Development of Tests for Arm Coordination Impairment in Paralympic Classification

In a contemporary clinical laboratory it is very common to have to assess the agreement between two quantitative methods of measurement. The correct statistical approach to assess this degree of agreement is not obvious. Correlation and regression studies are frequently proposed. However, correlation studies the relationship between one variable and another, not the differences, and it is not recommended as a method for assessing the comparability between methods.
In 1983 Altman and Bland (B&A) proposed an alternative analysis, based on the quantification of the agreement between two quantitative measurements by studying the mean difference and constructing limits of agreement.
The B&A plot analysis is a simple way to evaluate a bias between the mean differences, and to estimate an agreement interval, within which 95% of the differences of the second method, compared to the first one, fall. Data can be analyzed both as unit differences plot and as percentage differences plot.
The B&A plot method only defines the intervals of agreements, it does not say whether those limits are acceptable or not. Acceptable limits must be defined a priori, based on clinical necessity, biological considerations or other goals.
The aim of this article is to provide guidance on the use and interpretation of Bland Altman analysis in method comparison studies.

Repeated measures correlation (rmcorr) is a statistical technique for determining the common within-individual association for paired measures assessed on two or more occasions for multiple individuals. Simple regression/correlation is often applied to non-independent observations or aggregated data; this may produce biased, specious results due to violation of independence and/or differing patterns between-participants versus within-participants. Unlike simple regression/correlation, rmcorr does not violate the assumption of independence of observations. Also, rmcorr tends to have much greater statistical power because neither averaging nor aggregation is necessary for an intra-individual research question. Rmcorr estimates the common regression slope, the association shared among individuals. To make rmcorr accessible, we provide background information for its assumptions and equations, visualization, power, and tradeoffs with rmcorr compared to multilevel modeling. We introduce the R package (rmcorr) and demonstrate its use for inferential statistics and visualization with two example datasets. The examples are used to illustrate research questions at different levels of analysis, intra-individual, and inter-individual. Rmcorr is well-suited for research questions regarding the common linear association in paired repeated measures data. All results are fully reproducible.

The aim of this study was to provide a comprehensive update on (1) the overall prevalence of cerebral palsy (CP); (2) the prevalence of CP in relation to birthweight; and (3) the prevalence of CP in relation to gestational age. A systematic review and meta-analysis was conducted and reported, based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) statement. Population-based studies on the prevalence of CP in children born in 1985 or after were selected. Statistical analysis was carried out using computer package R, version 2.14. A total of 49 studies were selected for this review. The pooled overall prevalence of CP was 2.11 per 1000 live births (95% confidence interval [CI] 1.98-2.25). The prevalence of CP stratified by gestational age group showed the highest pooled prevalence to be in children weighing 1000 to 1499g at birth (59.18 per 1000 live births; 95% CI 53.06-66.01), although there was no significant difference on pairwise meta-regression with children weighing less than 1000g. The prevalence of CP expressed by gestational age was highest in children born before 28 weeks' gestation (111.80 per 1000 live births; 95% CI 69.53-179.78; p<0.0327). The overall prevalence of CP has remained constant in recent years despite increased survival of at-risk preterm infants. © The Authors. Developmental Medicine & Child Neurology © 2013 Mac Keith Press.