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From |
"Joseph Coveney" <stajc2@gmail.com> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
Re: st: Re: three-level gllamm - variable as a nesting variable or a predictor? |

Date |
Tue, 8 Oct 2013 14:31:43 +0900 |

Lisa Marie Yarnell wrote: Thanks, Joseph. The two segments of the study are ascending and descending halves of a drinking experience; we are studying alcoholism. A person in the ascent (Segment A) has different physiological and mental symptoms than in the descent (Segment B). So perhaps this is like repeated measures and suggests a two-level model, as you said? But it is not a cross-over study per se, with two different treatments. It is just the two halves of a drinking experience. In the Stata output, when I constructed the model (again with 305 observations placed into either Segment A or Segment B, nested within 31 individuals), the Stata output showed: number of level 1 units = 305 number of level 2 units = 62 number of level 3 units = 31 I was confused at first because there are only really 2 levels of Level 2 (Segment A and Segment B). I wondered why the output shows 62 units at Level 2. But my thought was that Stata recognizes the three-level structure that I specified, and knowing that the Level 2 units are nested in 31 Level 1 persons, it indicates 62 units at Level 2? Can you explain why the Stata output would show 62 units at Level 2? Frankly, the way I had specified this model, I am not sure how I would depict this graphically--it seems mixed-up in some place. I will think about the alternative model that you suggested: [omitted] -------------------------------------------------------------------------------- Take a glance at my earlier post's mention of uniqueness to understand what's going on here. By analogy, it's as if you have selected 31 families that have exactly two children each, and that have named one child "A" and the other "B". Despite the families' children's identical names, they are all unique: the child named "A" of the Family ID 1234 is not the same as the child named "A" of Family ID 5678, and you would assign separate IDs to the two children who share the same name; moreover, the two children, "A" & "B", within a family are exchangeable (conditional on covariates of interest, if any) in that they can be viewed as randomly drawn from the populations that they represent--a (hypothetical) population of children of the one family and a separate (hypothetical) population of children of the other family. That is not what you have in your alcoholism study. Segment A for Study Participant No. 1234 is not unique, but rather is the same as Segment A for Study Participant No. 5678. Both Segments A are conceptually the same, because Study Segment A is as operationally defined in your study's protocol. Likewise for Study Segment B. You don't conceive of Study Segments A and B for Study Participant 1234 as unique, exchangeable members of a Population of Study Segments belonging exclusively to (nested under) Study Participant 1234. Why does -gllamm- show 62 units at Level 2? It is because behind the scenes -gllamm- generates 62 unique Level-2 IDs in accordance with your model specification in the same way that nested factors were (are still) generated in conventional ANOVA: if the user doesn't already specify 62 unique IDs for all of the levels of the nested factor, then the software generates them internally by forming an interaction term of the higher (nesting) factor and the lower (nested) factor, and then dropping the main effects term for the nested factor, as in -anova . . . group / patient_id|group- . . . It makes no sense to test the differences between levels of the nested factor, because the nested factor level indicator is viewed as just a counter (in lieu of an ID number) for the number of unique individuals under a given particular level of the nesting factor. In your three-level model specification, the variable *segment* is a counter variable (and A and B are arbitrary units of a counter) to indicate how many study segments Study Participant 1234 has, how many Study Participant 5678 has and so on. You specified a three-level hierarchy, and to -gllamm-, in your case, each study participant just happens to have exactly two nested (unique, exchangeable) study segments randomly drawn from the participant's own population of study segments. That's why you happen to have exactly double the number of second-level units as top-level units. Again, I recommend that you reconsider your -gllamm- model specification as two-level, with segment as a repeated-measures variable (cross-over indicator variable), optionally with a random slope (or two random intercepts, one for each segment type). I believe that proper graphical depiction will become more readily apparent and that the model will not seem so mixed up. Joseph Coveney * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: three-level gllamm - variable as a nesting variable or a predictor?***From:*Lisa Marie Yarnell <lisa.yarnell@utexas.edu>

**st: Re: three-level gllamm - variable as a nesting variable or a predictor?***From:*"Joseph Coveney" <stajc2@gmail.com>

**Re: st: Re: three-level gllamm - variable as a nesting variable or a predictor?***From:*Lisa Marie Yarnell <lisa.yarnell@utexas.edu>

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