I have an analytical dilemma wherein I have a single DV with multiple categorical and continuous IVs (one of which is a continuous IV that has multiple measurements across time). I'm not sure the best way to model for this.

Specifically, I have 60 pregnant elk from which I took monthly cortisol samples across gestation (some missing values, so 5-8 samples/female across gestation). I'm interested in how those stress measurements across gestation (along with a range of other IVs that don't vary with time, e.g., dam age, sire age, calf birthdate) influence the birth mass of each female's calf.

Any suggestions on analysis for situations where a single DV is predicted by longitudinal measures of a time-varying IV (together with non-varying IVs)?


The data seem as:

ID   DV    Month 1   Month 2  ...   Month 8     dam age, sire age, calf birthdate ...
 1  ....
 2  ....
60  ....

The problem is how to deal with cortisol measured 8 times.

Method 1: Put 8 cortisol directly into the model. Advantage is the possibility to detect the different effect of cortisol measured at the different times. Disadvantage is 1) model will be complicated. 2) consume 8 degree of freedom, especially the sample size is small as 60. 3) because of the missing values in cortisol measurements, some of the elks will be excluded.

Method 2: Derived new variables or summarize the 8 cortisol measurements into few variables. This method totally depends on the special knowledge on that field (elk pregnant). For example, just keep month 8 cortisol if you think the only last month cortisol has effect on DV. If you think that peak value of cortisol is good predictor, then create a variable with maximum of 8 cortisol. If you think that the first 4 months cortisol and the last 4 months cortisol have different effect on DV, then create two variables to present the level of cortisol at two periods.


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