The meta-analytic framework for the evaluation of surrogate endpoints in clinical trials

For a number of reasons, surrogate endpoints are considered instead of the so-called true endpoint in clinical studies, especially when such endpoints can be measured earlier, and/or with less burden for patient and experimenter. Surrogate endpoints may occur more frequently than their standard counterparts. For these reasons, it is not surprising that the use of surrogate endpoints in clinical practice is increasing.     

A Markov-chain-based regression model with random effects for the analysis of 18O-labelled mass spectra

The enzymatic ¹⁸O-labelling is a useful technique for reducing the influence of the between-spectra variability on the results of mass-spectrometry experiments. A difficulty in applying the technique lies in the quantification of the corresponding peptides due to the possibility of an incomplete labelling, which may result in biased estimates of the relative peptide abundance. To address the problem, Zhu et al. [A Markov-chain-based heteroscedastic regression model for the analysis of high-resolution enzymatically ¹⁸O-labeled mass spectra, J. Proteome Res. 9(5) (2010), pp. 2669–2677] proposed a Markov-chain-based regression model with heteroscedastic residual variance, which corrects for the possible bias. In this paper, we extend the model by allowing for the estimation of the technical and/or biological variability for the mass spectra data. To this aim, we use a mixed-effects version of the model. The performance of the model is evaluated based on results of an application to real-life mass spectra data and a simulation study.     

Some Methods of Replacing the Nearest Neighbor Method

In this article, two classifiers, which generalize the nearest neighbor method, are introduced and studied. The first of them is based on calculating the distances to all objects from a learning sample. The second one additionally considers directions of the objects. Both of them have locally nonlinear classification borders. A number of real and artificial datasets and methods of error estimation are used.     

The Assessment of Industrial Health Hazards on The Basis of Observational Data — Some Statistical Pitfalls

Because of ethical and practical difficulties, controlled experimentation is seldom possible in the field of industrial medicine. Safety standards for industrial hazards must therefore be based upon uncontrolled observational data. This paper is concerned with the effects of population selection and of the association between exposure and response upon the exposure-response relationships derived from uncontrolled studies. It is shown that serious distortions may result and that ‘safe limits’ derived from uncontrolled studies may underestimate the real hazard.     

Exact Bounds for the Bias of Trimmed Means

Trimmed means remove outliers but affect the estimation efficiency in uncontaminated samples. This paper provides and analyses sharp upper and lower bounds on the bias in the non–parametric estimation of population means by various trimmed means in different scale units. It also presents sharp evaluations of expected trimmed means by the expectations of properly truncated parent distributions.     

Exploring and validating surrogate endpoints in colorectal cancer

Sargent et al (J Clin Oncol 23: 8664–8670, 2005) concluded that 3-year disease-free survival (DFS) can be considered a valid surrogate (replacement) endpoint for 5-year overall survival (OS) in clinical trials of adjuvant chemotherapy for colorectal cancer. We address the question whether the conclusion holds for trials involving other classes of treatments than those considered by Sargent et al. Additionally, we assess if the 3-year cutpoint is an optimal one. To this aim, we investigate whether the results reported by Sargent et al. could have been used to predict treatment effects in three centrally randomized adjuvant colorectal cancer trials performed by the Japanese Foundation for Multidisciplinary Treatment for Cancer (JFMTC) (Sakamoto et al. J Clin Oncol 22:484–492, 2004). Our analysis supports the conclusion of Sargent et al. and shows that using DFS at 2 or 3 years would be the best option for the prediction of OS at 5 years.     

Maximal Expectations of Extreme Order Statistics from Increasing Density and Failure Rate Populations

We determine sharp bounds for the expectations of the first and second and last order statistics for the i.i.d. samples from the increasing density and failure rate populations. These bounds are expressed by means of the population mean and standard deviation. The results are determined by use of the projections of elements of Hilbert spaces onto convex cones.     

Functional Discriminant Coordinates

In this article we propose a new method of construction of discriminant coordinates and their kernel variant based on the regularization (ridge regression). Moreover, we compare the case of discriminant coordinates, functional discriminant coordinates and the kernel version of functional discriminant coordinates on 20 data sets from a wide variety of application domains using values of the criterion of goodness and statistical tests. Our experiments show that the kernel variant of discriminant coordinates provides significantly more accurate results on the examined data sets.     

Evaluations of generalized order statistics from bounded populations

We consider generalized order statistics with arbitrary model parameters based on distributions supported on finite intervals. We determine optimal bounds on the expectations of arbitrary linear combinations of centered generalized order statistics gauged in support length scale units. More precise representations of bounds are obtained for single generalized order statistics and respective differences.     

Sharp bounds on l-estimates and their expectations for dependent samples

Sharp bounds on the expectation of each L-estimate are determined for samples of identically distributed random variables with a given expectation and a central absolute moment of order p > 1, The distributions achieving these bounds are constructed. Analogous results are derived in the cases of symmetric, noraiegative and bounded random variables, Accurate bounds for L-estimates of arbitrary finite samples are concluded.