The Application of Statistical Methods of Meta-Analysis for Heterogeneity Modelling in Medicine and Pharmacy, Psychology, Quality Control and Assurance

Dankmar Böhning (), Uwe Malzahn, Peter Schlattmannn, Uwe-Peter Dammann (), Wolfgang Mehnert (), Heinz Holling () and Ralf Schulze
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Dankmar Böhning: Free University Berlin, Department of Epidemiology
Uwe Malzahn: Free University Berlin, Department of Epidemiology
Peter Schlattmannn: Free University Berlin, Department of Epidemiology
Uwe-Peter Dammann: Asta-Medica, Werk Künsebeck
Wolfgang Mehnert: Free University Berlin, Department of Pharmaceutical Technology
Heinz Holling: University of Münster, Occupational Psychology
Ralf Schulze: University of Münster, Occupational Psychology

A chapter in Mathematics — Key Technology for the Future, 2003, pp 533-553 from Springer

Abstract: Abstract In the past few years meta-analysis has become increasingly popular in many areas of science such as medicine and pharmacy, psychology and other social sciences. In these areas of application meta-analyses have been performed in order to obtain a pooled estimate of various single studies. Obtaining a single summary measure implicitly assumes homogeneity of these studies, i.e. the results of individual studies differ only by chance. In this case a combined estimate of the individual studies provides a powerful and important result. However this pooled estimate may be seriously misleading if study conditions are heterogenous. Thus, increasingly an approach has been advocated which considers meta- analysis as a study over studies. This approach seeks to investigate heterogeneity between studies. An important feature of this type of meta-analysis lies in the fact that it tries to identify factors which cause heterogeneity. It has been the focus on this project (in corporation with the unit of quality assurance of ASTA Medica at location Künsebeck) to extend this approach appropriately to the area of quality control, where batches of the produced goods replace the role of studies in medicine or the social sciences. Clearly, in this setting an investigation of heterogeneity is equally attractive, since identification and modelling of heterogeneity helps to improve the production process.

Keywords: Mixture Model; Control Chart; Mixture Distribution; Defective Item; Nonparametric Maximum Likelihood (search for similar items in EconPapers)
Date: 2003
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DOI: 10.1007/978-3-642-55753-8_43

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