[R-sig-ME] lmer under "single" nests

Pierce, Steven p|erce@1 @end|ng |rom m@u@edu
Thu Aug 11 14:58:12 CEST 2022


You have what is sometimes called "sparsely clustered" data. Below are a couple methodology papers relevant to this situation.

Clarke, P. (2008). When can group level clustering be ignored? Multilevel models versus single-level models with sparse data. Journal of Epidemiology and Community Health, 62, 752-758. https://doi.org/10.1136/jech.2007.060798

McNeish, D. M. (2014). Modeling sparsely clustered data: Design-based, model based, and single-level methods. Psychological Methods, 19(4), 552-563. https://doi.org/10.1037/met0000024

Steven J. Pierce, Ph.D.
Associate Director
Center for Statistical Training & Consulting (CSTAT)
Michigan State University

-----Original Message-----
From: Elena Moreno <momae1112 using gmail.com> 
Sent: Wednesday, August 10, 2022 7:44 AM
To: r-sig-mixed-models using r-project.org
Subject: [R-sig-ME] lmer under "single" nests

Dear R-sig-mixed-models list:

I first want to thank you for your attention and willingness to help people
like me. I hope to get some light with my question:

I am applying a nested model as "(1|A/B)", having two individuals per nest
in most of the cases. However, some nests only have one individual. This is
because I'm working with kidney transplant data: there are cases when two
patients receive a kidney from the same donor (the donor gives both
kidneys), but there are cases where the donor gives just one kidney (so the
recipient doesn't share donor with anyone else).

Patients have several measures of renal function (creatinine) over time.

How does "lmer" handle this kind of situation when having some "single"
(with just one individual) nests in combination with non-single nests? It
is worth it to nest when, at most, there are only two patients per nest

If you need more details regarding the study design or even a sample of the
data, please tell me. By the way, I am not mathematician so I find
demonstrations difficult to understand but I am always eager and open to

Thank you very much and sorry for this naive question,


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