Imputation methods for missing outcome data in meta-analysis of clinical trials

Julian P T Higgins; Ian R White; Angela M Wood

doi:10.1177/1740774508091600

Imputation methods for missing outcome data in meta-analysis of clinical trials

Julian P T Higgins, Ian R White, Angela M Wood

Bristol Medical School (PHS)

Research output: Contribution to journal › Article (Academic Journal) › peer-review

269 Citations (Scopus)

Abstract

Missing outcome data from randomized trials lead to greater uncertainty and possible bias in estimating the effect of an experimental treatment. An intention-to-treat analysis should take account of all randomized participants even if they have missing observations.

Original language	English
Pages (from-to)	225-39
Number of pages	15
Journal	Clinical Trials
Volume	5
Issue number	3
DOIs	https://doi.org/10.1177/1740774508091600
Publication status	Published - 2008

Keywords

Schizophrenia
Haloperidol
Uncertainty
Randomized Controlled Trials as Topic
Humans
Outcome Assessment (Health Care)
Models, Statistical
Research Design
Meta-Analysis as Topic

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title = "Imputation methods for missing outcome data in meta-analysis of clinical trials",

abstract = "Missing outcome data from randomized trials lead to greater uncertainty and possible bias in estimating the effect of an experimental treatment. An intention-to-treat analysis should take account of all randomized participants even if they have missing observations.",

keywords = "Schizophrenia, Haloperidol, Uncertainty, Randomized Controlled Trials as Topic, Humans, Outcome Assessment (Health Care), Models, Statistical, Research Design, Meta-Analysis as Topic",

author = "Higgins, \{Julian P T\} and White, \{Ian R\} and Wood, \{Angela M\}",

year = "2008",

doi = "10.1177/1740774508091600",

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T1 - Imputation methods for missing outcome data in meta-analysis of clinical trials

AU - Higgins, Julian P T

AU - White, Ian R

AU - Wood, Angela M

PY - 2008

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N2 - Missing outcome data from randomized trials lead to greater uncertainty and possible bias in estimating the effect of an experimental treatment. An intention-to-treat analysis should take account of all randomized participants even if they have missing observations.

AB - Missing outcome data from randomized trials lead to greater uncertainty and possible bias in estimating the effect of an experimental treatment. An intention-to-treat analysis should take account of all randomized participants even if they have missing observations.

KW - Schizophrenia

KW - Haloperidol

KW - Uncertainty

KW - Randomized Controlled Trials as Topic

KW - Humans

KW - Outcome Assessment (Health Care)

KW - Models, Statistical

KW - Research Design

KW - Meta-Analysis as Topic

U2 - 10.1177/1740774508091600

DO - 10.1177/1740774508091600

M3 - Article (Academic Journal)

C2 - 18559412

SN - 1740-7745

VL - 5

SP - 225

EP - 239

JO - Clinical Trials

JF - Clinical Trials

IS - 3

ER -

Imputation methods for missing outcome data in meta-analysis of clinical trials

Abstract

Keywords

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