No abstract

No abstract

This study on serum ferritin levels in urinary iron excretion after 12h subcutaneous infusion of desferrioxamine in 10 thalassemia intermedia patients shows that even nontransfusion-dependent patients may have positive iron balance resulting in iron overload from 5 years of age. However, the iron overload found in these patients appears to be much lower than in age matched patients with transfusion-dependent thalassemia major. Iron overload increases with advancing age, as shown by increasing serum ferritin levels and desferrioxamine-induced urinary iron elimination. After a six month trial of 12h continuous subcutaneous desferrioxamine administration there was a significant decline in serum ferritin levels. From this study it seems that iron chelation is indicated in thalassemia intermedia patients over 5 years of age in order to prevent iron accumulation. However, the appropriate treatment schedule should be tailored to the individual needs of each patients, established by close monitoring of serum ferritin levels and desferrioxamine-induced urinary iron elimination.

No abstract

Multiple hypothesis testing collects a series of techniques usually based on p-values as a summary of the available evidence from many statistical tests. In hypothesis testing, under a Bayesian perspective, the evidence for a specified hypothesis against an alternative, conditionally on data, is given by the Bayes factor. In this study, we approach multiple hypothesis testing based on both Bayes factors and p-values, regarding multiple hypothesis testing as a multiple model selection problem. To obtain the Bayes factors we assume default priors that are typically improper. In this case, the Bayes factor is usually undetermined due to the ratio of prior pseudo-constants. We show that ignoring prior pseudo-constants leads to unscaled Bayes factor which do not invalidate the inferential procedure in multiple hypothesis testing, because they are used within a comparative scheme. In fact, using partial information from the p-values, we are able to approximate the sampling null distribution of the unscaled Bayes factor and use it within Efron's multiple testing procedure. The simulation study suggests that under normal sampling model and even with small sample sizes, our approach provides false positive and false negative proportions that are less than other common multiple hypothesis testing approaches based only on p-values. The proposed procedure is illustrated in two simulation studies, and the advantages of its use are showed in the analysis of two microarray experiments.