Hello everyone,
I have a question about baseline adjustment . . .
Do random intercept models (RI) or random coefficient models (RC) account for group differences in the baseline value of the outcome? I'm not asking the general question of whether or not we should control for baseline values (there is a lot of literature on this), but rather, with RI or RC models is it even necessary?
Suppose in a clinical trial, where outcome is measured on multiple occasions over time, randomization did not achieve balance between treatment and control groups on the initial value (Y0), is it necessary to control for the baseline value of the outcome in a RI or RC model? Is it redundant? Does it improve precision or efficiency? (Let's assume the outcome is continuous, rather than categorical).
I'd appreciate any opinions.
Thanks,
Paul
___________________________________
Paul F. Visintainer, PhD
Springfield, MA 01199
----------------------------------------------------------------------
CONFIDENTIALITY NOTICE: This email communication and any attachments may contain confidential and privileged information for the use of the designated recipients named above. If you are not the intended recipient, you are hereby notified that you have received this communication in error and that any review, disclosure, dissemination, distribution or copying of it or its contents is prohibited. If you have received this communication in error, please reply to the sender immediately or by telephone at (413) 794-0000 and destroy all copies of this communication and any attachments. For further information regarding Baystate Health's privacy policy, please visit our Internet web site at http://www.baystatehealth.com.
*
* For searches and help try:
* http://www.stata.com/help.cgi?search
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
