Medical Devices Design Verification Sampling

S

snowy

#1
My company performs both attribute and variable data analysis for design verification. When performing attribute analysis, we use the binomial distribution model and base our sample size (n) on the number of expected failures at a given confidence/reliability level. So, for example, for C/R of 95/90%, n=29 for 0 failures; n= 46 for 1 failure; n=61 for 3 failures; etc. Easy so far; here's where we get into "discussions": Any one of those criteria satisfies our requirements, so it seems like we can produce say 61 samples and if we have 0 failures after testing 29, or 1 after testing 46, we pass, and may stop testing. Is there anything mathematically wrong with "testing until we pass?" The pro point is that the experimenter's involvement in the testing can't have any effect on whether the design is good or not, since the test units have already been produced and once you meet one of the acceptance criteria, you pass. Also, the protocols used to generate the testing clearly state the alternate acceptance criteria before testing begins. The counterpoints revolve around lot acceptance testing (AQL's) and double, or multiple sampling but don't seem to apply since this is design verification testing and beta error is not considered. There are also many comments about not being sure the process is working well, but that, again, is process, not design, and not part of design verification.
So, anyone know if the testing scheme we use is incorrect?
 
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2

20130523

#2
The only piece missing from the information you have provided is what is considered verification. If you pass consecutively at each level then your system probably works. But if you fail, so to speak at the first level and then expand to have a larger sample size, and pass, I (as an auditor) may have concern that you have not demonstrated verification, as you have not demonstrated repeatability.

I allow repeat pulls in my system, but I have it charted out to require 3-5 consecutive passes to be considered verified.

Mathematically, I don't see anything incorrect, but AQLs basics would still apply in determining the acceptable quality level, but ISO 13485 section 7.3.5 is short and simple, so you could probably make your case if you needed to.
 
S

snowy

#3
Thanks, Ambir. Verification is defined as meeting the predefined acceptance criteria. As I mentioned we use either variable or attribute analysis with acceptance criteria defined by risk based (RPN) Confidence/Reliability levels, with 95% (alpha) confidence of 95%, 90%, etc., percentages meeting the specification tested. The variable analysis is done using k factor stats; the attribute analysis, with binomial stats. The variable analysis method and acceptance criteria are the same as done for Ppk, with the percentages backed into from the calculated index. The attribute analysis is performed as I described in the original post with a number of ways of achieving success. However, I don't see the beta error portion that always enters into the intervals used to calculate sample sizes for AQL's entering into the calculations I mentioned.
 
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