Scoring the SF-36 health survey in scleroderma using independent component analysis and principle component analysis

The short form SF-36 survey is a widely used survey of patient health related quality of life. It yields eight subscale scores of functional health and well-being that are summarized by two physical and mental component summary scores. However, recent studies have reported inconsistent results between the eight subscales and the two component summary measures when the scores are from a sick population. They claim that this problem is due to the method used to compute the SF-36 component summary scores, which is based on principal component analysis with orthogonal rotation.In this thesis, we explore various methods in order to identify a method that is more accurate in obtaining the SF-36 physical and mental component component summary scores (PCS and MCS), with a focus on diseased patient subpopulations. We first explore traditional data analysis methods such as principal component analysis (PCA) and factor analysis using maximum likelihoodestimation and apply orthogonal and oblique rotations with both methods to data from the Canadian Scleroderma Research Group registry. We compare these common approaches to a recently developed data analysis method from signal processing and neural network research, independent component analysis (ICA). We found that oblique rotation is the only method that reduces the meanmental component scores to best match the mental subscale scores. In order to try to better elucidate the differences between the orthogonal and oblique rotation, we studied the performance of PCA with the two approaches for recovering the true physical and mental component summary scores in a simulated diseased population where we knew the truth. We explored the methods in situations where the true scores were independent and when they were also correlated. We found that ICA and PCA with orthogonal rotation performed very similarly when the data were generated to be independent, but differently (with ICA performing worse) when the data were generated to be correlated. PCA with oblique rotation tended to perform worse than both methods when the data were independent, but better when the data were correlated. We also discuss the connection between ICA and PCA with orthogonal rotation, which lends strength to the use of the varimax rotation for the SF-36.Finally, we applied ICA to the scleroderma data and found relatively low correlation between ICA and unrotated PCA in estimating the PCS and MCS scores and very high correlation between ICA and PCA with varimax rotation. PCA with oblique rotation also had a relatively high correlation with ICA. Hence, we concluded that ICA could be seen as a compromise solution between the two methods.

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