time varing cox model in R

I am currently researching the association between menopause and the incidence of diabetes, with menopause status as a time-varying exposure in my analysis. Menopause status is a binary variable, taking values 0 and 1. I have collected data at baseline and at three follow-up time points. I also have the examination dates for all participants, which allows me to calculate follow-up times. My question pertains to the data structure: should it be in long format, and if so, should I define the follow-up times before proceeding? In a long-format dataset for my study, I typically calculate and include the follow-up times as the time intervals between each observation. These intervals represent the time that has passed since the previous observation for each participant.I have problems with how i should preaper my data for the main analysis in R . I need your help and idea

Data should be in long format

glm(formula = CHD ~ AGE, family = binomial(link = "logit"), data = chdage)

is an example that illustrates why—CHD and AGE are variables, and variables are in columns.

Follow-up times are analogous to age. See this worked example showing the conversion of ages to cohorts.

This topic was automatically closed 21 days after the last reply. New replies are no longer allowed.

If you have a query related to it or one of the replies, start a new topic and refer back with a link.