Linked Life Data

9032413

Source:http://linkedlifedata.com/resource/pubmed/id/9032413

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
1997-3-13
pubmed:abstractText
This paper considers methods of statistical analysis for highly skewed immune response data. Observations from population studies of immunological variables are rarely normally distributed between individuals; typically the distribution shows extreme levels of skewness. In some situations, skewness remains considerable even after transforming the data. Using resampling techniques, applied to several actual datasets of ELISA assay data, we consider the robustness of normal parametric methods, e.g. t tests and linear regression. Despite the skewness of the transformed data, we demonstrate that such methods are quite robust depending on the number of observations, type of analysis and severity of skewness. We also illustrate how bootstrap resampling can be used to provide a valid alternative method of analysis that can be used either for checking normal parametric analysis or as a direct method of analysis. We illustrate this combined approach by analysing real data to test for association between human serum antibodies to malaria merozoite surface proteins, MSP1 and MSP2, and resistance to clinical malaria, and confirm the protective effect of antibodies to MSP1 and demonstrated a similar protective effect for some antibodies to MSP2.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
0022-1759
pubmed:author
pubmed:issnType
Print
pubmed:day
14
pubmed:volume
201
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
99-114
pubmed:dateRevised
2009-9-29
pubmed:meshHeading
pubmed:year
1997
pubmed:articleTitle
Statistical analysis of highly skewed immune response data.
pubmed:affiliation
Institute of Cell, Animal and Population Biology, University of Edinburgh, UK. dmcg@srv0.bio.ed.ac.uk
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't