Segmented polynomials for incidence rate estimation from prevalence data

Severin Guy Mahiané, Oliver Laeyendecker

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


The study considers the problem of estimating incidence of a non remissible infection (or disease) with possibly differential mortality using data from a(several) cross-sectional prevalence survey(s). Fitting segmented polynomial models is proposed to estimate the incidence as a function of age, using the maximum likelihood method. The approach allows automatic search for optimal position of knots, and model selection is performed using the Akaike information criterion. The method is applied to simulated data and to estimate HIV incidence among men in Zimbabwe using data from both the NIMH Project Accept (HPTN 043) and Zimbabwe Demographic Health Surveys (2005–2006).

Original languageEnglish (US)
Pages (from-to)334-344
Number of pages11
JournalStatistics in Medicine
Issue number2
StatePublished - Jan 30 2017


  • incidence rate
  • maximum likelihood estimation
  • model selection
  • mortality
  • prevalence
  • segmented polynomials

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability


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