Altered platelet morphology and function have been reported in patients with diabetes. They are likely to be associated with the pathological processes and increased risk of vascular disease seen in these patients. Mean platelet volume (MPV), platelet count, and megakaryocyte (MK) ploidy (DNA content) were measured in (1) nondiabetics with normal coronary arteries, (2) nondiabetics with coronary artery atherosclerosis, (3) diabetics without evidence of vascular complications, and (4) diabetics with vascular disease. The platelet count (+/- SD) was increased in all groups but only significantly in the diabetics with vascular disease (236 +/- 65 versus 250 +/- 54 versus 257 +/- 64 versus 295 +/- 90 [P < or = .05] x 10(9)/L, for groups, I, II, II, and IV, respectively). The MPV was significantly increased in patients with atherosclerosis (7.0 +/- 0.4 versus 8.0 +/- 1.2 [P < or = .05] versus 7.2 +/- 0.9 versus 8.1 +/- 0.9 [P < or = .05] IL). Geometric mean MK ploidy was significantly increased in all groups compared with controls (16 +/- 1.5 versus 18.7 +/- 1.8 [P < or = .05] versus 19.8 +/- 1.6 [P < or = .05] versus 20.1 +/- 2.7 [P < or = .05]). Furthermore, some patients with vascular disease and/or diabetes had a modal ploidy shift from 16 (the normal mammalian modal ploidy) to 32, with a concomitant reduction of MKs in the 8 and 16 ploidy classes. This shift was seen particularly in the diabetics with vascular disease (P = .007). Interleukin-6 (IL-6) levels were measured and were elevated in patients with atherosclerosis; the highest levels were found in the diabetic patients (0.7 +/- 0.9 versus 5.3 +/- 5.5 [P < or = .05] versus 2.5 +/- 2.8 versus 6.7 +/- 5.5 [P < or = .05] ng/L). In the diabetic patients with atherosclerosis, fibrinogen levels were also increased (2.85 +/- 0.76 versus 3.34 +/- 1.32 versus 2.43 +/- 1.50 versus 5.59 +/- 1.72 [P< or = .05] g/L). Furthermore, IL-6 levels correlated with MK ploidy (r = .45, P = .009) and fibrinogen levels (r = .5, P = .0001). This study demonstrates that patients with vascular disease, particularly diabetics, have an altered MK ploidy distribution, showing a shift toward higher ploidy in association with an increased platelet mass (count x volume). Changes in platelets in diabetes probably reflect MK changes, which themselves are a response to systemic change.

Quality of life data present considerable statistical challenges because of their longitudinal and multidimensional nature, and also because the available data are often very unbalanced through missing values. Here we exemplify the potential of multi-level models, that is, hierarchical random coefficient models, for such data. The discussion is developed in the context of analysing the quality of life data from a trial of palliative treatment in non-small-cell lung cancer. Not only do multi-level models provide a flexible modelling framework for the investigation of the underlying behaviour of response, for example, giving simple estimates of treatment effects, but they also permit a description of the differences between subjects and allow the analysis of multi-dimensional outcomes. The assumptions of Normality, homogeneity, and independence of the within- and between-subject variance components can be investigated and the models can be extended to provide explicit modelling of variance heterogeneity. It is concluded that multi-level models, for which software is now available, provide a natural and powerful approach to the analysis of longitudinal data in general, and multi-dimensional quality of life data in particular.

Overviews that combine single effect estimates from published studies generally use a summary statistic approach where the effect of interest is first estimated within each study and then averaged across studies in an appropriately weighted manner. Combining multiple regression coefficients from publications is more problematic, particularly when there are differences in study design and inconsistent reporting of effect sizes and standard errors. This paper describes the use of a hierarchical model in such circumstances. Its use is illustrated in a meta-analysis of the metabolic ward studies that have investigated the effect of changes in intake of various dietary lipids on blood cholesterol. These studies all reported average blood cholesterol for groups of individuals who were studied on one or more diets. Thirty-one studies had randomized cross-over designs, 12 had matched parallel group designs, 12 had non-randomized Latin square designs and 16 had other uncontrolled designs. The hierarchical model allowed the different types of comparison (within-group between-diet, between matched group) that were made in the various studies to each contribute to the overall estimates in an appropriately weighted manner by distinguishing between-study variation, within-study between-matched-group variation and within-group between-diet variation. The hierarchical models do not require consistent specification of effect sizes and standard errors and hence have particular utility in combining results from published studies where the relationships between a dependent variable and two or more predictors have been investigated using heterogeneous methods of analysis.