Linked Life Data

7696819

Source:http://linkedlifedata.com/resource/pubmed/id/7696819

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
1995-4-28
pubmed:abstractText
In this paper we use a stochastic model for the HIV epidemic in homosexual populations to characterize the HIV infection and seroconversion. Using computer generated data, we compare the fitting of infection distributions and of seroconversion distributions by different parametric models as well as by nonparametric methods. The nonparametric methods include the Kaplan-Meier method, EMS method, Bacchetti's method, and the spline approximation. The parametric models include most of the models which have been used in the literature. The comparison criteria are the chi-square statistic, the AIC (Akaike Information Criterion) and the residual sums of squares. The numerical results suggest that for the proportional mixing pattern, the EMS method, the spline method, Bacchetti's method, and the generalized log-logistic distributions with three and with four parameters provide better fitting for the infection and the seroconversion distributions in most cases. For the restricted mixing patterns, the EMS method, the spline method, Bacchetti's method, and some mixtures of distributions provide close fitting to the infection and the seroconversion distributions.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Mar
pubmed:issn
0025-5564
pubmed:author
pubmed:issnType
Print
pubmed:volume
126
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
81-123
pubmed:dateRevised
2009-11-11
pubmed:meshHeading
pubmed:year
1995
pubmed:articleTitle
Characterization of HIV infection and seroconversion by a stochastic model of the HIV epidemic.
pubmed:affiliation
Department of Mathematical Sciences, University of Memphis, Tennessee.
pubmed:publicationType
Journal Article, Comparative Study, Research Support, U.S. Gov't, P.H.S.