Alexander N. Donev

Ana Grgić's book—the third volume of Amsterdam University Press's Eastern European Screen Cultures series—is the first attempt to present early cinema in the Balkans in all its diversity and intricacy—not as the parallel existence of separate national protocinemas but as a unitary phenomenon. In Grgić's perspective, the dates of the first film shows or first original local productions are not the most important. She focuses instead on relationships and common denominators, exploring in depth what made the easy acceptance of cinema possible in the region and marked its long-standing importance. In conducting this strategy, Grgić has had to overcome several persistent biases.

We propose a new class of models providing a powerful unification and extension of existing statistical methodology for analysis of data obtained in mixture experiments. These models, which integrate models proposed by Scheffé and Becker, extend considerably the range of mixture component effects that may be described. They become complex when the studied phenomenon requires it, but remain simple whenever possible. This article has supplementary material online.

The experimental design plays an important role in every experimental study. However, if errors in the settings of the studied factors cannot be avoided, i.e. Berkson errors occur, the estimates of the model parameters may be biased and the variability in the study increased. Correction methods for the effect of Berkson errors are compared. The emphasis is on the study of correlated Berkson errors which follow non-Gaussian distribution as this appears to have been a neglected, yet important, area. It is shown that the regression calibration approach bias correction methods are useful when the Berkson errors are independent. However, when these errors are dependent, the newly proposed method B-SIMEX clearly outperforms the other methods.

Dose-response studies are an essential part of the drug discovery process. They are typically carried out on a large number of chemical compounds using serial dilution experimental designs. This paper proposes a method of selecting the key parameters of these designs (maximum dose, dilution factor, number of concentrations and number of replicated observations for each concentration) depending on the stage of the drug discovery process where the study takes place. This is achieved by employing and extending results from optimal design theory. Population D- and D _S -optimality are defined and used to evaluate the precision of estimating the potency of the tested compounds. The proposed methodology is easy to use and creates opportunities to reduce the cost of the experiments without compromising the quality of the data obtained in them.

This paper is concerned with the statistical analysis of data obtained in studies of the joint action of drugs. The three methods that are compared are illustrated on real data (1), using the statistical package SAS. It is argued that while the results obtained using these methods do not differ substantially, the method allowing for estimating simultaneously all required parameters is to be preferred. It allows for a statistical test for the significance of the joint action of the drug combinations to be carried out.

There is little confidence in the consistency of estimation of DNA concentrations when samples move between laboratories. Evidence on this consistency is largely anecdotal. Therefore there is a need first to measure this consistency among different laboratories and then identify and implement remedies. A pilot experiment to test logistics and provide initial data on consistency was therefore conceived. DNA aliquots at nominal concentrations between 10 and 300 ng/μl were dispensed into the wells of 96-well plates by one participant - the coordinating centre. Participants estimated the concentration in each well and returned estimates to the coordinating centre. Considerable overall variability was observed among estimates. There were statistically significant differences between participants' measurements and between fluorescence emission and absorption spectroscopy. Anecdotal evidence of variability in DNA concentration estimation has been substantiated. Reduction in variability between participants will require the identification of major sources of variation, specification of effective remedies and their implementation.