Jürgen Läuter

Comprehensive Survey of the Relationship Between Serum Concentration and Therapeutic Effect of Amitriptyline in Depression

The relationship between serum concentration (Cs) of amitriptyline and its therapeutic effect in depression has been investigated frequently over the last 3 decades; however, the results were controversial and no consensus was reached. Therefore, we have performed a comprehensive survey and meta-analysis of the subject. All relevant literature was included, and the design of studies on the serum concentration-therapeutic effect relationship (SCTER) of amitriptyline was evaluated. Pooled original data from SCTER studies with adequate design were analysed by various statistical methods: regression analysis of therapeutic effect and Cs; comparison of the mean therapeutic effect in various ranges of Cs; dichotomisation of outcome and analysis according to sensitivity of receiver operation curves; frequency of responders and nonresponders in ranges determined by points of sensitivity; analysis of the distribution of Cs in responders and nonresponders; logistic regression of responders and nonresponders with Cs and other independent variables; calculation of effect size (g) and mean effect size (gm).Forty-five SCTER studies of amitriptyline were identified, and 27 studies met the minimum criteria of adequate study design. Inadequate study design predicted the finding of no SCTER. Analysis of the pooled data from studies with adequate design confirmed a therapeutic window of the sum of Cs of amitriptyline and its active metabolite nortriptyline of about 80 to 200 µg/L. A moderate and significant positive gm (0.538, 95% confidence interval 0.167 to 0.909) was calculated for treatment with Cs within the therapeutic window in comparison with treatment with Cs outside the therapeutic window (19 studies with adequate design and original data available, n = 583).In conclusion, the evidence for a biphasic SCTER of amitriptyline in depression is considerably improved, and the results may help to find a consensus in the future. However, the clinical benefit of therapeutic drug monitoring of amitriptyline can only be demonstrated in a controlled and randomised study. Furthermore, the results provide further evidence that antidepressants at optimum Cs are superior to placebo in the treatment of depression.

MULTIVARIATE TESTS OF NORMAL MEAN VECTORS WITH RESTRICTED ALTERNATIVES

ABSTRACT In this paper, we consider tests for the hypothesis that the mean vector is zero against one-sided alternatives when the observation vectors are independently and identically distributed as normal with unknown covariance matrix. The exact null-distribution of the tests is derived. The tests generalize the centre-direction test proposed by Tang et al.[1] for known covariance. In addition, the modification is order- and scale-invariant. Power comparisons with some other tests are presented. It can be shown that the null distribution of the test statistic holds for data arising from any elliptical distribution, not just the normal distribution.

Multivariate Many-To-One Procedures with Applications to Preclinical Trials

Comparisons of several treatments with a control represent a standard situation in preclinical trials. Usually, they are considered with a single variable, resulting in multiple test procedures such as the Dunnett test (1). Here, the multivariate many-to-one problem is considered, where several variables are observed on each individual of the control and treatment groups. Classical MANOVA tests and their derivatives for the many-to-one problem require large sample sizes in order to be powerful if the dimension is high. In this paper, a new class of stabilized multivariate tests proposed by Läuter (2) and Läuter, Glimm, and Kropf (3) is extended to this special design. The new tests are based on linear scores which are derived in a certain way from the original variables. They utilize factorial relations among the variables. It is shown here that the procedures keep the multiple level. In simulation experiments several versions of multivariate tests are compared with each other. Standard approaches are included as well as different score versions and a comparison of Dunnett-like procedures with Bonferroni-type procedures. Generally, an improved power of the new tests compared to standard procedures is demonstrated.

A theorem on the principal components inference

A method of multivariate data compression and dimension reduction is established, which is based on principal components and avoids all overfitting effects. This method allows the use of ‘compressed’ data for exact level-alpha tests of hypotheses on the mean vectors. It is a particularity of the method that the coefficients of the constructed linear scores depend solely on the residual sums of products matrix; the empirical means are not necessary to determine the compression. Thus, novel and very simple confidence regions of the unknown multivariate mean vectors are also obtained. The method can be combined with strategies of selecting variables. Furthermore, multiple testing procedures are derived, which serve for finding all sets of variables with deviations from the null hypothesis. The methods are evaluated by computer simulations.

Detection of Pairwise Correlations in a Multivariate Structure

Proposals are given for testing the independence between two sets of variables, (x 1 ,…,x p ) and (y 1 ,…,y p ), where correlations are suspected only between variables with the same subscripts and not so much across different subscripts. A permutation test and two parametric tests are proposed and compared with each other in simulation studies. The methods are demonstrated in two typical applications including a situation with pre-processed data, where a proposal is given for a correlation analysis based on factor scores.

Stable Multivariate Procedures — Strategies and Software Tools

In 1996, Lauter proposed a new class of exact multivariate parametric tests for small samples of high-dimensional observations, which has been extended in subsequent papers. Here we give an overview on the two main strategies — the use of special linear scores in standard parametric tests and the construction of new tests based on random matrices with a uniform left-spherical matrix distribution. It is shown how these tests can be integrated into standard packages. In particular, SPSS macros are offered.

Celebrating Fifty Years of the Biometrical Journal

The publication of Volume 50 of the Biometrical Journal (formerly Biometrische Zeitschrift) in 2008 provides the perfect opportunity to describe the history of the present journal. We report on the long period of preparation for the journal within the German Region (DR) of the International Biometric Society (IBS). A special paragraph is dedicated to the first ten volumes. We emphasize the role of the journal as a bond between German biometricians on both sides of the border between the two German nations and IBS regions at that time. Furthermore, we report on the development of its thematic spectrum and impact factors and provide citation frequencies.

On the admissibility of stable spherical multivariate tests

This paper deals with correlation tests from the class of spherical tests introduced by Lauter (Biometrics 52 (1996) 964). These methods provide an alternative to classical MANOVA approaches and are particularly useful in small samples. Following a brief introduction of the spherical tests, it is shown that the so-called principal component correlation test is admissible in this class. A Bayesian approach is used to prove this result.

Data Compression and Selection of Variables, with Respect to Exact Inference

In many applications of statistics, we are confronted with a large number p of dif-ferent variables whereas the number n of independent individuals remains limited.The classical multivariate tests like Wilks’ Λ or Hotelling’s T 2 test do not attain sufficient power under these circumstances because they cannot take into account special parameter structures. Often overfitted parameter estimations and unstable behaviour arise, and confirmatory data analysis becomes difficult. We will nevertheless intend to investigate the multivariate data by exact statistical tests. Methods of dimension reduction and data compression are preferentially used.

Hochdimensionale statistische Analysen zur Charakterisierung klinischer Zustände

Im Rahmen eines Forschungsprojektes zur „Analyse hirnfunktioneller und kognitiver Storungen und ihrer Veranderungen unter Psychopharmakotherapie“, an der verschiedene medizinische Disziplinen beteiligt sind, werden psychiatrisch Kranke und gesunde Probanden mit einem umfangreichen Methodenspektrum untersucht, um die pathophysiologischen Mechanismen dieser Krankheiten aufzudecken und Moglichkeiten der Diagnoseunterstutzung und der Verlaufskontrolle unter Therapie zu erkunden.