Abstract
ABSTRACT In this paper, we study the problem of heterogeneity in cervical mucus hydration at different times relative to the mucus peak both between cycles and women, specifying and estimating appropriate multilevel latent class models for longitudinal data. We estimate multilevel and growth latent class models which classify women on the basis of the evolution of cervical mucus characteristics observed over the fertile period of each menstrual cycle taking into account that we observe a different number of cycles per woman and correlation over time between consecutive observations. The effect of potential covariates on mucus evolution patterns is as well evaluated. Results confirm the existence of heterogeneity in mucus evolution between cycles and women. Moreover, an important significant effect of a woman’s age is found.
Highlights
The observation of the cervical mucus symptom (CMS) is a widely used indicator to identify ovulation and the fertile phase in a menstrual cycle (e.g., Billings et al 1972).The mucus symptom allows a woman to precisely define the beginning of this phase and informs on the event of ovulation because cervical mucus secretions, stimulated by a rise in oestrogen, are known to increase in volume about 5-6 days prior to ovulation (Katz, Slade, and Nakajima 1997; Moghissi, Syner, and Evans 1972)
We dispose of a two-level dataset: observed cervical mucus characteristics in menstrual cycles of women and we address the measurement of heterogeneity in trajectories following two ways
We estimate multilevel latent class models, which classify women on the basis of the evolution of cervical mucus characteristics observed over the fertile period of each menstrual cycle, taking into account that we observe a different number of cycles per woman and that there exits heterogeneity between cycles
Summary
The observation of the cervical mucus symptom (CMS) is a widely used indicator to identify ovulation and the fertile phase in a menstrual cycle (e.g., Billings et al 1972). We estimate multilevel latent class models, which classify women on the basis of the evolution of cervical mucus characteristics observed over the fertile period of each menstrual cycle, taking into account that we observe a different number of cycles per woman and that there exits heterogeneity between cycles. The estimation of a multilevel latent class growth mixture model (Gomes and Dias 2015) classifies women, again on the basis of observed mucus evolution in the fertile phase of their cycles, which is assumed to follow a latent trajectory with random coefficients; this allows to analyse more in depth the effect of the age of the women on mucus heterogeneity. As age increases, variation of mucus type over the fertile window diminishes
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