School‐based alcohol education: results of a cluster‐randomized controlled trial

Morgenstern, Matthis; Wiborg, Gudrun; Isensee, Barbara; Hanewinkel, Reiner

doi:10.1111/j.1360-0443.2008.02471.x

Cited by 32 publications

(25 citation statements)

References 37 publications

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“…Applications of imputation by chained equations have now appeared in quite diverse fields: addiction (Schnoll et al 2006;MacLeod et al 2008;Adamczyk and Palmer 2008;Caria et al 2009;Morgenstern et al 2009), arthritis and rheumatology (Wolfe et al 2006;Rahman et al 2008;van den Hout et al 2009), atherosclerosis (Tiemeier et al 2004;van Oijen et al 2007;McClelland et al 2008), cardiovascular system (Ambler et al 2005;van Buuren et al 2006a;Chase et al 2008;Byrne et al 2009;Klein et al 2009), cancer (Clark et al 2001Royston et al 2004;Barosi et al 2007;Fernandes et al 2008;Sharma et al 2008;McCaul et al 2008;Huo et al 2008;Gerestein et al 2009), epidemiology (Cummings et al 2006;Hindorff et al 2008;Mueller et al 2008;Ton et al 2009), endocrinology (Rouxel et al 2004;Prompers et al 2008), infectious diseases (Cottrell et al 2005;Walker et al 2006;Cottrell et al 2007;Kekitiinwa et al 2008;Nash et al 2008;Sabin et al 2008;Thein et al 2008;Garabed et al 2008;Michel et al 2009), genetics …”

Section: Applications Of Chained Equationsmentioning

confidence: 99%

mice: Multivariate Imputation by Chained Equations inR

Buuren¹,

2011

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The R package mice imputes incomplete multivariate data by chained equations. The software mice 1.0 appeared in the year 2000 as an S-PLUS library, and in 2001 as an R package. mice 1.0 introduced predictor selection, passive imputation and automatic pooling. This article documents mice 2.9, which extends the functionality of mice 1.0 in several ways. In mice 2.9, the analysis of imputed data is made completely general, whereas the range of models under which pooling works is substantially extended. mice 2.9 adds new functionality for imputing multilevel data, automatic predictor selection, data handling, post-processing imputed values, specialized pooling routines, model selection tools, and diagnostic graphs. Imputation of categorical data is improved in order to bypass problems caused by perfect prediction. Special attention is paid to transformations, sum scores, indices and interactions using passive imputation, and to the proper setup of the predictor matrix. mice 2.9 can be downloaded from the Comprehensive R Archive Network. This article provides a hands-on, stepwise approach to solve applied incomplete data problems.

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Section: Applications Of Chained Equationsmentioning

confidence: 99%

mice: Multivariate Imputation by Chained Equations inR

Buuren¹,

2011

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“…[22][23][24][25][26][27] Similarly, 25 of the 39 trials that evaluated generic programmes did not find significant positive effects, whereas the remaining 14 studies 28-41 reported significantly beneficial effects of the programmes with regards to some of the measures of alcohol use.…”

Section: Peer Reviewmentioning

confidence: 98%

Universal alcohol misuse prevention programmes for children and adolescents: Cochrane systematic reviews

Foxcroft

Tsertsvadze

2012

Perspect Public Health

127

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In these Cochrane reviews, some school, family or multi-component prevention programmes were shown to be effective in reducing alcohol misuse in youths. However, these results warrant a cautious interpretation, since bias and/or contextual factors may have affected the trial results. Further research should replicate the most promising studies identified in these reviews and pay particular attention to content and context factors through rigorous evaluation.

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“…van Buuren and Groothuis-Oudshoorn [7] discuss the wide range of medical fields that have used chained equations imputation (e.g. addiction [8], epidemiology [9], infectious diseases [10], genetics [11], cancer [12], obesity and physical activity [13]), and a brief review of available software that have implemented chained equations imputation is given by [14]. Given the popularity of chained equations, among users of varying degrees of expertise, there is now guidance in its practical use (e.g.…”

Section: Introductionmentioning

confidence: 99%

Joint modelling rationale for chained equations

Hughes

White

Seaman

et al. 2014

BMC Med Res Methodol

115

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BackgroundChained equations imputation is widely used in medical research. It uses a set of conditional models, so is more flexible than joint modelling imputation for the imputation of different types of variables (e.g. binary, ordinal or unordered categorical). However, chained equations imputation does not correspond to drawing from a joint distribution when the conditional models are incompatible. Concurrently with our work, other authors have shown the equivalence of the two imputation methods in finite samples.MethodsTaking a different approach, we prove, in finite samples, sufficient conditions for chained equations and joint modelling to yield imputations from the same predictive distribution. Further, we apply this proof in four specific cases and conduct a simulation study which explores the consequences when the conditional models are compatible but the conditions otherwise are not satisfied.ResultsWe provide an additional “non-informative margins” condition which, together with compatibility, is sufficient. We show that the non-informative margins condition is not satisfied, despite compatible conditional models, in a situation as simple as two continuous variables and one binary variable. Our simulation study demonstrates that as a consequence of this violation order effects can occur; that is, systematic differences depending upon the ordering of the variables in the chained equations algorithm. However, the order effects appear to be small, especially when associations between variables are weak.ConclusionsSince chained equations is typically used in medical research for datasets with different types of variables, researchers must be aware that order effects are likely to be ubiquitous, but our results suggest they may be small enough to be negligible.

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School‐based alcohol education: results of a cluster‐randomized controlled trial

Abstract: The results indicate that this brief school-based intervention had a small short-term preventive effect on alcohol misuse.

Cited by 32 publications

References 37 publications

mice: Multivariate Imputation by Chained Equations inR

mice: Multivariate Imputation by Chained Equations inR

Universal alcohol misuse prevention programmes for children and adolescents: Cochrane systematic reviews

Joint modelling rationale for chained equations

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