Objectives Perimenopause significantly impacts women's health, but is under-researched due to challenges in assessing perimenopause status. Using CARDIA data, we compared the validity of six approaches for classifying perimenopause status in order to better understand the performance of classification techniques which can be applied to general cohort data. Specifically, we examined the validity of a self-reported question concerning changes in menstrual cycle length and two full prediction models using all available data concerning menstrual cycles as potential indicators of perimenopause. The validity of these three novel methods of perimenopause classification were compared to three previously established classification methods. Methods For each method, women were classified as pre- or peri-menopausal at Year 15 of follow-up (ages 32-46). Year 15 perimenopause status was then used to predict Year 20 post-menopausal status (yes/no) to estimate measures of validity and area under the curve. Results The validity of the methods varied greatly, with four having an area under the curve greater than 0.8. Conclusions When designing studies, researchers should collect the data required to construct a prediction model for classifying perimenopause status that includes age, smoking status, vasomotor symptoms, and cycle irregularities as predictors. The inclusion of additional data regarding menstrual cycles can be used to construct a full prediction model which may offer improved validity. Valid classification methods that use readily available data are needed to improve the scientific accuracy of research regarding perimenopause, promote research on this topic, and inform clinical practices.