Flexible parametric survival models built on age-specific antimullerian hormone percentiles are better predictors of menopause

FR Tehrani, MA Mansournia, M Solaymani-Dodaran, Ewout Steyerberg, F Azizi

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Objective: This study aimed to improve existing prediction models for age at menopause. Methods: We identified all reproductive aged women with regular menstrual cycles who met our eligibility criteria (n = 1,015) in the Tehran Lipid and Glucose Study-an ongoing population-based cohort study initiated in 1998. Participants were examined every 3 years and their reproductive histories were recorded. Blood levels of antimullerian hormone (AMH) were measured at the time of recruitment. Age at menopause was estimated based on serum concentrations of AMH using flexible parametric survival models. The optimum model was selected according to Akaike Information Criteria and the realness of the range of predicted median menopause age. Results: We followed study participants for a median of 9.8 years during which 277 women reached menopause and found that a spline-based proportional odds model including age-specific AMH percentiles as the covariate performed well in terms of statistical criteria and provided the most clinically relevant and realistic predictions. The range of predicted median age at menopause for this model was 47.1 to 55.9 years. For those who reached menopause, the median of the absolute mean difference between actual and predicted age at menopause was 1.9 years (interquartile range 2.9). Conclusions: The model including the age-specific AMH percentiles as the covariate and using proportional odds as its covariate metrics meets all the statistical criteria for the best model and provides the most clinically relevant and realistic predictions for age at menopause for reproductive-aged women.
Original languageUndefined/Unknown
Pages (from-to)676-681
Number of pages6
JournalMenopause. The Journal of the North American Menopause Society
Issue number6
Publication statusPublished - 2016

Research programs

  • EMC NIHES-02-65-01

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