The aim of this study was to use Monte Carlo simulations to compare logistic regression with propensity scores in terms of bias, precision, empirical coverage probability, empirical power, and robustness when the number of events is low relative to the number of confounders. The authors simulated a cohort study and performed 252,480 trials. In the logistic regression, the bias decreased as the number of events per confounder increased. In the propensity score, the bias decreased as the strength of the association of the exposure with the outcome increased. Propensity scores produced estimates that were less biased, more robust, and more precise than the logistic regression estimates when there were seven or fewer events per confounder. The logistic regression empirical coverage probability increased as the number of events per confounder increased. The propensity score empirical coverage probability decreased after eight or more events per confounder. Overall, the propensity score exhibited more empirical power than logistic regression. Propensity scores are a good alternative to control for imbalances when there are seven or fewer events per confounder; however, empirical power could range from 35% to 60%. Logistic regression is the technique of choice when there are at least eight events per confounder.

The Bergman Minimal Model enables estimation of two key indices of glucose/insulin dynamics: glucose effectiveness and insulin sensitivity. In this paper we describe MINMOD Millennium, the latest Windowsbased version of minimal model software. Extensive beta testing of MINMOD Millennium has shown that it is user-friendly, fully automatic, fast, accurate, reproducible, repeatable, and highly concordant with past versions of MINMOD. It has a simple interface, a comprehensive help system, an input file editor, a file converter, an intelligent processing kernel, and a file exporter. It provides publication-quality charts of glucose and insulin and a table of all minimal model parameters and their error estimates. In contrast to earlier versions of MINMOD and some other minimal model programs, Millennium provides identified estimates of insulin sensitivity and glucose effectiveness for almost every subject. Disciplines

Standardization of study design and outcome parameters would help to compare different studies evaluating various novel therapeutic concepts. Comparison to the human clinical counterpart during study design may help increase the predictive value of preclinical research using animal models and improve the process of developing efficacious therapies.

We report on a quantitative analysis of electrocorticography data from a study that acquired continuous ambulatory recordings in humans over extended periods of time. The objectives were to examine patterns of seizures and spontaneous interictal spikes, their relationship to each other, and the nature of periodic variation. The recorded data were originally acquired for the purpose of seizure prediction, and were subsequently analysed in further detail. A detection algorithm identified potential seizure activity and a template matched filter was used to locate spikes. Seizure events were confirmed manually and classified as either clinically correlated, electroencephalographically identical but not clinically correlated, or subclinical. We found that spike rate was significantly altered prior to seizure in 9 out of 15 subjects. Increased pre-ictal spike rate was linked to improved predictability; however, spike rate was also shown to decrease before seizure (in 6 out of the 9 subjects). The probability distribution of spikes and seizures were notably similar, i.e. at times of high seizure likelihood the probability of epileptic spiking also increased. Both spikes and seizures showed clear evidence of circadian regulation and, for some subjects, there were also longer term patterns visible over weeks to months. Patterns of spike and seizure occurrence were highly subject-specific. The pre-ictal decrease in spike rate is not consistent with spikes promoting seizures. However, the fact that spikes and seizures demonstrate similar probability distributions suggests they are not wholly independent processes. It is possible spikes actively inhibit seizures, or that a decreased spike rate is a secondary symptom of the brain approaching seizure. If spike rate is modulated by common regulatory factors as seizures then spikes may be useful biomarkers of cortical excitability.