Sequential predictions of menstrual cycle lengths

Bortot, Paola; Masarotto, Guido; Scarpa, Bruno

doi:10.1093/biostatistics/kxq020

Cited by 15 publications

(55 citation statements)

References 14 publications

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“…This body of work includes modeling menstrual cycle length while recognizing its skewness, accounting for within‐woman dependency, modeling covariates association with the variance, and including covariate effects on both the standard and nonstandard menstrual cycle lengths (Harlow and Zeger, 1991; Lin, Raz, and Harlow, 1997; Guo et al, 2006). Recently, Bortot, Masarotto, and Scarpa (2010) proposed a state–space approach to model the menstrual cycle lengths with an autoregressive‐moving average model to capture the temporal dependence. None of the above‐mentioned literature have addressed the issue of association between menstrual cycle characteristics and fecundity.…”

Section: Introductionmentioning

confidence: 99%

A Joint Mixed Effects Dispersion Model for Menstrual Cycle Length and Time‐to‐Pregnancy

2012

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Menstrual cycle patterns are often used as indicators of female fecundity and are associated with hormonally dependent diseases such as breast cancer. A question of considerable interest is in identifying menstrual cycle patterns, and their association with fecundity. A source of data for addressing this question is prospective pregnancy studies that collect detailed information on reproductive aged women. However, methodological challenges exist in ascertaining the association between these two processes as the number of longitudinally measured menstrual cycles is relatively small and informatively censored by time to pregnancy (TTP), as well as the cycle length distribution being highly skewed. We propose a joint modeling approach with a mixed effects dispersion model for the menstrual cycle lengths and a discrete survival model for TTP to address this question. This allows us to assess the effect of important characteristics of menstrual cycle that are associated with fecundity. We are also able to assess the effect of fecundity predictors such as age at menarche, age, and parity on both these processes. An advantage of the proposed approach is the prediction of the TTP, thus allowing us to study the efficacy of menstrual cycle characteristics in predicting fecundity. We analyze two prospective pregnancy studies to illustrate our proposed method by building a model based on the Oxford Conception Study, and predicting for the New York State Angler Cohort Prospective Pregnancy Study. Our analysis has relevant findings for assessing fecundity.

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Section: Introductionmentioning

confidence: 99%

A Joint Mixed Effects Dispersion Model for Menstrual Cycle Length and Time‐to‐Pregnancy

2012

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“…Bortot et al . () developed a hierarchical state space model that captured skewness in the cycle lengths via an auto‐regressive moving average ARMA(1,1) model.…”

Section: Introductionmentioning

confidence: 64%

“…Our approach contrasts with that of Bortot et al . (), who did not estimate full individual level parameters for the women, given their more limited follow‐up time in their data set (Miolo et al ., ), and for similar reasons did not develop parameters to focus on the menopausal transition.…”

Section: Introductionmentioning

confidence: 99%

Modelling Menstrual Cycle Length and Variability at the Approach of Menopause by Using Hierarchical Change Point Models

Huang

Elliott

Harlow

2013

Journal of the Royal Statistical Society Series C: Applied Statistics

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SUMMARY As women approach menopause, the patterns of their menstrual cycle lengths change. To study these changes, we need to jointly model both the mean and variability of cycle length. Our proposed model incorporates separate mean and variance change points for each woman and a hierarchical model to link them together, along with regression components to include predictors of menopausal onset such as age at menarche and parity. Additional complexity arises from the fact that the calendar data have substantial missingness due to hormone use, surgery, and failure to report. We integrate multiple imputation and time-to event modeling in a Bayesian estimation framework to deal with different forms of the missingness. Posterior predictive model checks are applied to evaluate the model fit. Our method successfully models patterns of women’s menstrual cycle trajectories throughout their late reproductive life and identifies change points for mean and variability of segment length, providing insight into the menopausal process. More generally, our model points the way toward increasing use of joint mean-variance models to predict health outcomes and better understand disease processes.

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“…Further, preconception counsellors can use current methods of targeting acts of intercourse (see Scarpa and Dunson () and Bortot et al . ()) for those with predicted intercourse or fecundity problems to maximize their success in pregnancy. AUC for our TTP prediction was slightly lower than the AUC of 0.77 that was found in the TTP prediction in McLain et al .…”

Section: Summary and Discussionmentioning

confidence: 99%

Joint Analysis of Longitudinal and Survival Data Measured on Nested Timescales by Using Shared Parameter Models: An Application to Fecundity Data

McLain

Sundaram

Louis

2014

Journal of the Royal Statistical Society Series C: Applied Statistics

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Summary We consider the joint modelling, analysis and prediction of a longitudinal binary process and a discrete time-to-event outcome. We consider data from a prospective pregnancy study, which provides day level information regarding the behaviour of couples attempting to conceive. Reproductive epidemiologists are particularly interested in developing a model for individualized predictions of time to pregnancy (TTP). A couple’s intercourse behaviour should be an integral part of such a model and is one of the main focuses of the paper. In our motivating data, the intercourse observations are a long series of binary data with a periodic probability of success and the amount of available intercourse data is a function of both the menstrual cycle length and TTP. Moreover, these variables are dependent and observed on different, and nested, timescales (TTP is measured in menstrual cycles whereas intercourse is measured on days within a menstrual cycle) further complicating its analysis. Here, we propose a semiparametric shared parameter model for the joint modelling of the binary longitudinal data (intercourse behaviour) and the discrete survival outcome (TTP). Further, we develop couple-based dynamic predictions for the intercourse profiles, which in turn are used to assess the risk for subfertility (i.e. TTP longer than six menstrual cycles).

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Sequential predictions of menstrual cycle lengths

Cited by 15 publications

References 14 publications

A Joint Mixed Effects Dispersion Model for Menstrual Cycle Length and Time‐to‐Pregnancy

A Joint Mixed Effects Dispersion Model for Menstrual Cycle Length and Time‐to‐Pregnancy

Modelling Menstrual Cycle Length and Variability at the Approach of Menopause by Using Hierarchical Change Point Models

Joint Analysis of Longitudinal and Survival Data Measured on Nested Timescales by Using Shared Parameter Models: An Application to Fecundity Data

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