Fitting a multiplicative incidence model to age- and time-specific prevalence data

Ian C. Marschner

doi:10.2307/2532889

Fitting a multiplicative incidence model to age- and time-specific prevalence data

Ian C. Marschner^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

14 Citations (Scopus)

Abstract

We discuss the assessment of age- and time-specific disease incidence rising prevalence data. A method is described for conveniently fitting a discrete-time multiplicative model, subject to positivity constraints, using the EM-algorithm. Together with smoothing, it allows essentially nonparametric assessment of incidence trends. The method is illustrated using previously analyzed data on toxoplasmosis.

Original language	English
Pages (from-to)	492-499
Number of pages	8
Journal	Biometrics
Volume	52
Issue number	2
DOIs	https://doi.org/10.2307/2532889
Publication status	Published - Jun 1996
Externally published	Yes

Keywords

Cross-sectional data
EM-algorithm
Incidence and prevalence
Multiplicative age time models
Smoothing
Toxoplasmosis

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10.2307/2532889

Cite this

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abstract = "We discuss the assessment of age- and time-specific disease incidence rising prevalence data. A method is described for conveniently fitting a discrete-time multiplicative model, subject to positivity constraints, using the EM-algorithm. Together with smoothing, it allows essentially nonparametric assessment of incidence trends. The method is illustrated using previously analyzed data on toxoplasmosis.",

keywords = "Cross-sectional data, EM-algorithm, Incidence and prevalence, Multiplicative age time models, Smoothing, Toxoplasmosis",

author = "Marschner, \{Ian C.\}",

year = "1996",

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AB - We discuss the assessment of age- and time-specific disease incidence rising prevalence data. A method is described for conveniently fitting a discrete-time multiplicative model, subject to positivity constraints, using the EM-algorithm. Together with smoothing, it allows essentially nonparametric assessment of incidence trends. The method is illustrated using previously analyzed data on toxoplasmosis.

KW - Cross-sectional data

KW - EM-algorithm

KW - Incidence and prevalence

KW - Multiplicative age time models

KW - Smoothing

KW - Toxoplasmosis

UR - https://www.scopus.com/pages/publications/0029893358

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JO - Biometrics

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IS - 2

ER -

Fitting a multiplicative incidence model to age- and time-specific prevalence data

Abstract

Keywords

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