longitudinal analysis using lmer?

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?

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/

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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