Efficient computation of subset selection probabilities with application to Cox regression

An efficient method is presented to compute the probabilityof selection of a specified subset from the set of all subsetsof a fixed size where the subsets are taken from a populationwhose units have varying individual probabilities of selection.The problem is motivated by the computation of the exact marginallikelihood for the Cox proportional hazards model.     

Metabolic changes during carbon monoxide poisoning: An experimental study

Carbon monoxide (CO) is the leading cause of death by poisoning worldwide. The aim was to explore the effects of mild and severe poisoning on blood gas parameters and metabolites. Eleven pigs were exposed to CO intoxication and had blood collected before and during poisoning. Mild CO poisoning (carboxyhaemoglobin, COHb 35.2 ± 7.9%) was achieved at 32 ± 13 minutes, and severe poisoning (69.3 ± 10.2% COHb) at 64 ± 23 minutes from baseline (2.9 ± 0.5% COHb). Blood gas parameters and metabolites were measured on a blood gas analyser and nuclear magnetic resonance spectrometer, respectively. Unsupervised principal component, analysis of variance and Pearson's correlation tests were applied. A P-value ≤ .05 was considered statistically significant. Mild poisoning resulted in a 28.4% drop in oxyhaemoglobin (OHb) and 12-fold increase in COHb, while severe poisoning in a 65% drop in OHb and 24-fold increase in COHb. Among others, metabolites implicated in regulation of metabolic acidosis (lactate, P < .0001), energy balance (pyruvate, P < .0001; 3-hydroxybutyrc acid, P = .01), respiration (citrate, P = .007; succinate, P = .0003; fumarate, P < .0001), lipid metabolism (glycerol, P = .002; choline, P = .0002) and antioxidant-oxidant balance (glutathione, P = .03; hypoxanthine, P < .0001) were altered, especially during severe poisoning. Our study adds new insights into the deranged metabolism of CO poisoning and leads the way for further investigation.     

Optimal designs for a multiresponse Emax model and efficient parameter estimation

The aim of dose finding studies is sometimes to estimate parameters in a fitted model. The precision of the parameter estimates should be as high as possible. This can be obtained by increasing the number of subjects in the study, N, choosing a good and efficient estimation approach, and by designing the dose finding study in an optimal way. Increasing the number of subjects is not always feasible because of increasing cost, time limitations, etc. In this paper, we assume fixed N and consider estimation approaches and study designs for multiresponse dose finding studies. We work with diabetes dose–response data and compare a system estimation approach that fits a multiresponse Emax model to the data to equation‐by‐equation estimation that fits uniresponse Emax models to the data. We then derive some optimal designs for estimating the parameters in the multi‐ and uniresponse Emax model and study the efficiency of these designs.