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 (donor)? 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 learn. Thank you very much and sorry for this naive question, Elena
lmer under "single" nests
3 messages · Elena Moreno, Pierce, Steven, Ben Bolker
1 day later
Elena, 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 at gmail.com> Sent: Wednesday, August 10, 2022 7:44 AM To: r-sig-mixed-models at 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 (donor)? 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 learn. Thank you very much and sorry for this naive question, Elena
11 days later
The 2008 reference looks a little bit naive/old-fashioned to me; the 2014 paper looks more useful. The answers to your questions (what methods should you use etc.) will depend on the answers to some of these questions: * are you more interested in fixed/population-level effects or in the variance components? (The former are easier.) * how many groups do you have at both levels? (I think but am not not quite sure that 'B' represents donors and 'A' represents some higher-level grouping variable [hospital etc.]?) * how many observations *per group*? (i.e. having 1-2 kidneys per donor, but 4-5 measurements per kidney, is much better than having a single kidney per donor) * the responses are continuous/will be treated as Gaussian? That makes things *much* easier/better than if they were binary outcomes (which is sort of a worst-case scenario)
On Thu, Aug 11, 2022 at 8:58 AM Pierce, Steven <pierces1 at msu.edu> wrote:
Elena, 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 at gmail.com> Sent: Wednesday, August 10, 2022 7:44 AM To: r-sig-mixed-models at 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 (donor)? 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 learn. Thank you very much and sorry for this naive question, Elena [[alternative HTML version deleted]]
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