title: Primary unit for statistical analysis in morphometry: patient or cell?
abstract: In a series of 16 oxyphilic follicular neoplasms of the thyroid (8 adenomas and 8 carcinomas), three different approaches for the analysis of morphometric data were evaluated. It was shown that the statistical design of morphometric studies is by nature nested due to subsampling of cells within each patient. Therefore, the most appropriate analysis would be to account for this hierarchical structure. However, related statistical methods are not at present well established, especially as far as classification rules are concerned. Therefore, the nested design is converted into the simple factorial one by considering only one kind of statistical unit - either patients or cells. The results of the study presented indicate that ignoring the patient as unit of analysis leads to a substantial error in statistical output, regardless of the particular procedure applied. Moreover, the size of the error can be neither diminished nor controlled. Choosing patients as primary units assures accurate results and also has an advantage of gaining some additional information by calculating several distributional estimates in each patient. However, this approach often requires a reduction of dimensions and, furthermore, is not encouraged in certain fields of quantitative cytology. Advantages and disadvantages of all approaches have been summarized and practical recommendations for their use have been worked out.
authors.affiliation: Department of Pathology, University of Graz, School of Medicine, Austria.
authors.forename: O
authors.initials: O
authors.lastname: Tsybrovskyy
authors.forename: A
authors.initials: A
authors.lastname: Berghold
mesh_terms: D018263:Adenocarcinoma, Follicular / Q000473:pathology*; D018249:Adenoma, Oxyphilic / Q000473:pathology*; D000704:Analysis of Variance; D016002:Discriminant Analysis; D005069:Evaluation Studies as Topic; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D015233:Models, Statistical; D013964:Thyroid Neoplasms / Q000473:pathology*