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Ophthalmic statistics note 8: missing data-exploring the unknown

Ophthalmic statistics note 8: missing data-exploring the unknown
Ophthalmic statistics note 8: missing data-exploring the unknown
Medical research is conducted to answer uncertainties and to identify effective treatments for patients. Different questions are best addressed by different types of study design—but the randomised, controlled clinical trial is typically viewed as the gold standard, providing a very high level of evidence, when examining efficacy.1 While clinical trial methodology has advanced considerably with clear guidance provided as to how to avoid sources of bias, even the most robustly designed study can succumb to missing data.2 ,3 In this statistics note, we discuss strategies for dealing with missing data but what we hope emerges is a very clear message that there is no ideal solution to missing data and prevention is the best strategy.
0007-1161
291-294
Bunce, Catey
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Quartilho, Ana
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Freemantle, Nick
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Doré, Caroline J.
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Cipriani, Valentina
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Crabb, David
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Cumberland, Philippa
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Czanner, Gabriela
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Elders, Andrew
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Finana, Marta Garcia
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Nash, Rachel
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Saunders, Luke
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Rogers, Chris
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Skene, Simon
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Stephenson, John
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Stratton, Irene
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Xing, Wen
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on behalf of the Ophthalmic Statistics Group
Bunce, Catey
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Quartilho, Ana
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Freemantle, Nick
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Doré, Caroline J.
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Cipriani, Valentina
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Crabb, David
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Cumberland, Philippa
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Czanner, Gabriela
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Elders, Andrew
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Finana, Marta Garcia
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Nash, Rachel
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Saunders, Luke
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Rogers, Chris
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Skene, Simon
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Stephenson, John
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Stratton, Irene
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Xing, Wen
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Bunce, Catey, Quartilho, Ana, Freemantle, Nick and Doré, Caroline J. , on behalf of the Ophthalmic Statistics Group (2016) Ophthalmic statistics note 8: missing data-exploring the unknown. British Journal of Ophthalmology, 100 (3), 291-294. (doi:10.1136/bjophthalmol-2015-307821).

Record type: Editorial

Abstract

Medical research is conducted to answer uncertainties and to identify effective treatments for patients. Different questions are best addressed by different types of study design—but the randomised, controlled clinical trial is typically viewed as the gold standard, providing a very high level of evidence, when examining efficacy.1 While clinical trial methodology has advanced considerably with clear guidance provided as to how to avoid sources of bias, even the most robustly designed study can succumb to missing data.2 ,3 In this statistics note, we discuss strategies for dealing with missing data but what we hope emerges is a very clear message that there is no ideal solution to missing data and prevention is the best strategy.

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e-pub ahead of print date: 30 December 2015
Published date: 23 February 2016
Additional Information: Funding Information: The posts of CB and AQ are partly funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology.

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Local EPrints ID: 486881
URI: http://eprints.soton.ac.uk/id/eprint/486881
ISSN: 0007-1161
PURE UUID: 2daf28f0-b00c-47da-94a5-f80a703f5fa8
ORCID for Irene Stratton: ORCID iD orcid.org/0000-0003-1172-7865

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Date deposited: 08 Feb 2024 17:31
Last modified: 18 Mar 2024 04:01

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Contributors

Author: Catey Bunce
Author: Ana Quartilho
Author: Nick Freemantle
Author: Caroline J. Doré
Author: Valentina Cipriani
Author: David Crabb
Author: Philippa Cumberland
Author: Gabriela Czanner
Author: Andrew Elders
Author: Marta Garcia Finana
Author: Rachel Nash
Author: Luke Saunders
Author: Chris Rogers
Author: Simon Skene
Author: John Stephenson
Author: Irene Stratton ORCID iD
Author: Wen Xing
Corporate Author: on behalf of the Ophthalmic Statistics Group

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