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- Repeated Measures Longitudinal Regression in JMP Pro 13

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Jan 5, 2017 10:08 AM
(2491 views)

JMP Pro 13 allows assigning up to two Subject terms for the Exchangeable Structure of the Repeated Structure tab in the Mixed Model dialog. Can anyone give me an __example__ where two subject terms would be appropriate or preferred for repeated measures longitudinal regression? In particular, I'm wondering whether I could use ID and Eye as Subject terms for the partial dataset listed below (where Eye is nested within ID) and Year_Cat as the repeated term. Or should I use only ID as the Subject term and concatenate "Eye" and "Year_Cat" as the Repeated term? Thanks in advance.

ID | EYE (right=1, left=2) | YEAR_CAT |

589 | 1 | 0 |

589 | 1 | 1 |

589 | 1 | 2 |

589 | 1 | 3 |

589 | 1 | 4 |

589 | 2 | 0 |

589 | 2 | 1 |

589 | 2 | 2 |

589 | 2 | 3 |

589 | 2 | 4 |

670 | 1 | 0 |

670 | 1 | 1 |

670 | 1 | 2 |

670 | 1 | 3 |

670 | 1 | 4 |

670 | 1 | 5 |

670 | 1 | 6 |

670 | 2 | 0 |

670 | 2 | 1 |

670 | 2 | 2 |

670 | 2 | 3 |

670 | 2 | 4 |

670 | 2 | 5 |

670 | 2 | 6 |

5 REPLIES

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As an example, in measuring the effects on achievement scores of students where students are nested within schools, two subject terms--school and students within a school--would be appropriate.

In your case you can specify the random subject effects as shown below and leave the default Residual for Repeated Structure:

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Yes I understand that. But I have been advised to use an exchangeable structure and avoid using random effects code. So can I use both ID and Eye as Subject terms and Year_Cat as the Repeated term, all within the repeated structure tab? Or should I use only ID as the Subject term and concatenate "Eye" and "Year_Cat" as the Repeated term? The latter is what Julian Parris had recommended to me for JMP Pro 11 a couple of years ago.

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"The difference between the two options is how measurements taken from right eye and left eye should be characterized. I think they are from different subjects rather time points."

Each ID has data from two eyes (or only one eye if data from the other eye are missing). And each eye of a given ID has multiple time points. For example, ID 1 might have right eye — year 0, year 1, year 3, and year 5 — and left eye — year 0, year 1, year 3, and year 5.

So which of your two options would be preferable for me?

Thanks,

Michael

Each ID has data from two eyes (or only one eye if data from the other eye are missing). And each eye of a given ID has multiple time points. For example, ID 1 might have right eye — year 0, year 1, year 3, and year 5 — and left eye — year 0, year 1, year 3, and year 5.

So which of your two options would be preferable for me?

Thanks,

Michael

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If I believe the difference in the measurement between the left eye and right eye is random then I would use the first option. If not, then the second.