Header

UZH-Logo

Maintenance Infos

Modelling neuromotor ratings with floor-effects


Gasser, T; Rousson, V (2004). Modelling neuromotor ratings with floor-effects. Statistics in Medicine, 23(23):3641-3653.

Abstract

Associated movements (AMs) are a classical diagnostic tool to assess differences between normal children and children with some motor dysfunction. This paper presents a methodology to produce age- and gender-dependent reference-curves for AMs of normal children, for various tasks of a test battery. Data available consist of separate ratings of duration and extent of AMs, which are ordinal quantities with few levels. Other problems are severe age- and gender-dependent floor-effects (as well as some ceiling-effects), leaving little information for analysis at older ages. To get a better scale, we combined the two ordinal ratings into one meaningful and quasi-continuous quantity referred to as intensity of AMs. In order to solve problems due to floor-effects, ceiling-effects and discreteness, we assumed left- , right- and interval-censored values, respectively. We considered a censored regression problem and postulated a truncated normal distribution for the non-censored values (after an appropriate transformation of the data). Using Wei and Tanner's poor man's data augmentation algorithm, together with the technique of linear mixed effects modelling, useful reference-curves could be produced. In contrast to the cumulative probabilities approach for ordinal data, our methodology allows the calculation of individual age- and gender-standardized values, which puts us in a position to investigate numerous scientific questions.

Abstract

Associated movements (AMs) are a classical diagnostic tool to assess differences between normal children and children with some motor dysfunction. This paper presents a methodology to produce age- and gender-dependent reference-curves for AMs of normal children, for various tasks of a test battery. Data available consist of separate ratings of duration and extent of AMs, which are ordinal quantities with few levels. Other problems are severe age- and gender-dependent floor-effects (as well as some ceiling-effects), leaving little information for analysis at older ages. To get a better scale, we combined the two ordinal ratings into one meaningful and quasi-continuous quantity referred to as intensity of AMs. In order to solve problems due to floor-effects, ceiling-effects and discreteness, we assumed left- , right- and interval-censored values, respectively. We considered a censored regression problem and postulated a truncated normal distribution for the non-censored values (after an appropriate transformation of the data). Using Wei and Tanner's poor man's data augmentation algorithm, together with the technique of linear mixed effects modelling, useful reference-curves could be produced. In contrast to the cumulative probabilities approach for ordinal data, our methodology allows the calculation of individual age- and gender-standardized values, which puts us in a position to investigate numerous scientific questions.

Statistics

Citations

7 citations in Web of Science®
6 citations in Scopus®
Google Scholar™

Altmetrics

Downloads

1 download since deposited on 17 Jun 2009
0 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Epidemiology, Biostatistics and Prevention Institute (EBPI)
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:2004
Deposited On:17 Jun 2009 15:35
Last Modified:05 Apr 2016 13:16
Publisher:Wiley-Blackwell
ISSN:0277-6715
Publisher DOI:https://doi.org/10.1002/sim.1914
PubMed ID:15534892

Download

Preview Icon on Download
Filetype: PDF - Registered users only
Size: 1MB
View at publisher

TrendTerms

TrendTerms displays relevant terms of the abstract of this publication and related documents on a map. The terms and their relations were extracted from ZORA using word statistics. Their timelines are taken from ZORA as well. The bubble size of a term is proportional to the number of documents where the term occurs. Red, orange, yellow and green colors are used for terms that occur in the current document; red indicates high interlinkedness of a term with other terms, orange, yellow and green decreasing interlinkedness. Blue is used for terms that have a relation with the terms in this document, but occur in other documents.
You can navigate and zoom the map. Mouse-hovering a term displays its timeline, clicking it yields the associated documents.

Author Collaborations