Application of multilevel models to morphometric data. Part 1. Linear models and hypothesis testing.
Morphometric data usually have a hierarchical structure (i.e., cells are nested within patients), which should be taken into consideration in the analysis. In the recent years, special methods of handling hierarchical data, called multilevel models (MM), as well as corresponding software have received considerable development. However, there has been no application of these methods to morphometric data yet. In this paper we report our first experience of analyzing karyometric data by means of MLwiN - a dedicated program for multilevel modeling. Our data were obtained from 34 follicular adenomas and 44 follicular carcinomas of the thyroid. We show examples of fitting and interpreting MM of different complexity, and draw a number of interesting conclusions about the differences in nuclear morphology between follicular thyroid adenomas and carcinomas. We also demonstrate substantial advantages of multilevel models over conventional, single-level statistics, which have been adopted previously to analyze karyometric data. In addition, some theoretical issues related to MM as well as major statistical software for MM are briefly reviewed.
D000236:Adenoma / Q000235:genetics / Q000473:pathology*; D000465:Algorithms; D002277:Carcinoma / Q000235:genetics / Q000473:pathology*; D002467:Cell Nucleus / Q000473:pathology; D006801:Humans; D019044:Image Cytometry / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007620:Karyometry / Q000295:instrumentation / Q000379:methods; D016014:Linear Models; D015233:Models, Statistical; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012984:Software / Q000592:standards; D013964:Thyroid Neoplasms / Q000235:genetics / Q000473:pathology*