A New Class of Self-Designing Clinical Trials

A class of self-designing clinical trials is considered which according to an effective but simple, finite learning algorithm consists of automatically adaptively planned weighted group sequential trials with a decision about rejection of the null-hypothesis at each step, but the full level- a-test at the end of the study preserved

A Self-Designing Rule for Clinical Trials with Arbitrary Response Variables

For testing one{sided but also two{sided hypotheses concerning several treatment arms in group sequentially performed clinical trials with arbitrary outcome variables, a general learning method is considered that allows for a complete self-designing of the study. All information available prior to a stage is used for estimating the sample size and the weight for the next step. In 'using up' the variance, the test statistic is built in a bounded finite but random number of stages t

A Short-Cut Method for Computing Positive Variance Component Estimates

In a general variance component model with positive variance components a short-cut metho

Ordnungserhaltende positive Varianzschätzer bei gepaarten Messungen ohne Wiederholungen

Sind in einer statistischen Analyse mehr als eine Variationsursache zu berücksichtigen, s

A Note on Combining Dependent Tests of Significance

In combining several tests of significance the individual test statistics are allowed to be dependent. By choosing the weighted inverse normal method for the combination, the dependency of the original test statistics is then characterized by a correlation of the transformed statistics. For this correlation a confidence region, an unbiased estimator and an unbiased estimate of its variance are derived. The combined test statistic is extended to include the case of possibly dependent original test statistics. A simulation study shows the performance of the actual significance level

A short-cut method for computing positive variance component estimates

In a general variance component model with positive variance components a short-cut method is presented that yields almost everywhere for these components positive estimators that are invariant with respect to mean value translation and stay near the unbiasedness.In einem allgemeinen Varianz-Komponenten-Modell mit positiven Varianzkomponenten wird eine verkürzte Methode vorgestellt, welche für diese Komponenten fast überall positive Schätzer ergibt, die invariant bzgl. Mittelwerttranslationen sind und nahe der Unverzerrtheit bleiben

Messen faserförmiger Partikel: Strategie zur Auswertung des ersten VDI-Ringversuchs zur Asbestfaserbestimmung nach Richtlinie VDI 3492 Blatt 1

Die Umsetzung der Richtlinie zur Bestimmung der Anzahl von anorganischen faserförmigen Partikeln auf staubbelegten Filtern wurde in einem Ringversuch von VDI und DIN überprüft. Die Auswertung der Filter wurde zunächst in zwei Referenzlabors und danach in den teilnehmenden Prüflabors vorgenommen. Die Beurteilung der korrekten Umsetzung der Richtlinie durch ein Prüflabor sollte durch den Vergleich der Zählergebnisse aus diesem Prüflabor mit den Ergebnissen aus den beiden Referenzlabors erfolgen. Hier wird nun eine Auswertungsmethode für den Vergleich eines Prüflabors mit den beiden Referenzlabors vorgestellt, die auf einer Wurzeltransformation der Zählergebnisse basiert und mögliche Verzerrungen zwischen den Zählergebnissen der Referenzlabors sowie Korrelationen berücksichtigt. Die Umsetzung der Richtlinie wird schließlich mit Hilfe einer meta-analytischen Zusammenfassung von Teststatistiken überprüft

Adaptive confidence intervals of desired length and power for normal means

In all empirical or experimental sciences, it is a standard approach to present results, additionally to point estimates, in form of confidence intervals on the parameters of interest. The length of a confidence interval characterizes the accuracy of the whole findings. Consequently, confidence intervals should be constructed to hold a desired length. Basic ideas go back to Stein (1945) and Seelbinder (1953) who proposed a two-stage procedure for hypothesis testing about a normal mean. Tukey (1953) additionally considered the probability or power a confidence interval should possess to hold its length within a desired boundary. In this paper, an adaptive multi-stage approach is presented that can be considered as an extension of Stein's concept. Concrete rules for sample size updating are provided. Following an adaptive two-stage design of O'Brien and Fleming (1979) type, a real data example is worked out in detail

Adaptive group sequential confidence intervals for the ratio of normal means

In controlled group sequential trials, we consider the ratio of normal means as the effect measure of interest and derive group sequential nested confidence intervals on this parameter. In an interim analysis, the sample sizes of the following stages can be determined in a completely adaptive way using the unblinded data from all previously performed stages. Despite the data-dependent adaptation, the nested confidence intervals always keep the predefined confidence coefficient. Using nested confidence intervals, we make test decisions either in noninferiority or in superiority trials. However, in an interim analysis, we can change the planning from showing noninferiority to showing superiority or vice versa without affecting the predefined confidence coefficient of the nested intervals. A real data example is worked out in detail and the change in the planning from showing superiority to showing noninferiority is shown during the ongoing trial