A key assumption underpinning generalized linear models (which linear regression is a type of) is the independence of observations. In longitudinal data this will simply not hold. Observations within an individual (between time points) are likely to be more similar than those between individuals.

支持广义线性模型 (线性回归是一种类型)的关键假设是观测的独立性。 在纵向数据中，这根本不成立。 个人内部(时间点之间)的观察可能比个人之间的观察更相似。

So, how do you deal with this? One option is to fit a generalized linear mixed model in which there are random intercept and slope terms for each individual. This will tell you for a specific individual (i.e. conditional on the random intercept and slope) what is the effect of a variable on an outcome. However, this isn’t very useful if you are concerned with the marginal effect, i.e. what is the effect of a variable on an outcome on average i