Analysis of large and small samples of biochemical and clinical data.
Statistical software often offers a list of various descriptive statistics of location and scale, but rarely selects an efficient estimate that is statistically adequate for an actual univariate sample. The sample interval estimate for a specified degree of uncertainty seems to be more meaningful if it covers an unknown value of the population parameter. The concept of an interval estimate in medicine is then used for medical decision-making. The proposed methodology, which uses the S-Plus algorithm for biochemical, biological and clinical data analysis contains the following steps: (i) Exploratory data analysis identifies basic statistical features and patterns of the data, the distributions of which are mostly non-normal, non-homogeneous and often corrupted by outliers. (ii) Sample assumptions about data, independence of sample elements, normality and homogeneity are examined. (iii) Power transformation and the Box-Cox transformation to improve sample symmetry and stabilize the spread. (iv) Classical and robust statistics for both large (n>30) and medium-sized samples (15<n<30), point and interval estimates for the parameters of location, scale and shape. For an analysis of small samples (4<n<20) the Horn procedure of pivot measures is recommended. The proposed methodology is demonstrated in two case studies, a large sample analysis of mean pregnenolone concentrations in the umbilical blood of newborns, and a small sample analysis of mean haptoglobin concentrations in human serum.