The goals of EU competition law: a comprehensive empirical investigation

For any field of law, the goal it was designed to achieve permeates every aspect of its application and interpretation. This is particularly true when the black letter of the law is cryptic and silent on most aspects of how it should be interpreted and applied, as is the case with competition law, which for the most part revolves around a small number of highly abstract provisions. It is only natural then that ample scholarly work has been devoted to identifying the goals and purposes of competition law. By and large these attempts have been textual, historical, and teleological. We introduce here instead a quantitative analysis of the case law and present the results of the first empirical study into the goals and purposes of EU competition law as they emerge from the entirety of the case law of the European Court of Justice, opinions of the Advocate Generals, Commission decisions, and speeches of Commissioners for Competition. This body of almost 4,000 sources paints a comprehensive picture of the underlying goals of EU competition law, and helps conclusively confirm some previous insights while debunking others, thereby helping to advance the present application and future evolution of competition law

WTO Case Law in 2014

Giorgio Sacerdoti (ed.)</p

WTO Case Law in 2014

Giorgio Sacerdoti (ed.)</p

A critical examination of the principal theories of the Hebrew verbal system between 1827 and 1954 with particular reference to the waw consecutive problem

SIGLEAvailable from British Library Document Supply Centre- DSC:D42136/82 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

The response to 5-FU treatment can be captured with the combined Gompertz/two-compartment pharmacokinetic model: Indicative figures from two treated mice, one from each treatment group.

<p>Left panel–CM.41 from 5-FU 1; Right panel–CM.43 from 5-FU 2. The black squares indicate the measured tumor volumes while the green line is the fitted model based on Eqs (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143840#pone.0143840.e002" target="_blank">2</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143840#pone.0143840.e005" target="_blank">5</a>). The red dashed line is the prediction of what would have happened if the tumor was left untreated based on the pre-treatment data estimates of the tumor growth rate. Our model appears to accurately capture, both the growth and treatment dynamics of the tumor. The NMSE values for the three tumors of CM.41 are 44.7%, 26.8% and 35.4% and for the three tumors of CM.43 these are 18.5%, 6.7% and 13.0% respectively.</p

Drug efficiency is variable and depends on tumor growth rate.

<p>A. Left Panel–Estimates of the drug kill rate parameter, <i>k</i>_<i>eff</i>, for the two treatment groups in our experiment (A.1.: 5-FU 1; A.2.: 5-FU 2). Unlike the tumor growth rate, which is appears mouse specific, response to treatment that is reflected through <i>k</i>_<i>eff</i> is less dependent on mouse. B. Top-right Panel: Scatterplot of <i>τ</i>_<i>g</i> against <i>k</i>_<i>eff</i> and non-parametric regression fit from kernel regression (red line). A negative correlation between the two parameters or equivalently, a positive correlation between growth and drug kill rates is observed, further supported by the value of Spearman’s correlation coefficient (<i>r</i> = -0.726). C. Bottom-right Panel: Scatter plot of the estimated values of the drug kill rate parameter (<i>k</i>_<i>eff</i>) from all three tumors per mouse, for both treatment groups, Two distinct groups are formed, whereby the first group (black–more effective per unit drug volume) comprises four out of the five 5FU-1 mice and the second group (red–less effective per unit drug volume) comprises the remaining 5FU-1 mouse (CM.62) and all five 5-FU2 mice.</p

Experimental design and pharmacokinetic model: A. Schematic outline of the experiment. B. Plasma (<i>C</i>_<i>1</i>) and tumor site (<i>C</i>_<i>2</i>) drug concentrations for the two 5-FU dosages administered in the experiment.

<p>The figures show the concentrations from the time when the drug was administered until 24 hours later and are calculated according to Eq (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143840#pone.0143840.e005" target="_blank">5</a>).</p

The variability of tumor growth <i>in vivo</i> can be captured with the Gompertz model: Indicative growth curves for a mouse with fast growing tumors (CM.37 –Left panel) and another with slow growing tumors (CM.53 –Right panel).

<p>The mice belong to the DMSO group and received no drug treatment. The black squares are the measured tumor volumes and the red line is the fitted model output using Eq (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143840#pone.0143840.e002" target="_blank">2</a>) without treatment. The model provides an overall satisfactory fit to the data. The NMSE values are 17.4%, 5.3%, 10.7% for the three tumors of CM.37 and 16.1%, 27.1%, 19.8% for the three tumors of CM.53. Additionally tumor growth rate appears to be mouse specific.</p

Tumor growth <i>in vivo</i> is mouse-specific: A. Top panel–Estimates of the tumor doubling time parameter <i>τ</i>_<i>g</i>. For each of the eight mice in the DMSO group we obtain one estimate for each of the three monitored tumors. The figure suggests that tumor growth is mouse specific. Four mice (CM.37, 38, 60 & 78) exhibit three fast growing tumors while three mice (CM.53, 76 & 77) exhibit three slow growing tumors. The only exception is CM.79, for which the first tumor is slow growing, unlike the other two tumors. B. Bottom panel–The existence of two discrete growth rate groups is further illustrated by the distribution of all three tumor doubling times for the eight mice, shown here in a three-dimensional scatter plot.

<p>Mice CM.37, 38, 60 & 78 comprise one group, while the other four comprise a second group.</p