[R-sig-ME] longitudinal analysis using lmer?

[Murray Jorgensen]

Andrew Gelman at least seems to glory in the ability of random effects
models to cope with cases having meagre data. See the discussion in the
Gelman/Hell book on the Radon data and Lac Qui Parle County.

Murray Jorgensen

Emmanuel Charpentier wrote:
> Le lundi 31 août 2009 à 12:53 -0400, Robert Terwilliger a écrit :
>> Thanks everyone for all the advice.
>>
>> One question I have (maybe there will be more...... :-P ):
>> Should I exclude subjects that have only 1 or 2 data points?
> 
> The question you shuld try to answer is "*Why* do they have only 1 or 2
> points ?".
> 
> HTH,
> 
> 					Emmanuel Charpentier
> 
> 
>> On Fri, Aug 28, 2009 at 7:07 PM, Ken Beath<ken-PJqznCQlsrTvnOemgxGiVw at public.gmane.org> wrote:
>>> On 29/08/2009, at 6:07 AM, Robert Terwilliger wrote:
>>>
>>>> One more thing........
>>>>
>>>> What i sent was only a small sample of the data, just for the purpose
>>>> of showing what kind of set we have.
>>>>
>>>> We have about 150 subjects, with starting ages between 8 and 21, with
>>>> 3-5 data points (yearly visits) per subject.
>>>>
>>> This will be fine, although it isn't as good as having a smaller number of
>>> complete series. One point is that they don't look completely linear, so a
>>> polynomial (maybe quadratic) or regression spline may be a better option.
>>> Judging by the scatter the random effect variance will probably be close to
>>> zero.
>>>
>>> Ken
>>>
>>>
>>>
>>>
>>>> Thanks,
>>>>
>>>> --
>>>> Robert Terwilliger
>>>> Physicist
>>>> Laboratory of Neurocognitive Development
>>>> Western Psychiatric Institute and Clinic
>>>> University of Pittsburgh Medical Center
>>>> Loeffler Building
>>>> 121 Meyran Avenue  #114
>>>> Pittsburgh, PA 15213
>>>> 412.383.8174  - Office
>>>> 412.383.8179 - Fax
>>>> em: raterwil at gmail.com
>>>> http://www.wpic.pitt.edu/research/lncd/
>>>>
>>>> *******************************************************
>>>> Dear R mixed effects gurus,
>>>>
>>>> I have the following data below. Attached is a png graphic
>>>> representing the data.
>>>>
>>>> I would like to run the following analysis:
>>>>
>>>> signal ~ age | subject.
>>>>
>>>> For your information (not statically relevant), the "signal" variable
>>>> is from a functional MRI experiment.
>>>>
>>>> At issue is whether this analysis is valid using "lme". From the graph
>>>> (and the table below), one can see that there are five subjects.
>>>> However, each subject begins at a different age. Subject 1 begins at 8
>>>> and goes to 12, while subject 5 begins at 14. From my study of
>>>> longitudinal analysis, usually each subject begins at the same
>>>> starting point, while these data have subjects beginning at different
>>>> starting points (different ages).
>>>>
>>>> Any insight is appreciated.
>>>>
>>>> subject age     signal
>>>> 1       8       0.108
>>>> 1       9       0.139
>>>> 1      10    NA
>>>> 1       11      0.151
>>>> 1       12      0.148
>>>> 2       10      0.127
>>>> 2      11    NA
>>>> 2       12      0.135
>>>> 2       13      0.146
>>>> 3       9       0.105
>>>> 3       10      0.123
>>>> 3       11      0.134
>>>> 3       12      0.151
>>>> 3       13      0.145
>>>> 4       12      0.130
>>>> 4       13      0.169
>>>> 4       14      0.146
>>>> 4       15      0.174
>>>> 5       14      0.158
>>>> 5       15      0.141
>>>> 5       16      0.178
>>>> 5      17    NA
>>>> 5       18      0.172
>>>>
>>>> _______________________________________________
>>>> R-sig-mixed-models at r-project.org mailing list
>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>>
>>>
>>
>>
> 
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-- 
Dr Murray Jorgensen      http://www.stats.waikato.ac.nz/Staff/maj.html
Department of Statistics, University of Waikato, Hamilton, New Zealand
Email: maj at waikato.ac.nz                                Fax 7 838 4155
Phone  +64 7 838 4773 wk    Home +64 7 825 0441   Mobile 021 0200 8350