Date:         Mon, 20 Feb 2012 09:14:19 -0600
Reply-To:     Robin R High <rhigh@UNMC.EDU>
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         Robin R High <rhigh@UNMC.EDU>
Subject:      Re: Non-inferiority Study
Comments: To: June Gothberg <jgothberg@YAHOO.COM>
In-Reply-To:  <201202190337.q1I5lBqf006060@waikiki.cc.uga.edu>
Content-Type: text/plain; charset="US-ASCII"

June,

What level of venue is your "traditional standard" and how many alternate
venues are there? (as your data have 4 1s, a 2 and a 3) And how many
observations per venue do you actually have?

Robin High
UNMC





From:
June Gothberg <jgothberg@YAHOO.COM>
To:
SAS-L@LISTSERV.UGA.EDU
Date:
02/18/2012 09:38 PM
Subject:
Non-inferiority Study
Sent by:
"SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>



I have some questions on running a non-inferiority study outside of
clinical
trials.

First, I am having trouble determining a cut-score for the study I am
running and cannot find direction in the literature other than the
references to set the null proportion difference to -.10 or in a few cases
-.05. I am struggling to determine the range of meaningful difference.

Second, I am measuring venue and whether collecting data in an alternate
venues is "not appreciably worse" than the traditional standard for four
different variables. For example, I measured the number of participant
words
per idea. Thus my data is continuous, and I am confused as to how to code
this. Here is a truncated version of my data with the variables of
interest
being venue and wordidea:

data noninf;
input subject $ gender $ state $ group $ venue $ ideas words wordidea;
cards;
1 2 1 2 1 4 82    20.50
2 2 1 2 1 6 115   19.17
3 2 1 2 1 3 37    12.33
4 2 1 2 1 8 101   12.63
16 2 2 3 2 18 2217  123.17
30 1 3 1 3 6 485   80.83
;
run;

I tried reformatting my data:

data noninf;
input v1_wi v2_wi;
cards;
20.50 123.17 19.17 80.83; /*with more cards*/
run;


proc ttest data=noninf dist=normal tost(-10, 10);
paired v1_wi*v2_wi;
run;

with these results:


The TTEST Procedure

Difference:  v1_wi - v2_wi

                  N        Mean     Std Dev     Std Err     Minimum
Maximum

                  9    -26.2180     40.9299     13.6433      -113.4
26.7048

                     Mean       95% CL Mean        Std Dev      95% CL Std
Dev

                 -26.2180    -57.6795   5.2434     40.9299     27.6464
78.4122

                              TOST Level 0.05 Equivalence Analysis

                              Lower                        Upper
                     Mean     Bound      90% CL Mean       Bound
Assessment

                 -26.2180       -10 > -51.5884  -0.8477 <     10 Not
equivalent

                        Test         Null        DF    t Value    P-Value

                        Upper         -10         8      -1.19     0.8657
                        Lower          10         8      -2.65     0.0145
                        Overall                                    0.8657

Any help would be appreciated.
Thank you,
June