Injuries of the Head, Face, and Neck in Relation to Ski Helmet Use
Harborview Injury Prevention & Research Center, School of Public Health & Community Medicine, University of Washington, Seattle, WA, USA. Epidemiology
(Impact Factor: 6.2).
04/2008; 19(2):270-6. DOI: 10.1097/EDE.0b013e318163567c
The extent to which helmet use reduces the risk of injury in ski- and snowboard-related accidents is unclear. We studied the association of helmet use with injuries of the head, face, and neck among skiers and snowboarders involved in falls and collisions.
We conducted a case-control study, using ski patrol injury reports for the years 2000-2005 from 3 ski resorts in the western United States. We identified all skiers and snowboarders involved in falls or collisions who received care from the ski patrol. Helmet use among persons with injuries of the head (n = 2537), face (n = 1122), or neck (n = 565) was compared with helmet use among those involved in falls and collisions who received care for injuries below the neck (n = 17,674). We calculated odds ratios for head, face, and neck injury among helmeted compared with unhelmeted persons.
The adjusted odds ratios were 0.85 for head injury (95% confidence interval = 0.76-0.95), 0.93 for facial injury (0.79-1.09), and 0.91 for neck injury (0.72-1.14).
Helmets may provide some protection from head injury among skiers and snowboarders involved in falls or collisions.
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- "van Buuren and Groothuis-Oudshoorn  discuss the wide range of medical fields that have used chained equations imputation (e.g. addiction , epidemiology , infectious diseases , genetics , cancer , obesity and physical activity ), and a brief review of available software that have implemented chained equations imputation is given by . Given the popularity of chained equations, among users of varying degrees of expertise, there is now guidance in its practical use (e.g. "
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ABSTRACT: Chained 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.
Taking 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.
We 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.
Since 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.
BMC Medical Research Methodology 02/2014; 14(1):28. DOI:10.1186/1471-2288-14-28 · 2.27 Impact Factor
Available from: Tracey J. Dickson
- "In the design phase it has been suggested that sports injury research is plagued with the lack of consistent injury and exposure definitions , which is exacerbated by the inappropriate choice, or lack of adequate controls in the dominant case-control study design . For example, Finch et al described four snowsport head injury studies assessing the effectiveness of helmet use     none of which used biomechanical matching of controls to the head injured cases. The controls included people who did not experience either a head injury or a head impact. "
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ABSTRACT: With the increasing concern about the long-term effects of concussive and sub-concussive head accelerations in sport, this research applies two technologies initially developed for team-based sports to snowsports to understand the characteristics of snowsport head acceleration. Results indicate that pediatric snowsports participants regularly achieved speeds over 23 km/h; snowsport head accelerations are rare and that when they do occur they are generally of low magnitude; and those most at risk were make snowboarders.
Procedia Engineering 08/2013; 60:220-225. DOI:10.1016/j.proeng.2013.07.079
Available from: Catharina G Groothuis-Oudshoorn
- "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. 2001, 2003; Clark and Altman 2003; Royston 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 (Souverein et al. 2006), health economics (Briggs et al. 2003; Burton et al. 2007; Klein et al. 2008; Marshall et al. 2009), obesity and physical activity (Orsini et al. 2008a; Wiles et al. 2008; Orsini et al. 2008b; van Vlierberghe et al. 2009), pediatrics and child development (Hill et al. 2004; Mumtaz et al. 2007; Deave et al. 2008; Samant et al. 2008; Butler and Heron 2008; Ramchandani et al. 2008; van Wouwe et al. 2009), rehabilitation (van der Hulst et al. 2008), behavior (Veenstra et al. 2005; Melhem et al. 2007; Horwood et al. 2008; Rubin et al. 2008), quality of care (Sisk et al. 2006; Roudsari et al. 2007; Ward and Franks 2007; Grote et al. 2007; Roudsari et al. 2008; Grote et al. 2008; Sommer et al. 2009), human reproduction (Smith et al. 2004a,b; Hille et al. 2005; Alati et al. 2006; O'Callaghan et al. 2006; Hille et al. 2007; Hartog et al. 2008), management sciences (Jensen and Roy 2008), occupational health (Heymans et al. 2007; Brunner et al. 2007; Chamberlain et al. 2008), politics (Tanasoiu and Colonescu 2008), psychology (Sundell et al. 2008) and sociology (Finke and Adamczyk 2008). All authors use some form of chained equations to handle the missing data, but the details vary considerably. "
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ABSTRACT: Multivariate Imputation by Chained Equations (MICE) is the name of software for imputing incomplete multivariate data by Fully Conditional Speci cation (FCS). MICE V1.0 appeared in the year 2000 as an S-PLUS library, and in 2001 as an R package. MICE V1.0 introduced predictor selection, passive imputation and automatic pooling. This article presents MICE V2.0, which extends the functionality of MICE V1.0 in several ways. In MICE V2.0, the analysis of imputed data is made completely general, whereas the range of models under which pooling works is substantially extended. MICE V2.0 adds new functionality for imputing multilevel data, automatic predictor selection, data handling, post-processing imputed values, specialized pooling and model selection. Imputation of categorical data is improved in order to bypass problems caused by perfect prediction. Special attention to transformations, sum scores, indices and interactions using passive imputation, and to the proper setup of the predictor matrix. MICE V2.0 is freely available from CRAN as an R package mice. This article provides a hands-on, stepwise approach to using mice for solving incomplete data problems in real data.
Journal of statistical software 12/2011; 45(3). DOI:10.18637/jss.v045.i03 · 3.80 Impact Factor
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