I am trying to estimate a linear relation between body temperature and body mass, and I have a sample of measurements from subjects, with most subjects having one measurement, but several subjects having two measurements in different times.

In order to estimating the slope 95% confidence interval, I could have used the usual formula, but since my data is partially correlated (due to having two measurements from the same subjects, for some of the subjects), I understand this will not be correct. So I was told it is better to use bootstraping technique.

My question is: How do I use bootstrapping while accounting for the fact that some of the subjects were sampled twice?

(In fact, there is an additional twist here, since I am using Passing-Bablok linear model, due to the nature of data; but answers related to ordinary linear regression, or even bootstrapping of 1 variable alone, are welcome as well).

I am trying to estimate a linear relation between body temperature and body mass, and I have a sample of measurements from subjects, with most subjects having one measurement, but several subjects having two measurements in different times.

In order to estimating the slope 95% confidence interval, I could have used the usual formula, but since my data is partially correlated (due to having two measurements from the same subjects, for some of the subjects), I understand this will not be correct. So I was told it is better to use bootstraping technique.

My question is: How do I use bootstrapping while accounting for the fact that some of the subjects were sampled twice? Should I just take those subjects with 1/2 the probability of other subjects? Is that a common practice, and if so - how is it called? (possibly "Inverse Probability Bootstrap Sampling"?)

(In fact, there is an additional twist here, since I am using Passing-Bablok linear model, due to the nature of data; but answers related to ordinary linear regression, or even bootstrapping of 1 variable alone, are welcome as well).