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In cohort studies of change over time (for instance change in repeatedly measured weight after a dietary intervention, using linear mixed models or GEE), what is the detriment of including individuals with only one data-point?

Does the answer change depending on whether GEE or GLMM is used?

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  • $\begingroup$ +1 Not sure if coincidence or if you are following-up in a new thread for clarity's sake, but I am wondering this exact same question in the comments here: stats.stackexchange.com/questions/358231/… $\endgroup$
    – Mark White
    Commented Jul 24, 2018 at 1:15
  • $\begingroup$ Also, shouldn't the title read more than one, per the question in the body of the post? $\endgroup$
    – Mark White
    Commented Jul 24, 2018 at 2:45
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    $\begingroup$ Repeated measures doesn’t equate, sadly, to participants having complete or valid measurements each time. The question is partly what to do with these individuals. I get that conceptually they shouldn’t be used in GLMM, but GEE is “population change.” Also I wondered more so the harm of including them, because many people are including single data points. $\endgroup$
    – bobmcpop
    Commented Jul 24, 2018 at 12:22
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    $\begingroup$ @A.Donda imagine we send texts to people that are various questions. A lot of people only answer 1, but other people answer 2 or 3. How do we model the dependency between the answers of those people that answered 2 or 3 as repeated measures, when we still have lots of people that have single observations? $\endgroup$
    – Mark White
    Commented Jul 24, 2018 at 12:46
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    $\begingroup$ Thanks. I figured out what was happening in my multilevel model case. I will try to answer your question (as a way of making sure I understand the answer to my question) this evening! $\endgroup$
    – Mark White
    Commented Jul 26, 2018 at 15:16

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