AN ANALYSIS FOR MENSTRUAL DATA WITH TIME-VARYING COVARIATES

This paper concerns the analysis of menstrual data; in particular, met hodology to identify variables that contribute to the variability of m enstrual cycles both within and between women. The basis for the propo sed methodology is a parameterization of the mean length of a menstrua l cycle conditional upon the past cycles and covariates. This approach accommodates the length-bias and censoring commonly found in menstrua l data. Data from a longitudinal study of menstrual patterns and other variables among Lese women of the Ituri Forest, Zaire, illustrate the methodology. A small simulation illustrates the bias caused by incorr ectly deleting the censored cycles.