SummaryPrincipal component analysis is a mathematical technique for summarizing a set of related measurements as a set of derived variates, frequently fewer in number, which are definable as independent linear functions of the original measurements. Consideration of their mathematical nature shows that they do not, themselves, necessarily correspond to sensible biological concepts, though they are more amenable to statistical study than the original measurements. Further, by assessing the extent to which they are in accordance with biological hypotheses, or with the results of other, similar, analyses, they can be transformed into other linear functions which are meaningful in the biological sense, or consistent with other results. Thus the specific technique of principal component analysis is developed into a more general component analysis approach. With proper regard for the purpose the analysis is intended to serve and for the mathematical restrictions involved, this approach can lead to a useful condensation of a mass of data, a better under-standing of the observed individuals as entities rather than collections of isolated measurements, and to the formulation of new hypotheses for subsequent examination.
A Markov Model to Estimate Mortality Due to HIV/AIDS Using Viral Load Levels-Based States and CD4 Cell Counts: A Principal Component Analysis Approach