Hello Stata Forum,
I hope you all are doing well!
I wanted to clarify my utilization of the mixed linear model and whether it would more appropriate to say do an ANOVA or control for other variables.
I have a single-arm study evaluating the efficacy of a weight loss drug, drug A, in reducing a calculated weight score at 3 months from pre-intervention (month4). There are 10 study subjects as shown below with weight scores recorded at months 1, 2, and 3 (all recorded) after administration.
My objective is to determine if there is a significant difference between pre-intervention (month1) and post-intervention at 3 months (month4). Rather than complete a simple paired-sample T-test, I wanted to trend the change over time.
I first converted to long format from my data collection:
reshape long month, i(subject) j(time)
then used the repeated measures mixed model with random effects for time
mixed month time || subject:, var reml
margins, at (time=(1(1)4))
marginsplot, x(time)
Computing standard errors:
Mixed-effects REML regression Number of obs = 40
Group variable: subject Number of groups = 10
Obs per group:
min = 4
avg = 4.0
max = 4
Wald chi2(1) = 22.83
Log restricted-likelihood = 1.9729634 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
month | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
time | -.1075 .0224969 -4.78 0.000 -.1515932 -.0634068
_cons | 1.6035 .0848196 18.90 0.000 1.437257 1.769743
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
subject: Identity |
var(_cons) | .0542298 .0285948 .0192934 .1524288
-----------------------------+------------------------------------------------
var(Residual) | .0253056 .0066456 .0151245 .0423402
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 21.80 Prob >= chibar2 = 0.0000
I see it is significant but I also get a fixed portion prediction - am I controlling for time incorrectly? Also, would I be best served performing an ANOVA?
Thank you all
I hope you all are doing well!
I wanted to clarify my utilization of the mixed linear model and whether it would more appropriate to say do an ANOVA or control for other variables.
I have a single-arm study evaluating the efficacy of a weight loss drug, drug A, in reducing a calculated weight score at 3 months from pre-intervention (month4). There are 10 study subjects as shown below with weight scores recorded at months 1, 2, and 3 (all recorded) after administration.
My objective is to determine if there is a significant difference between pre-intervention (month1) and post-intervention at 3 months (month4). Rather than complete a simple paired-sample T-test, I wanted to trend the change over time.
| subject | month1 | month2 | month3 | month4 |
| 1 | 1.91 | 1.50 | 1.66 | 1.45 |
| 2 | 1.50 | 1.20 | 1.10 | 0.90 |
| 3 | 1.64 | 2.03 | 1.50 | 1.44 |
| 4 | 2.03 | 2.00 | 1.60 | 1.50 |
| 5 | 1.49 | 1.65 | 1.55 | 1.67 |
| 6 | 1.65 | 1.50 | 1.45 | 1.33 |
| 7 | 1.38 | 1.32 | 1.10 | 1.40 |
| 8 | 1.55 | 1.50 | 1.34 | 1.20 |
| 9 | 1.39 | 1.55 | 1.67 | 1.20 |
| 10 | 1.24 | 1.10 | 0.90 | 0.60 |
reshape long month, i(subject) j(time)
then used the repeated measures mixed model with random effects for time
mixed month time || subject:, var reml
margins, at (time=(1(1)4))
marginsplot, x(time)
Computing standard errors:
Mixed-effects REML regression Number of obs = 40
Group variable: subject Number of groups = 10
Obs per group:
min = 4
avg = 4.0
max = 4
Wald chi2(1) = 22.83
Log restricted-likelihood = 1.9729634 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
month | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
time | -.1075 .0224969 -4.78 0.000 -.1515932 -.0634068
_cons | 1.6035 .0848196 18.90 0.000 1.437257 1.769743
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
subject: Identity |
var(_cons) | .0542298 .0285948 .0192934 .1524288
-----------------------------+------------------------------------------------
var(Residual) | .0253056 .0066456 .0151245 .0423402
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 21.80 Prob >= chibar2 = 0.0000
I see it is significant but I also get a fixed portion prediction - am I controlling for time incorrectly? Also, would I be best served performing an ANOVA?
Thank you all
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