Dynamic analysis of recurrent event data with missing observations, with application to infant diarrhoea in Brazil

Abstract:

This paper examines and applies methods for modelling longitudinal binary data subject to both intermittent missingness and dropout. The paper is based around the analysis of data from a study into the health impact of a sanitation programme carried out in Salvador, Brazil. Our objective was to investigate risk factors associated with incidence and prevalence of diarrhoea in children aged up to 3 years old. In total, 926 children were followed up at home twice a week from October 2000 to January 2002 and for each child daily occurrence of diarrhoea was recorded. A challenging factor in analysing these data is the presence of between-subject heterogeneity not explained by known risk factors, combined with significant loss of observed data through either intermittent missingness (average of 78 days per child) or dropout (21% of children). We discuss modelling strategies and show the advantages of taking an event history approach with an additive discrete time regression model. © 2007 Board of the Foundation of the Scandinavian Journal of Statistics.

Año de publicación:

2007

Keywords:

• Diarrhoea incidence and prevalence
• Discrete time martingales
• DROPOUT
• Longitudinal binary data
• Additive regression model
• Missing data

Fuente:

scopus