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Open Access Research article

Refining developmental coordination disorder subtyping with multivariate statistical methods

Christophe Lalanne12*, Bruno Falissard23, Bernard Golse245 and Laurence Vaivre-Douret2456

Author Affiliations

1 AP-HP, Department of Clinical Research, Saint-Louis Hospital, Paris, France

2 Inserm Unit UMR-SO 669, University Paris Sud, Paris Descartes, Paris, France

3 AP-HP, Paul Brousse Hospital, Public Health Department, Villejuif, France

4 AP-HP, Necker-Enfants Malades Hospital, Paris, France

5 University Paris Descartes, Sorbonne Paris Cité, France

6 AP-HP, Port Royal-Cochin Hospital, Dept. Obstetrics & Gynecology, Paris, France

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BMC Medical Research Methodology 2012, 12:107  doi:10.1186/1471-2288-12-107

Published: 26 July 2012



With a large number of potentially relevant clinical indicators penalization and ensemble learning methods are thought to provide better predictive performance than usual linear predictors. However, little is known about how they perform in clinical studies where few cases are available. We used Random Forests and Partial Least Squares Discriminant Analysis to select the most salient impairments in Developmental Coordination Disorder (DCD) and assess patients similarity.


We considered a wide-range testing battery for various neuropsychological and visuo-motor impairments which aimed at characterizing subtypes of DCD in a sample of 63 children. Classifiers were optimized on a training sample, and they were used subsequently to rank the 49 items according to a permuted measure of variable importance. In addition, subtyping consistency was assessed with cluster analysis on the training sample. Clustering fitness and predictive accuracy were evaluated on the validation sample.


Both classifiers yielded a relevant subset of items impairments that altogether accounted for a sharp discrimination between three DCD subtypes: ideomotor, visual-spatial and constructional, and mixt dyspraxia. The main impairments that were found to characterize the three subtypes were: digital perception, imitations of gestures, digital praxia, lego blocks, visual spatial structuration, visual motor integration, coordination between upper and lower limbs. Classification accuracy was above 90% for all classifiers, and clustering fitness was found to be satisfactory.


Random Forests and Partial Least Squares Discriminant Analysis are useful tools to extract salient features from a large pool of correlated binary predictors, but also provide a way to assess individuals proximities in a reduced factor space. Less than 15 neuro-visual, neuro-psychomotor and neuro-psychological tests might be required to provide a sensitive and specific diagnostic of DCD on this particular sample, and isolated markers might be used to refine our understanding of DCD in future studies.