# Menopause data An example data set with interval censored data and competing risks. The data come from Cycle I of the Health Examination Survey of the National Center for Health Statistics, and contain information on the menopausal status of 2423 women (MacMahon and Worcestor, 1966). data ```r data(menopause) ``` ## Format A matrix containing 2423 rows and 4 columns. Each row (x1,x2,y1,y2) corresponds to a subject in the study. The interval (x1,x2] contains the unobservable age of menopause X. The interval [y1,y2] contains the type of menopause Y, where Y=1 represents operative menopause and Y=2 represents natural menopause. We use the value 100 to represent infinity. ## Details The Health Examination Survey used a nationwide probability sample of people between age 18 and 79 from the United States civilian, noninstitutional population. The participants were asked to complete a self-administered questionnaire. The sample contained 4211 females, of whom 3581 completed the questionnaire. We restrict attention to the age range 25-59 years. Furthermore, seven women who were less than 35 years of age and reported having had a natural menopause were excluded as being an error or abnormal. The remaining data set contains information on 2423 women. MacMahon and Worcestor (1966) found that there was marked terminal digit clustering in the response of this question, especially for women who had a natural menopause. Therefore, Krailo and Pike (1983) decided to only consider the menopausal status of women at the time of the questionnaire, yielding current status data on the age of menopause with two competing risks: operative menopause and natural menopause. ## Source MacMahon and Worcestor (1966). Age at menopause, United States 1960 - 1962. **National Center for Health Statistics. Vital and Health Statistics**, volume 11, number 19. ## References Krailo and Pike (1983). Estimation of the distribution of age at natural menopause from prevalence data. **American Journal of Epidemiology** 117 356-361. ## See Also `menopauseMod` ## Examples ```r # Load the data data(menopause) # Compute the MLE mle <- computeMLE(R=menopause, B=c(0,1,1,1)) # Plot first sub-distribution function P(X<=x, 0.5

menopause.rd function