Dear mixed model experts, We have a dataset of older adults. We measured their mental health (MH) 6 months before retirement and again 12 months post retirement. At both of these time points we also measured their physical activity (PA) (min/day), income (INC) and general health (GH). We would like to create a model that tells us if change in physical activity over the retirement threshold predicts change in mental health, and we'd like to use the model to predict how much mental health is predicted to change when physical activity is increased from perhaps 15 minutes to 60 minutes. We'd like to use a mixed model rather than just using change (difference) scores. And we'd like to control for things like change in general physical health and change in income. This is what the data look like *ID time MH PA GH INC* 01 pre 4 15 56 560 02 pre 5 30 30 1200 .. ..... .. .. .. ... 01 post 7 40 50 50 02 post 8 45 30 0 I'm not sure how best to build the model. Something like this? model <- lmer (MH ~ PA * time + GH + INC + (1|participant.ID) ) Thank you in advance. Dot
model specification for repeated measure
3 messages · Dot Dumuid, Thierry Onkelinx
Dear Dot, The specification of your covariates seems reasonable to me. You need to check if the Gaussian distribution is relevant for your measurements on mental health. Best regards, ir. Thierry Onkelinx Statisticus / Statistician Vlaamse Overheid / Government of Flanders INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND FOREST Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance thierry.onkelinx at inbo.be Havenlaan 88 bus 73, 1000 Brussel www.inbo.be /////////////////////////////////////////////////////////////////////////////////////////// To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey /////////////////////////////////////////////////////////////////////////////////////////// 2018-02-01 11:37 GMT+01:00 Dot Dumuid <haveaballphysio at gmail.com>:
Dear mixed model experts,
We have a dataset of older adults. We measured their mental health (MH) 6
months before retirement and again 12 months post retirement.
At both of these time points we also measured their physical activity (PA)
(min/day), income (INC) and general health (GH).
We would like to create a model that tells us if change in physical
activity over the retirement threshold predicts change in mental health,
and we'd like to use the model to predict how much mental health is
predicted to change when physical activity is increased from perhaps 15
minutes to 60 minutes. We'd like to use a mixed model rather than just
using change (difference) scores. And we'd like to control for things like
change in general physical health and change in income.
This is what the data look like
*ID time MH PA GH INC*
01 pre 4 15 56 560
02 pre 5 30 30 1200
.. ..... .. .. .. ...
01 post 7 40 50 50
02 post 8 45 30 0
I'm not sure how best to build the model. Something like this?
model <- lmer (MH ~ PA * time + GH + INC + (1|participant.ID) )
Thank you in advance.
Dot
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Thank you! On Thu, Feb 1, 2018 at 10:54 PM, Thierry Onkelinx <thierry.onkelinx at inbo.be> wrote:
Dear Dot, The specification of your covariates seems reasonable to me. You need to check if the Gaussian distribution is relevant for your measurements on mental health. Best regards, ir. Thierry Onkelinx Statisticus / Statistician Vlaamse Overheid / Government of Flanders INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND FOREST Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance thierry.onkelinx at inbo.be Havenlaan 88 bus 73, 1000 Brussel www.inbo.be //////////////////////////////////////////////////////////// /////////////////////////////// To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey //////////////////////////////////////////////////////////// /////////////////////////////// 2018-02-01 11:37 GMT+01:00 Dot Dumuid <haveaballphysio at gmail.com>:
Dear mixed model experts, We have a dataset of older adults. We measured their mental health (MH) 6 months before retirement and again 12 months post retirement. At both of these time points we also measured their physical activity
(PA)
(min/day), income (INC) and general health (GH). We would like to create a model that tells us if change in physical activity over the retirement threshold predicts change in mental health, and we'd like to use the model to predict how much mental health is predicted to change when physical activity is increased from perhaps 15 minutes to 60 minutes. We'd like to use a mixed model rather than just using change (difference) scores. And we'd like to control for things
like
change in general physical health and change in income.
This is what the data look like
*ID time MH PA GH INC*
01 pre 4 15 56 560
02 pre 5 30 30 1200
.. ..... .. .. .. ...
01 post 7 40 50 50
02 post 8 45 30 0
I'm not sure how best to build the model. Something like this?
model <- lmer (MH ~ PA * time + GH + INC + (1|participant.ID) )
Thank you in advance.
Dot
[[alternative HTML version deleted]]
_______________________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models