An analysis for menstrual data with time-varying covariates

This paper concerns the analysis of menstrual data; in particular, methodology to identify variables that contribute to the variability of menstrual cycles both within and between women. The basis for the proposed methodology is a parameterization of the mean length of a menstrual cycle conditional upon the past cycles and covariates. This approach accommodates the length-bias and censoring commonly found in menstrual 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 incorrectly deleting the censored cycles.