Blindness

Lawrence M. Friedman (), Curt D. Furberg () and David L. DeMets ()
Additional contact information
Curt D. Furberg: Wake Forest University, School of Medicine
David L. DeMets: University of Wisconsin, Department of Biostatistics & Medical Informatics

Chapter Chapter 7 in Fundamentals of Clinical Trials, 2010, pp 119-132 from Springer

Abstract: Abstract In any clinical trial, bias is one of the main concerns. Bias may be defined as systematic error, or “difference between the true value and that actually obtained due to all causes other than sampling variability” [1]. It can be caused by conscious factors, subconscious factors, or both. Bias can occur at a number of places in a clinical trial, from the initial design through data analysis and interpretation. One general solution to the problem of bias is to keep the participant and the investigator blinded, or masked, to the identity of the assigned intervention. One can also blind several other aspects of a trial including the assessment, classification, and evaluation of the response variables. Large sample size does not reduce bias although it generally improves precision and thus power.

Keywords: International Normalize Ratio; Monitoring Committee; International Normalize Ratio Control; Taste Disorder; Oral Direct Thrombin Inhibitor (search for similar items in EconPapers)
Date: 2010
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4419-1586-3_7

Ordering information: This item can be ordered from
http://www.springer.com/9781441915863

DOI: 10.1007/978-1-4419-1586-3_7

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().