Document Detail


A method for assessing age-time disease incidence using serial prevalence data.
MedLine Citation:
PMID:  9423255     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
This paper considers nonparametric estimation of age- and time-specific trends in disease incidence using serial prevalence data collected from multiple cross-sectional samples of a population over time. The methodology accounts for differential selection of diseased and undiseased individuals resulting, for example, from differences in mortality. It is shown that when a log-linear incidence odds model is adopted, an EM algorithm provides a convenient method for carrying out maximum likelihood estimation, primarily using existing generalized linear models software. The procedure is quite general, allowing a range of age-time incidence models to be fitted under the same framework. Furthermore, by making use of existing software for fitting generalized additive models, the procedure can be generalized with virtually no extra complexity to allow maximization of a penalized likelihood for smooth nonparametric estimation. Automatic choice of smoothing level for the penalized likelihood estimates is discussed, using generalized cross-validation. The method is applied to a data set on serial toxoplasmosis prevalence, which has previously been analyzed under the assumption of nondifferential selection. A variety of age-time incidence models are fitted, and the sensitivity to plausible differential selection patterns is considered. It is found that nonmultiplicative models are unnecessary and that qualitative incidence trends are fairly robust to differential selection.
Authors:
I C Marschner
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Publication Detail:
Type:  Journal Article; Research Support, U.S. Gov't, P.H.S.    
Journal Detail:
Title:  Biometrics     Volume:  53     ISSN:  0006-341X     ISO Abbreviation:  Biometrics     Publication Date:  1997 Dec 
Date Detail:
Created Date:  1998-02-04     Completed Date:  1998-02-04     Revised Date:  2007-11-14    
Medline Journal Info:
Nlm Unique ID:  0370625     Medline TA:  Biometrics     Country:  UNITED STATES    
Other Details:
Languages:  eng     Pagination:  1384-98     Citation Subset:  IM    
Affiliation:
Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA.
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Biometry / methods*
Cohort Studies
Cross-Sectional Studies
England / epidemiology
Epidemiologic Methods*
Humans
Incidence*
Likelihood Functions
Models, Statistical*
Multivariate Analysis
Prevalence*
Probability
Toxoplasmosis / epidemiology
Grant Support
ID/Acronym/Agency:
AI24643/AI/NIAID NIH HHS

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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