I am new to longitudinal data analysis, and currently learning by myself to be able to carry out hypothesis testing.
I have a short longitudinal data in terms of limited repeated measures (only baseline and time-1 post baseline are available). Also there is a group indicator ("studygroup") to suggest control versus intervention group. The goal of analysis is to evaluate if the trend of outcome (from baseline to time-1) differs between groups (control vs. intervention).
Variables:
- outcome: meanscore_sds (calculated based on a scale, ranging from 1-5)
- time variable: time (0: baseline; 1: time-1)
- group indicator: studygroup (0: control; 1: intervention)
- sex: (0: boy; 1: girl)
- id variable: surid
Code:
global restrict = "t1dropcase == 0 & t2dropcase == 0" // limit to eligible analytical sample mixed meanscore_sds time##studygroup if $restrict || surid: , residual(uns, t(time)) var ml
Code:
Wald chi2(3) = 17.65 Log likelihood = -6160.3764 Prob > chi2 = 0.0005 --------------------------------------------------------------------------------- meanscore_sds | Coef. Std. Err. z P>|z| [95% Conf. Interval] ----------------+---------------------------------------------------------------- 2.time | .0660626 .0308159 2.14 0.032 .0056645 .1264606 | studygroup | Intervention | .0048707 .03559 0.14 0.891 -.0648845 .0746259 | time#studygroup | 2#Intervention | .0393069 .0432313 0.91 0.363 -.045425 .1240387 | _cons | 4.276469 .0253691 168.57 0.000 4.226747 4.326191 --------------------------------------------------------------------------------- ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ surid: Identity | var(_cons) | .0984436 1.990941 6.00e-19 1.61e+16 -----------------------------+------------------------------------------------ Residual: Unstructured | var(e1) | .682875 1.991058 .0022516 207.1039 var(e2) | .5772798 1.991008 .0006693 497.893 cov(e1,e2) | .0536583 1.990978 -3.848587 3.955904 ------------------------------------------------------------------------------ LR test vs. linear model: chi2(3) = 123.58 Prob > chi2 = 0.0000
- coefficient for "time": among control group, outcome change at time1 compared to baseline
- coefficient for "studygroup": outcome in intervention compared to control control at baseline
- but I am not sure how to interpret the coefficient for interaction term. Is it the difference of baseline-time1 trend between two groups? Or is it the difference of outcome at time1 between intervention and control?
Best,
Mengmeng
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