Hi SAS Users, I am trying to do a multilevel mediation analysis using a 2x4 RCT design, where there are 2 conditions (control vs. experimental) and 4 time points (baseline, 1 month, 3 month and 6 month). A cut of the person-time dataset is shown below. Obs Idnum cond time Mediator Outcome 1 20001 1, control 0 Baseline 40 6.75 2 20001 1, control 1 month 38 6.75 3 20001 1, control 3 month 38 7.75 4 20001 1, control 6 month 20 5.50 5 20005 1, control 0 Baseline 6 3.25 6 20005 1, control 1 month 4 2.25 7 20005 1, control 3 month 2 2.50 8 20005 1, control 6 month 12 1.75 9 20006 1, control 0 Baseline 12 6.00 10 20006 1, control 1 month 14 6.00 11 20006 1, control 3 month 12 6.50 12 20006 1, control 6 month 12 5.25 13 20007 1, control 0 Baseline 10 7.75 14 20007 1, control 1 month 8 7.00 15 20007 1, control 3 month 2 7.00 16 20007 1, control 6 month 6 7.50 17 20008 2, experimental 0 Baseline 34 5.00 18 20008 2, experimental 1 month 40 7.50 19 20008 2, experimental 3 month 38 5.25 20 20008 2, experimental 6 month 40 4.25 21 20009 1, control 0 Baseline 12 3.75 22 20009 1, control 1 month 2 3.25 23 20009 1, control 3 month 20 4.75 24 20009 1, control 6 month 20 4.00 25 20010 2, experimental 0 Baseline 20 6.25 26 20010 2, experimental 1 month 20 5.75 27 20010 2, experimental 3 month 18 2.25 28 20010 2, experimental 6 month 10 4.75 The intervention cond id coded as 0 and 1; putative mediator and outcome were measured at baseline and all follow ups. Is there a way to evaluate mediation effects using this longitudinal data set with time varying measures? If so, what is the best way of measuring the direct and indirect effects? I used a random residual statement to account for within subject correlation, but the issue is that this solution of fixed effects is the marginal means. I need to evaluate the temporal effects as well, i.e., intervention -> mediates the intermediary putative mediator between 1 and 3 month -> changes outcome at 6 month. This may be complicated but any help is much appreciated!
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