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.
Murphy
S A
SA
Department of Statistics, Pennsylvania State University, University Park, USA.
Bentley
G R
GR
O'Hanesian
M A
MA
D000293:Adolescent; D000328:Adult; D015982:Bias; D015023:Democratic Republic of the Congo; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008597:Menstrual Cycle; D008875:Middle Aged; D015233:Models, Statistical; D012016:Reference Values; D012621:Seasons