Hello,

I'm trying to choose the best way to analyze data from a longitudinal
study and I hope you can help me.

My outcome variable is continuous and was measured at two points in
time. I want to study the association of my depvar with a 3-level
independent variable adjusting for gender and age.

         depvar (T1 - T2) = indepvar (3 levels) / age sex

I've been reading the advantages of analyzing longitudianal data with
the mixed procedure, but I don't know if this will still apply for
just two repeated measures, or if I should just keep it simple using a
paired t-test / modeling the difference.

(I ran both models and the output is not the same)

Thank you for your comments or suggestions.

Greets,

C

On Dec 16, 7:54=A0am, C <claudiasatiza...@gmail.com> wrote:
> Hello,
>
> I'm trying to choose the best way to analyze data from a longitudinal
> study and I hope you can help me.
>
> My outcome variable is continuous and was measured at two points in
> time. I want to study the association of my depvar with a 3-level
> independent variable adjusting for gender and age.
>
> =A0 =A0 =A0 =A0 =A0depvar (T1 - T2) =3D indepvar (3 levels) / age sex
>
> I've been reading the advantages of analyzing longitudianal data with
> the mixed procedure, but I don't know if this will still apply for
> just two repeated measures, or if I should just keep it simple using a
> paired t-test / modeling the difference.
>
> (I ran both models and the output is not the same)
>
> Thank you for your comments or suggestions.
>
> Greets,
>
> C

I am not even sure what you are doing with a t-test?
A t-test can only compare the difference between the two time points
for your depvar, but not adjust.

 But Proc mixed makes sense to me.

proc mixed data=3Dblah... method =3D ml;
class id indepvar;
model depvar =3D  indepvar age sex(which I hope is coded 0 1 otherwise
put in class statement)/s;
random intercept/subject=3Did;
run;

On Dec 16, 5:22=A0pm, leannica <leanne.str...@gmail.com> wrote:
> On Dec 16, 7:54=A0am, C <claudiasatiza...@gmail.com> wrote:
>
>
>
>
>
> > Hello,
>
> > I'm trying to choose the best way to analyze data from a longitudinal
> > study and I hope you can help me.
>
> > My outcome variable is continuous and was measured at two points in
> > time. I want to study the association of my depvar with a 3-level
> > independent variable adjusting for gender and age.
>
> > =A0 =A0 =A0 =A0 =A0depvar (T1 - T2) =3D indepvar (3 levels) / age sex
>
> > I've been reading the advantages of analyzing longitudianal data with
> > the mixed procedure, but I don't know if this will still apply for
> > just two repeated measures, or if I should just keep it simple using a
> > paired t-test / modeling the difference.
>
> > (I ran both models and the output is not the same)
>
> > Thank you for your comments or suggestions.
>
> > Greets,
>
> > C
>
> I am not even sure what you are doing with a t-test?
> A t-test can only compare the difference between the two time points
> for your depvar, but not adjust.
>
> =A0But Proc mixed makes sense to me.
>
> proc mixed data=3Dblah... method =3D ml;
> class id indepvar;
> model depvar =3D =A0indepvar age sex(which I hope is coded 0 1 otherwise
> put in class statement)/s;
> random intercept/subject=3Did;
> run;- Hide quoted text -
>
> - Show quoted text -

one more thing in this case your depvar is one variable v1 at
timepoint 1 and v2 at timepoint 2 it doesnt look at change.

Hi,

Thank you for your help and suggestions.
Indeed you are right, what I meant by paired t-test is a repeated
measures ancova.

Best,
C


On 17 d=E9c, 02:31, leannica <leanne.str...@gmail.com> wrote:
> On Dec 16, 5:22=A0pm, leannica <leanne.str...@gmail.com> wrote:
>
>
>
> > On Dec 16, 7:54=A0am, C <claudiasatiza...@gmail.com> wrote:
>
> > > Hello,
>
> > > I'm trying to choose the best way to analyze data from a longitudinal
> > > study and I hope you can help me.
>
> > > My outcome variable is continuous and was measured at two points in
> > > time. I want to study the association of my depvar with a 3-level
> > > independent variable adjusting for gender and age.
>
> > > =A0 =A0 =A0 =A0 =A0depvar (T1 - T2) =3D indepvar (3 levels) / age sex
>
> > > I've been reading the advantages of analyzing longitudianal data with
> > > the mixed procedure, but I don't know if this will still apply for
> > > just two repeated measures, or if I should just keep it simple using =
a
> > > paired t-test / modeling the difference.
>
> > > (I ran both models and the output is not the same)
>
> > > Thank you for your comments or suggestions.
>
> > > Greets,
>
> > > C
>
> > I am not even sure what you are doing with a t-test?
> > A t-test can only compare the difference between the two time points
> > for your depvar, but not adjust.
>
> > =A0But Proc mixed makes sense to me.
>
> > proc mixed data=3Dblah... method =3D ml;
> > class id indepvar;
> > model depvar =3D =A0indepvar age sex(which I hope is coded 0 1 otherwis=
e
> > put in class statement)/s;
> > random intercept/subject=3Did;
> > run;- Hide quoted text -
>
> > - Show quoted text -
>
> one more thing in this case your depvar is one variable v1 at
> timepoint 1 and v2 at timepoint 2 it doesnt look at change.