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I have 3 measurement points, N=4000, N=2000, N=3000 (week 0, week 6, week 12).

My goal is to estimate the relative importance of 14 regressors on one dependent variable. Now I can do that separately for each time point, but the N are comparably small to estimate relative regressor importance with such a huge number of regressors.

So I thought about simply "pooling" participants, pretending the data are cross-sectional, and using N=4000+2000+3000. Obviously this violates the assumption of independence of observations, so I should not do that.

Alternatives? I can't use a regression in which I simply add time as regressor because the bottleneck week 6 (only N=2000) will lead to listwise deletion of 50% of the subjects.

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    $\begingroup$ Have you thought about linear mixed models or marginal models? $\endgroup$
    – Davide
    Dec 18 '13 at 14:30
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Fourteen predictors is usually not an unworkable number with 2,000 - 4,000 observations, so a separate regression at each time point does not seem out of the question. Also, it's not a foregone conclusion that if you include time as a 15th predictor you have to use listwise deletion. Have you found that the data are missing in such a way that pairwise deletion would lead to unsound findings?

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  • $\begingroup$ 14 predictors for the regression is fine. After that I run relative importance analysis, and bootstrap CIs of the RelaImpo estimates. These CIs are pretty large (due to correlation structure of predictors and the way they are bootstrapped). N=2k is not large enough for this analysis. The missings are weird. There is about 25% dropout from week 0 to week 12, and week 6 seems to be just a smaller sample (reanalysis don't know why that is). I don't know of any other way than listwise deletion in regression analysis with multiple timepoints. What would you suggest? Thank you $\endgroup$
    – Torvon
    Jan 21 '13 at 12:22
  • $\begingroup$ Sounds as if 3 separate regressions are the way to go, and that you'd want to condense your 14 correlated predictors into a smaller number of more less correlated ones. Whether it's best do that via judgment-based selection, or PCA, or EFA is hard for me to say. $\endgroup$
    – rolando2
    Jan 21 '13 at 20:44
  • $\begingroup$ My dissertation is about a higher resolution of symptoms instead of simplifying with EFA/PCA/CFA/LCA, so I cannot do that. I need to do this regression longitudinal, minimizing loss of subjects. What happens when I simply go with this: "y ~ x1 + x2 + x3 ... + age + sex + TIME" instead of running "y ~ x1 + x2 + x3 ... + age + sex" per measurement point ? Is that feasible? $\endgroup$
    – Torvon
    Jan 22 '13 at 13:51

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