A parametric study of acetabular cup design variables using finite element analysis and statistical design of experiments.

To isolate the primary variables influencing acetabular cup and interface stresses, we performed an evaluation of cup loading and cup support variables, using a Statistical Design of Experiments (SDOE) approach. We developed three-dimensional finite element (FEM) models of the pelvis and adjacent bone. Cup support variables included fixation mechanism (cemented or noncemented), amount of bone support, and presence of metal backing. Cup loading variables included head size and cup thickness, cup/head friction, and conformity between the cup and head. Interaction between and among variables was determined using SDOE techniques. Of the variables tested, conformity, head size, and backing emerged as significant influences on stresses. Since initially nonconforming surfaces would be expected to wear into conforming surfaces, conformity is not expected to be a clinically significant variable. This indicates that head size should be tightly toleranced during manufacturing, and that small changes in head size can have a disproportionate influence on the stress environment. In addition, attention should be paid to the use of nonmetal backed cups, in limiting cup/bone interface stresses. No combination of secondary variables could compensate for, or override the effect of, the primary variables. Based on the results using the SDOE approach, adaptive FEM models simulating the wear process may be able to limit their parameters to head size and cup backing.     

Genetic association studies: design,analysis and interpretation

This paper provides a review of the design and analysis of genetic association studies. In case control studies, the different contingency tables and their relationships to the underlying genetic model are defined. Population stratification is discussed, with suggested methods to identify and correct for the effect. The transmission disequilibrium test is provided as an alternative family-based test, which is robust to population stratification. The relative benefits of each analysis are summarised.     

Playback experiments: design and analysis

The scientific value of the outcome of an experiment is closely related to its design and analysis. This article deals with the design issues of pseudoreplication (whether the experimental design has the statistical features needed to answer the question as posed) and execution errors (problems arising from how the experiment was conducted). Three issues of analysis are also dealt with: the number and type of response measures to record; how measures should, and should not, be combined into a single response measure; and how to interpret an apparent lack of response. Interactive playback is considered separately because it raises its own specific design and analysis issues. Although the examples generally refer to video playback, these issues are common to all experiments in behaviour. Received: 23 September 1999 / Received in revised form: 24 February 2000 / Accepted: 25 February 2000     

The scope for improving the design of laboratory animal experiments.

The factors which need to be taken into account in designing a 'good' experiment are reviewed. Such an experiment should be unbiased, have high precision, a wide range of applicability, it should be simple, and there should be a means of quantifying uncertainty (Cox 1958). The relative precision due to the use of randomized block designs was found to range from 96% to 543% in 5 experiments involving 30 variables. However, a survey of 78 papers published in two toxicology journals showed that such designs were hardly used. Similarly, designs in which more than one factor was varied simultaneously ('factorial designs') were only used in 9% of studies, though interactions between variables such as dose and strain of animal may be common, so that single factor experiments could be misleading. The consequences of increased within-group variability due to infection and genetic segregation were quantified using data published by G?rtner (1990). Both substantially reduced precision, but toxicologists continue to use non-isogenic laboratory animals, leading to experiments with a lower level of precision than is necessary. It is concluded that there is scope for improving the design of animal experiments, which could lead to a reduction in animal use. People using animals should be required to take formal training courses which include sessions on experimental design in order to minimize animal use and to increase experimental efficiency.     

The design and interpretation of 'top-up' experiments to investigate the effects of low radiation doses

The experimental design consisting of a partial tolerance dose followed by a top-up dose, is used as a method of comparing the effects of different radiations and irradiation schedules in vivo. It complements the usual approach of giving multiple equal fractions of a single radiation type to obtain an iso-effect, as it enables low doses per fraction to be studied without the need to use a large number of fractions or a long overall time. For normal tissues in animals, the effect of X-ray doses as low as 0.1 Gy per fraction can be detected when given as 20-40 fractions followed by a top-up dose of neutrons. In order to minimize variations in the effect of the top-up dose, neutrons are used as a top-up radiation in preference to X-rays. The methods of implementing this approach are explained in detail. Analysis of the data is described, with emphasis on the Linear Quadratic model of radiation dose-fractionation. However, it is not necessary to adopt this or any particular mathematical model in order to intercompare directly the effects of different radiations or irradiation schedules using the top-up approach. Such models nevertheless simplify the design of top-up experiments. Whilst any type of radiation can in principle be used as the top-up, this is given optimally as a dose of fast neutrons split into two fractions.     

Sample Power and ExpDesign: tools for improving design of animal experiments

Proper experimental design, involving the correct number of animals, should be a basic skill for any scientist working with animals. The authors describe a university-developed and freely available tutorial program and an interactive computer-assisted learning program, both of which guide students through the steps necessary for designing animal experiments and estimating optimal sample sizes.     

比较医学动物实验计算机辅助设计系统的研发

目的开发一款辅助研究者设计比较医学动物实验方案和学习实验设计的应用软件。方法根据实验动物应用的"科学、伦理、经济"原则筛选比较医学动物实验技术资料,运用关系数据库架构原理分析和组织入选数据,通过解析比较医学动物实验规律和特点设计程序框架和模块,采用C++语言、MFC库进行面向用户的程序设计。结果建立了程序相关资源库和模型选择、实验动物、环境条件、实验步骤、方案输出5个功能模块,并完成整个软件测试。结论研发成功比较医学动物实验计算机辅助设计系统,该系统能够基于微型计算机为用户提供有效、易用的动物实验辅助设计和自助学习功能。     

ChIP-chip: considerations for the design, analysis, and application of genome-wide chromatin immunoprecipitation experiments

Chromatin immunoprecipitation (ChIP) is a well-established procedure to investigate interactions between proteins and DNA. Coupled with whole-genome DNA microarrays, ChIPS allow one to determine the entire spectrum of in vivo DNA binding sites for any given protein. The design and analysis of ChIP-microarray (also called ChIP-chip) experiments differ significantly from the conventions used for locus ChIP approaches and ChIP-chip experiments, and these differences require new methods of analysis. In this light, we review the design of DNA microarrays, the selection of controls, the level of repetition required, and other critical parameters for success in the design and analysis of ChIP-chip experiments, especially those conducted in the context of mammalian or other relatively large genomes.     

Comments on design and analysis of multiple-choice feeding-preference experiments

An approach to the analysis of multiple-choice food selection experiments proposed recently is criticised on three grounds and modified methods are suggested.     

Functional interpretation of microarray experiments

Over the past few years, due to the popularisation of high-throughput methodologies such as DNA microarrays, the possibility of obtaining experimental data has increased significantly. Nevertheless, the interpretation of the results, which involves translating these data into useful biological knowledge, still remains a challenge. The methods and strategies used for this interpretation are in continuous evolution and new proposals are constantly arising. Initially, a two-step approach was used in which genes of interest were initially selected, based on thresholds that consider only experimental values, and then in a second, independent step the enrichment of these genes in biologically relevant terms, was analysed. For different reasons, these methods are relatively poor in terms of performance and a new generation of procedures, which draw inspiration from systems biology criteria, are currently under development. Such procedures, aim to directly test the behaviour of blocks of functionally related genes, instead of focusing on single genes.     

The design and analysis of microarray experiments: applications in parasitology

Microarray experiments can generate enormous amounts of data, but large datasets are usually inherently complex, and the relevant information they contain can be difficult to extract. For the practicing biologist, we provide an overview of what we believe to be the most important issues that need to be addressed when dealing with microarray data. In a microarray experiment we are simply trying to identify which genes are the most "interesting" in terms of our experimental question, and these will usually be those that are either overexpressed or underexpressed (upregulated or downregulated) under the experimental conditions. Analysis of the data to find these genes involves first preprocessing of the raw data for quality control, including filtering of the data (e.g., detection of outlying values) followed by standardization of the data (i.e., making the data uniformly comparable throughout the dataset). This is followed by the formal quantitative analysis of the data, which will involve either statistical hypothesis testing or multivariate pattern recognition. Statistical hypothesis testing is the usual approach to "class comparison," where several experimental groups are being directly compared. The best approach to this problem is to use analysis of variance, although issues related to multiple hypothesis testing and probability estimation still need to be evaluated. Pattern recognition can involve "class prediction," for which a range of supervised multivariate techniques are available, or "class discovery," for which an even broader range of unsupervised multivariate techniques have been developed. Each technique has its own limitations, which need to be kept in mind when making a choice from among them. To put these ideas in context, we provide a detailed examination of two specific examples of the analysis of microarray data, both from parasitology, covering many of the most important points raised.     

Contributions to the design and statistical analysis of in vivo SCE experiments.

Two issues that arise in the design and statistical analysis of in vivo SCE and similar experiments are considered. First, with regard to analysis, the merits of various methods of data transformation are explored in depth. The conclusion drawn is that common transformations of the type studied here seemingly offer little advantage in the assessment of whether a test agent induces SCE in a dose-related manner. Second, a proposal is made for a method to determine, subject to budgetary constraints, the desired numbers of animals/dose group and cells scored/animal. The approach advocated also lends itself to discussions weighing the gains and losses from possible reductions in the number of animals below the 'desired' levels.     

Simulation and interpretation of experiments with otoliths

The function of otolith as a gravisensor comprises a sequence of different physical and chemical processes, which experimental investigation is difficult or unfeasible. To understand the relationship between the input and output in otolith organ, certain assumptions concerning the mechanisms of transformation of information on the inner, intermediate stages have to be made. Their validity has to be tested by comparison of the properties of output characteristics, which follow from these assumptions, with experimental data. The goals of the present paper are: 1) to analyze some of the assumptions used in and related to the intermediate stages of transformation of mechanical stimulus in neural response; 2) to demonstrate by comparison with the results of modeling of otolith structure that some peculiarities of the experiment may be caused by spatial inhomogeneity of otolithic membrane (OM).