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Dermatological Research (dermatological + research)

Distribution by Scientific Domains
Medical Sciences100%

Selected Abstracts

Application of stereology to dermatological research

EXPERIMENTAL DERMATOLOGY, Issue 12 2009
Søren Kamp
Abstract:, Stereology is a set of mathematical and statistical tools to estimate three-dimensional (3-D) characteristics of objects from regular two-dimensional (2-D) sections. In medicine and biology, it can be used to estimate features such as cell volume, cell membrane surface area, total length of blood vessels per volume tissue and total number of cells. The unbiased quantification of these 3-D features allows for a better understanding of morphology in vivo compared with 2-D methods. This review provides an introduction to the field of stereology with specific emphasis on the application of stereology to dermatological research by supplying a short insight into the theoretical basis behind the technique and presenting previous dermatological studies in which stereology was an integral part. Both the theory supporting stereology and a practical approach in a dermatological setting are reviewed with the aim to provide the reader with the capability to better assess papers employing stereological estimators and to design stereological studies independently. [source]

Systems dermatology and clinical dermatological research

BRITISH JOURNAL OF DERMATOLOGY, Issue 1 2001
A. Oikarinen
[source]

Preliminary testing for normality: some statistical aspects of a common concept

CLINICAL & EXPERIMENTAL DERMATOLOGY, Issue 6 2006
V. Schoder
Summary Background., Statistical methodology has become an increasingly important topic in dermatological research. Adequacy of the statistical procedure depends among others on distributional assumptions. In dermatological articles, the choice between parametric and nonparametric methods is often based on preliminary goodness-of-fit tests. Aim., For the special case of the assumption of normally distributed data, the Kolmogorov,Smirnov test is the most popular choice. We investigated the performance of this test on four types of non-normal data, representing the majority of real data in dermatological research. Methods., Simulations were run to assess the performance of the Kolmogorov,Smirnov test, depending on sample size and severity of violations of normality. Results., The Kolmogorov,Smirnov test performs badly on data with single outliers, 10% outliers and skewed data at sample sizes <,100, whereas normality is rejected to an acceptable degree for Likert-type data. Conclusion., Preliminary testing for normality is not recommended for small-to-moderate sample sizes. [source]