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On initial diagnosis or when metabolic control is poor, subjects with type 1 (insulin-dependent) diabetes mellitus often exhibit decreased high density lipoprotein (HDL) cholesterol levels, which have been associated in numerous studies in non-diabetic subjects with atherosclerosis and coronary artery disease. We measured the activities of plasma lecithin:cholesterol acyltransferase (LCAT), post-heparin lipoprotein lipase, and the composition of the HDL subfractions HDL2 and HDL3, in ten poorly controlled type 1 diabetic patients admitted to a metabolic ward (six women and four men, aged 18-37 years). The measurements were repeated after metabolic control had been optimised and again a week after discharge. The results were compared with those of ten healthy normolipidaemic subjects matched for age, sex and body mass. LCAT activity increased significantly (P0.05) with improved metabolic control in the diabetic patients, and showed positive within-person correlation with HDL2 cholesterol ester (r = 0.67; P0.01), HDL2 free cholesterol (r = 0.67; P0.01), phosphatidylcholine (r = 0.49; P0.05), total phospholipids (r = 0.50; P0.01) and apolipoprotein A-I (apo A-I: r = 0.72; P0.01). With improving metabolic control HDL2 lipid levels increased more than twofold and the compositional changes in HDL2 were reflected by an increased apo A-I:apo A-II ratio (P0.05) and a decreased triglyceride:apo A-I ratio (P0.05). Changes in HDL3 levels and composition were minor. The results of this study indicate that an increase in LCAT activity increases the concentration and changes the composition of HDL2 in type 1 diabetic patients with improved metabolic control.

Methods for deriving empirical Bayes estimators are generally available. Corresponding general techniques for assessing the performance of these estimators are not widely developed yet, however. In this paper we provide a general procedure for assessing and comparing the performance of the empirical Bayes estimators and other estimators in a given data set.

Empirical Bayes approach to estimation of many parameters is considered. Special features of the techniques discussed are: (i) the handling of unequal sample sizes at various stages of an Empirical Bayes sampling scheme and (ii) a general iterative procedure for estimating the parameters of a parametric prior distribution based on the likelihood approach. Linear empirical Bayes estimation is also considered. Applica- tion of the general techniques is demonstrated with special reference to a multinomial data distribution. p where, for every i, 0 < Xi ~ m, 0 < 0i < 1 and i=1 ~ Xi : m, i=~=l 0i : 1. The above probability model is fundamental in the analysis of categorical data and contingency table analyses. In the above the quantity m often represents the sample size of a sample of objects classified into p classes. Suppose that in a current experiment, m observations are made on a p-category Bernoulli r.v. Y with probability 0i of belonging to category i. Let Xg be the number of Y's in i-th

Hypothyroidism, alone or combined with progesterone, significantly decreased 7,12-dimethylbenz(a)anthracene (DMBA) mammary tumorigenesis relative to controls. However, the decrease was less in the progesterone-treated group, and statistical analysis showed that progesterone enhanced tumorigenesis to the same extent in hypothyroid animals as in the controls. Most tumours in hypothyroid progesterone-treated rats were adenocarcinomata; in the absence of the hormone most tumours were benign. However, the difference between the tumour types in the 2 groups was not statistically significant. The morphological changes observed in the endocrine glands, genital tracts and non-neoplastic mammary tissue, considered in relation to previously reported data, suggest that hypothyroidism reduced the tumour yield mainly by secondarily inhibiting somatotrophin production and secretion, although the effect of decreased food intake could not be excluded completely. The higher tumour yield in the hypothyroid progesterone-treated rats may have been due to higher circulating levels of prolactin in this group compared with those in the hypothyroid group which received no hormone. ImagesFig. 1Fig. 2Fig. 3Fig. 4Fig. 5Fig. 6Fig. 7Fig. 8

It is shown that the problem of estimation of the correlation coefficient of a bivariate normal population when one of the variables is dichotomized may be attacked with “probit analysis” methods. This represents an extension of the work of Gillman and Goode (3), as it was possible to find by this approach an approximation to the large-sample variance of the resulting estimateG ofρ. An empirical investigation was undertaken with the object of obtaining some information about the distribution ofG for large sample size. Methods for determining the “pass-fail” cut-off are considered.