Arbeitspapier
"Randomisation bias" in the medical literature: A review
Randomised controlled or clinical trials (RCTs) are generally viewed as the most reliable method to draw causal inference as to the effects of a treatment, as they should guarantee that the individuals being compared differ only in terms of their exposure to the treatment of interest. This 'gold standard' result however hinges on the requirement that the randomisation device determines the random allocation of individuals to the treatment without affecting any other element of the causal model. This 'no randomisation bias' assumption is generally untestable but if violated would undermine the causal inference emerging from an RCT, both in terms of its internal validity and in terms of its relevance for policy purposes. This paper offers a concise review of how the medical literature identifies and deals with such issues.
- Sprache
-
Englisch
- Erschienen in
-
Series: IFS Working Papers ; No. W16/23
- Klassifikation
-
Wirtschaft
Methodological Issues: General
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Design of Experiments: General
- Thema
-
clinical trials
social experiments
design of experiments
randomisation bias
sample selection
causal inference
treatment effects
external validity
generalizability
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Sianesi, Barbara
- Ereignis
-
Veröffentlichung
- (wer)
-
Institute for Fiscal Studies (IFS)
- (wo)
-
London
- (wann)
-
2016
- DOI
-
doi:10.1920/wp.ifs.2016.1623
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Sianesi, Barbara
- Institute for Fiscal Studies (IFS)
Entstanden
- 2016
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