BookmarkSubscribeRSS Feed
Melk
Lapis Lazuli | Level 10

I have pre-post intervention data on a very small number of subjects (n=7).

 

The coutcome is continuous; measurements were taken across time at pre- and post-intervention for each patient. Time was not measured consistently, so 1 patient may have measurements at 3, 5, 7, and 9 while another may have 1, 5, 6, and 8.

 

My research question is: Is the intervention effective in lowering the outcome measurement? Although time was measured, outcome measurements over time is actually not part of our hypothesis.

 

What statistical test would I use to best assess this data? Would time be a time-varying covariate here?

2 REPLIES 2
StatsMan
SAS Super FREQ

Yes, TIME would most likely be a within-subject covariate in your model.  PROC GLIMMIX might be a good choice for the model if you feel that your sample size is large enough.  You can use the RANDOM statement to model the correlations between the observations on the same subject on the r-side and use one of the spatial structures (perhaps TYPE=SP(POW)) to account for the unequally spaced time points you observe.  

 

If all of the subjects received the same treatment, then perhaps comparing their baseline response to a response near the max observed time would be of interest?  You could model change from baselineas your response or write a CONTRAST statement to compare the response at the times of interest to you.  If you have a control group that received no treatment or more than one treatment group, then you could compare the results across the treatments or the treatment vs control using the LSMEANS statement with the AT option to set the TIME value of interest.  

 

The EFFECT statement could be used to set up a spline effect on TIME.  That might be especially useful given the small number of subjects you have.  

 

Melk
Lapis Lazuli | Level 10

Thank you so much for your response. Is GLIMMIX generally appropriate for such a smalle sample of n=7?

If so, would this tell me if my post was significantly lower in y than my pre?

 

proc glimmix data=dat;
  class Trial subject;
      model y = Trial | time / solution;
      random intercept / subject=subject;
      random intercept time / type=sp(pow) subject=subject*Trial;
run;
quit;

 

 

where trial = pre or post.

sas-innovate-2024.png

Join us for SAS Innovate April 16-19 at the Aria in Las Vegas. Bring the team and save big with our group pricing for a limited time only.

Pre-conference courses and tutorials are filling up fast and are always a sellout. Register today to reserve your seat.

 

Register now!

What is ANOVA?

ANOVA, or Analysis Of Variance, is used to compare the averages or means of two or more populations to better understand how they differ. Watch this tutorial for more.

Find more tutorials on the SAS Users YouTube channel.

Discussion stats
  • 2 replies
  • 1446 views
  • 1 like
  • 2 in conversation