistrierungForschungsnetzwerkeProspektive Analysen...
…
RANDOMIZED REAL WORLD EVIDENCE
RANDOMISIERTE ROUTINEDATEN-STUDIEN
?Non-Randomized
vs.Randomized
RCD Active
Learning from Winners
41 SHADES OF BLUE
„..a team at Googlecouldn’t decide between two blues,
so they’re testing41 shades between each blue
to see which one performs better... I can’t operate in an environment like that.“
Douglas Bowman, Google’s previous visual design leader left Googlehttps://stopdesign.com/archive/2009/03/20/goodbye-google.html
Today, Microsoft and several other leading companies
including Amazon, Booking.com, Facebook, and Google each conduct
more than 10000 online controlled experiments
annually…
Kohavi R, Thomke HS. Havard Buisness Review 2017
“Only one third of the ideas tested at Microsoft improved the metric(s) they
were designed to improve”
Kohavi, R. et al. 2013 Online Controlled Experiments at Large Scale. Retrieved from http://bit.ly/ExPScale
Learning Health Care System
RCD for RCT
RCD for RCTPatientenrekrutierung§ Datenbanken/Register durchsuchen§ Machbarkeit besser abschätzen§ Weniger abgebrochene RCTs
Mehr RCTs
RCD for RCTLängere Studien§ Langzeit Nutzen und Schaden§ Erweiterung klassischer Studien
durch Register
Mehr Ergebnisse
RCD for RCTEndpunktmessungen§ Liegedauer§ Krankenhausaufenthalte§ Adverse events§ Komplikationen§ ICU stay/Intensivstation§ Re-hospitalisierung§ Pflegestatus§ Kosten
Relevante Ergebnisse
RCD for RCTInterventionen§ EHR-basierte Diagnose tool§ Machine Learning / KI§ Alarm Systeme§ Verordnungsfeedback
Learning healthcare
RCD for RCTDemokratisierung der Forschung§ Weniger Kosten§ Relevante Forschungsagenda
Nützliche Evidenz
Zukunft
“Loss of significant benefits of an intervention were seen in 12 analyses (7.7%).Statistically significant harms were seen in 16 trial extension analyses (10.3%)…… in 14 of these (87.5%), the harms were significant only in the trial extension phase.”
Fitzpatrick T et al. JAMA Netw Open. 2018 Dec 7;1(8):e186019. doi: 10.1001/jamanetworkopen.2018.6019. CC-BY License. © 2018 Fitzpatrick T et al. JAMA Network Open.
“Sometimes when you innovate, you make mistakes.
It is best to admit them quickly, and get on with improving your
other innovations“Steve Jobs
JPA Ioannidis (U Stanford)D Contopoulus-Ioannidis (U Stanford)R Salman (U Edinburgh)H Ewald (U Basel)KA Mc Cord (U Basel)M Briel (U Basel)C Pauli-Magnus (U Basel)
B Kasenda (U Basel)HC Bucher (U Basel)TV Pereira (U Toronto)F Naudet (METRICS / INSERM)B Speich (U Oxford)R Kohavi (Microsoft)
Special Thanks
Lars.Hemkens@usb.ch
@LGHemkens