1 edition of Modeling and statistical analysis of Medaka bioassay data found in the catalog.
Modeling and statistical analysis of Medaka bioassay data
Donald Paul Gaver
by Naval Postgraduate School, Available from National Technical Information Service in Monterey, Calif, Springfield, Va
Written in English
A histopathologic examination of tissues from Oryzias laptipes (Japanese medaka fish) was performed to evaluate the carcinogenic potential of tricholoroethylene (TCE) in groundwater. The data were reported by Experimental Pathology Laboratories, Inc., in a report dated Jan. 19, 1990, submitted to the Army Biomedical Research and Development Laboratory, Ft. Detrick, MD. This paper provides a brief statistical analysis of some aspects of those data. The analysis does not reveal a strong positive relationship between TCE concentration over the range considered and probability (risk or hazard) of incurring at least one end point manifestation (here cystic degeneration or liver neoplasm) in a fish. Uncertainties in the point estimates are assessed by bootstrapping. Both non-parametric (weak statistical assumptions) and parametric (stronger statistical assumptions) analyses give similar inconclusive dose- response indications. A brief discussion is included of a biologically-based mathematical model that is likely to form an appropriate basis for more sophisticated data analysis. One contribution of this paper is to discuss and illustrate techniques for quantitative analysis of other similar data. The methods can also be used to assist in choosing an experimental design. Binomial Distribution; Censored data; Generalized linear model; Bootstrap.
|Statement||Donald P. Gaver, Patricia A. Jacobs|
|Contributions||Jacobs, Patricia A., Naval Postgraduate School (U.S.). Dept. of Operations Research|
|The Physical Object|
|Pagination||i, 34 p. :|
|Number of Pages||34|
Here we report our application of mixed-effects modeling for the normalization and statistical analysis of bead-based immunoassay data. Our data set consisted of bead-based immunoassay measurements . A stochastic model is proposed to describe time-dependent lethal effects of toxic compounds. It is based on simple mechanistic assumptions and provides a measure for the toxicity of a chemical compound, .
Normalizing the data with mixed-effects models can benefit downstream analysis of the data using mechanistically oriented network-level modeling methods such as those based on differential . A Bayesian approach to the analysis of quantal bioassay studies using nonparametric mixture models Kassandra Fronczyk1 ;and Athanasios Kottas2 1Department of Statistics, Rice University, Houston, .
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A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the : Donald Gaver. Quantal Bioassay Response is discrete Often a binary response: e.g.
Dead/Alive Dose response function is sometimes called “Tolerance Distribution” A logistic distribution is a natural model for such data 48. The query is that, based on developmental bioassay data I have chosen Parallel curve 4 PL model for analysis of data. since majority (90%) of the developmental bioassay data was symmetrical, well.
: SOME STATISTICAL ANALYSIS OF BIOLOGICAL DATA WITH PREDICTIVE MODELING: SOME STATISTICAL ANALYSIS OF BIOLOGICAL DATA (): Amritasu. The book will be a valuable source of information to students in the experimental area of statistical aspects of biological assay, professional statisticians with an interest in research in this Cited by: Statistical analysis 01/ STATISTICAL ANALYSIS OF RESULTS OF BIOLOGICAL ASSAYS AND TESTS 1.
INTRODUCTION This chapter provides guidance for the design of. Nonlinear Quantitative Response Assay. A nonlinear quantitative response assay is a full curve fit method which takes the whole dose-response relationship into consideration, including asymptotes.
UNISTAT’s optional Analysis of Bioassays module is a comprehensive implementation of United States Pharmacopoeia chapters >, >, > and 81> () and European Pharmacopoeia (. The database format is the most natural way of representing the bioassay data.
Doses, Dilutions and Potency. When analysing bioassay data using one of four bioassay methods (Parallel Line. Statistical Analysis of Bioassays, based on hazard modeling J.J.M. Bedaux and S.A.L.M. Kooijman Vrije Universiteit, de BoelelaanHV Amsterdam Environmental and Ecological statistics, 1.
The CLSI guidelines provide a solid methodology for validating an assay against the chosen parameters. 7 Although specialized software is available for analyzing the data from assay.
Human Respiratory Tract Model (HRTM). Gregoratto et al. () proposed a physiologically-based particle transport model that simplifies significantly the representation of particle clearance from the. Comment from the Stata technical group. William Dupont’s Statistical Modeling for Biomedical Researchers, Second Edition is ideal for a one-semester graduate course in biostatistics and.
We have developed statistical methods for certifying spectrum-like quantities using analysis of variance for functional type data, and have developed a splicing algorithm for combining curves, or connecting. Bioassay Analysis Using R: Abstract: We describe an add-on package for the language and environment R which allows simultaneous fitting of several non-linear regression models.
The focus is on analysis. Little provides business partner with SAS Institute teaching SAS courses in Design of Experiments, Statistical Quality Control, Nonlinear Modeling, Stability Analysis, JMP Scripting language, ESDA. are represented in the data. The statistical analysis of the data takes these variables into account and therefore objective conclusions can be drawn from the data.
Statistical tools are widely used within the. The high affinity and specificity of biological receptors determine the demand for and the intensive development of analytical systems based on use of these receptors.
Therefore, theoretical concepts. Standardize antibody concentration calculations and analysis. Finney has written the seminal text on statistical methods applied to bioassay design and analysis and is an excellent reference tool.
As. Either approach can bias the results and, in the case of parallel line bioassay, lead to unnecessary assay failure. This article demonstrates that a better statistical technique, Tobit analysis, can account.
ANALYSIS OF RADIOLIGAND ASSAY DATA 3   Statistical Analysis of Radioligand Assay Data By D. RODBA~D and G. R. FRAZlER The widespread use of large-scale radioimmunoassays (RIA) and re- lated techniques (saturation assays, competitive protein binding assays, or radioligand assays in general) has led to the development of numerous methods for routine d a t a .Statistical Analysis of Biomarker Data Greg Pond Ph.D., Ontario Clinical Oncology Group.
Escarpment Cancer Research Institute. Department of Oncology, McMaster University. Level II .If possible, do a power analysis to determine a good sample size for the experiment. Do the experiment. Examine the data to see if it meets the assumptions of the statistical test you chose (primarily .