Evaluation of WOMAC 20, 50, 70 response criteria in patients treated with hylan G-F 20 for knee osteoarthritis

Objective: A secondary analysis of a previously conducted one year randomised controlled trial to evaluate the capacity of responder criteria based on the WOMAC index to detect between treatment group differences. Methods: 255 patients with knee osteoarthritis were randomised to appropriate care with hylan G-F 20'' (AC+H) or appropriate care without hylan G-F 20'' (AC). In the original analysis, two definitions of patient response from baseline to month 12 were used: ( 1) at least a 20% reduction in WOMAC pain score ( WOMAC 20P); ( 2) at least a 20% reduction in WOMAC pain score and at least a 20% reduction in either WOMAC function or stiffness score ( WOMAC 20PFS). For this analysis, a responder was identified using 50% and 70% minimum clinically important response levels to investigate how increasing response affects the ability to detect treatment group differences. Results: The hylan G- F 20 group had numerically more responders using all patient responder criteria. Increasing the response level from 20% to 50% detected similar differences between treatment groups (25% to 29%). Increasing the response level to 70% reduced the differences between treatment groups (11% to 12%) to a point where the differences were not significant after Bonferroni adjustment. Conclusions: These results provide evidence for incorporating response levels ( WOMAC 50) in clinical trials. While differences at the highest threshold ( WOMAC 70) were not statistically detectable, an appropriately powered study may be capable of detecting differences even at this very high level of improvement.

A simple method to localise pleiotropic susceptibility loci using univariate linkage analyses of correlated traits

Univariate linkage analysis is used routinely to localise genes for human complex traits. Often, many traits are analysed but the significance of linkage for each trait is not corrected for multiple trait testing, which increases the experiment-wise type-I error rate. In addition, univariate analyses do not realise the full power provided by multivariate data sets. Multivariate linkage is the ideal solution but it is computationally intensive, so genome-wide analysis and evaluation of empirical significance are often prohibitive. We describe two simple methods that efficiently alleviate these caveats by combining P-values from multiple univariate linkage analyses. The first method estimates empirical pointwise and genome-wide significance between one trait and one marker when multiple traits have been tested. It is as robust as an appropriate Bonferroni adjustment, with the advantage that no assumptions are required about the number of independent tests performed. The second method estimates the significance of linkage between multiple traits and one marker and, therefore, it can be used to localise regions that harbour pleiotropic quantitative trait loci (QTL). We show that this method has greater power than individual univariate analyses to detect a pleiotropic QTL across different situations. In addition, when traits are moderately correlated and the QTL influences all traits, it can outperform formal multivariate VC analysis. This approach is computationally feasible for any number of traits and was not affected by the residual correlation between traits. We illustrate the utility of our approach with a genome scan of three asthma traits measured in families with a twin proband.