Statistical analysis of experimental designs applied to biological assays
(2010) STAM01 20101Department of Statistics
 Abstract
 Bioassays are methods employed to estimate the effect of a given substance in living matter, and therefore they are frequently used in the pharmaceutical industry.
The experimental design of bioassays has to take into account the intrinsic variability in the biological test units and other factors as operators, day variation, batch variation, etc. Thus, statistical models employed to analyse bioassays include both fixed and random effects. The sample size estimation in mixed models is a complicated issue and no general formulas can be applied. An alternative approach to estimate the sample size to obtain a certain estimate with a confidence interval of a specific width is to perform computer simulations.
In this thesis, simulations are... (More)  Bioassays are methods employed to estimate the effect of a given substance in living matter, and therefore they are frequently used in the pharmaceutical industry.
The experimental design of bioassays has to take into account the intrinsic variability in the biological test units and other factors as operators, day variation, batch variation, etc. Thus, statistical models employed to analyse bioassays include both fixed and random effects. The sample size estimation in mixed models is a complicated issue and no general formulas can be applied. An alternative approach to estimate the sample size to obtain a certain estimate with a confidence interval of a specific width is to perform computer simulations.
In this thesis, simulations are performed to calculate the confidence interval of the logarithm of the effect of the test relative to the standard as a function of the number of replicates. The simulated data is compared with experimental data. The results obtained with the simulations agree well with the experimental ones. Furthermore, the method discussed here can also be used to analyse other experimental designs in which the size of the main sources of data variability are known. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/studentpapers/record/1620399
 author
 Rosso, Aldana ^{LU}
 supervisor

 Peter Gustafsson ^{LU}
 organization
 course
 STAM01 20101
 year
 2010
 type
 H1  Master's Degree (One Year)
 subject
 keywords
 linear regression, analysis of variance, bioassay, mixed models, biological assays
 language
 English
 id
 1620399
 date added to LUP
 20100621 12:15:30
 date last changed
 20100621 12:15:30
@misc{1620399, abstract = {Bioassays are methods employed to estimate the effect of a given substance in living matter, and therefore they are frequently used in the pharmaceutical industry. The experimental design of bioassays has to take into account the intrinsic variability in the biological test units and other factors as operators, day variation, batch variation, etc. Thus, statistical models employed to analyse bioassays include both fixed and random effects. The sample size estimation in mixed models is a complicated issue and no general formulas can be applied. An alternative approach to estimate the sample size to obtain a certain estimate with a confidence interval of a specific width is to perform computer simulations. In this thesis, simulations are performed to calculate the confidence interval of the logarithm of the effect of the test relative to the standard as a function of the number of replicates. The simulated data is compared with experimental data. The results obtained with the simulations agree well with the experimental ones. Furthermore, the method discussed here can also be used to analyse other experimental designs in which the size of the main sources of data variability are known.}, author = {Rosso, Aldana}, keyword = {linear regression,analysis of variance,bioassay,mixed models,biological assays}, language = {eng}, note = {Student Paper}, title = {Statistical analysis of experimental designs applied to biological assays}, year = {2010}, }