OBJECTIVE: This study aimed to cross-validate two comparable Weibull models of prediction of age at natural menopause from two cohorts, the Scheffer, van Rooij, de Vet (SRV) cohort and the Tehran Lipid and Glucose Study (TLGS) cohort. It summarizes advantages and disadvantages of the models and underlines the need for achieving correct time dependency in dynamic variables like anti-Müllerian hormone. METHODS: Models were fitted in the original datasets and then applied to the cross-validation datasets. The discriminatory capacity of each model was assessed by calculating C-statistics for the models in their own data and in the cross-validation data. Calibration of the models on the cross-validation data was assessed by measuring the slope, intercept and Weibull shape parameter. RESULTS: The C-statistic for the SRV model on the SRV data was 0.7 (95% confidence interval (CI) 0.7-0.8) and on the TLGS data it was 0.8 (95% CI 0.8-0.9). For the TLGS model on the TLGS data, it was 0.9 (95% CI 0.8-0.9) and on the SRV data it was 0.7 (95% CI 0.6-0.8). After calibration of the SRV model on the TLGS data, the slope was 1, the intercept -0.3 and the shape parameter 1.1. The TLGS model on the SRV data had a slope of 0.3, an intercept of 12.7 and a shape parameter of 0.6. CONCLUSIONS: Both models discriminate well between women that enter menopause early or late during follow-up. While the SRV model showed good agreement between the predicted risk of entering menopause and the observed proportion of women who entered menopause during follow-up (calibration) in the cross-validation dataset, the TLGS model showed poor calibration.
OBJECTIVE: This study aimed to cross-validate two comparable Weibull models of prediction of age at natural menopause from two cohorts, the Scheffer, van Rooij, de Vet (SRV) cohort and the Tehran Lipid and Glucose Study (TLGS) cohort. It summarizes advantages and disadvantages of the models and underlines the need for achieving correct time dependency in dynamic variables like anti-Müllerian hormone. METHODS: Models were fitted in the original datasets and then applied to the cross-validation datasets. The discriminatory capacity of each model was assessed by calculating C-statistics for the models in their own data and in the cross-validation data. Calibration of the models on the cross-validation data was assessed by measuring the slope, intercept and Weibull shape parameter. RESULTS: The C-statistic for the SRV model on the SRV data was 0.7 (95% confidence interval (CI) 0.7-0.8) and on the TLGS data it was 0.8 (95% CI 0.8-0.9). For the TLGS model on the TLGS data, it was 0.9 (95% CI 0.8-0.9) and on the SRV data it was 0.7 (95% CI 0.6-0.8). After calibration of the SRV model on the TLGS data, the slope was 1, the intercept -0.3 and the shape parameter 1.1. The TLGS model on the SRV data had a slope of 0.3, an intercept of 12.7 and a shape parameter of 0.6. CONCLUSIONS: Both models discriminate well between women that enter menopause early or late during follow-up. While the SRV model showed good agreement between the predicted risk of entering menopause and the observed proportion of women who entered menopause during follow-up (calibration) in the cross-validation dataset, the TLGS model showed poor calibration.
Entities:
Keywords:
AGE AT MENOPAUSE; ANTI-MÜLLERIAN HORMONE; CROSS-VALIDATION STUDY; PREDICTION
Authors: Samuel W Golenbock; Lauren A Wise; Geralyn M Lambert-Messerlian; Elizabeth E Eklund; Bernard L Harlow Journal: Womens Midlife Health Date: 2020-09-01
Authors: A C de Kat; Y T van der Schouw; M J C Eijkemans; G C Herber-Gast; J A Visser; W M M Verschuren; F J M Broekmans Journal: BMC Med Date: 2016-10-03 Impact factor: 8.775