When entering confidence intervals only the program responses with a message box
The message shouldnot be in red as it is not a critical error. It results in 5.3% confidence C95%>OEL
Taking the point estimators, result in a higher 13.3% confidence that C95%>OEL. What is the logic ? For such a large sample (n=18) one expect no influence of measurement uncertainty.
Hello Theo,
A couple of ideas :

the higher risk without interval censorship appears due to the higher estimated GSD when you replaced [ab] with (a+b)/2.

I think it is discussed in another area of the forum (link) and in a small report I wrote, but using the interval censorship capability of IHSTAT_Bayes is not meant to be used as a way to model measurement error. Measurement error is usually modelled as a gaussian distribution centered on the point estimate. Interval censorship on the other hand, considers nothing is know within the interval, and is influenced by where the interval is compared to the overall exposure distribution. This might have caused the change in numbers. I see the interval censoring approach useful for detected but not quantified results, or for when there was too much chemical in the second section of the tube.

Thanks for the tip on the error, indeed it shoulnd be shown as critical.
Cheers
Hello Theo, It does not, in our mind at least. It is just a showcase of the capabilities of the engine.
Ah another curcumstance where interval censorship is useful, which I had forgotten to mention, and we might say example 3 illustrates that : when you calculate a 8h TWA from 2 samples, one of which is non quantified, what you know about the 8h value is actually a range. E.g. for 2 4hour samples : <10 and 20 correspond to [1015].
This and the other cases mentionned above are what interval censorhip is intended for.
Ok thanks for this additional information
What I understood from you earlier respons is that IHStatBayes replaces [ab] with (a+b)/2
As Nonquantified means no knowledge at all, not even <LoD, the range should be [1020]
When seeking the influence of CV=30%, you can use something like MC on the gaussian distribitions around the point estimators
Hello Theo, as additional info : Our measurement error model (algorithms available and public but not yet implemented in expostats) is the following :
True exposure(i) = lognormal(GM, GSD).
Oberved exposure(i) = normal( true exposure(i) , measuement error CV).
For my example, I beg to differ, a lab will report <LOQ, so we can assume the true value was anywhere between 0 and LOQ (0 and 10 in my example). so the TWA anywhere between (0+20)/2 and (10+20)/2, no ?
Excellent,
I would appriciate if the example I used in IHStatBayes can be processed.
Sometimes a lab repports ‘nothing’ as the sample was corrupt, disappeared or whatever. So for 2 4hour samples : unknown and 20 correspond to [10saturation concentration ] .
can you clarify please ? not sure I understand
See the first message on this subject
Below the point estimators from AIHA 2015 annex V. You may use CV 30%.
But if I can do it myself with the available and public algorithms, please forward them.
124
63
274
44
8
23
239
94
114
45
53
47
43
32
97
73
49
48
OK I understand, algorithms are presented in this report, and available here, but they might be a tad difficult to navigate. You could try the Csharp prototype or I can run the analysis for you but it’ll take a few days :). Main impact I can predict is a smaller GSD but overall small differences ( as shown by previous authors).
Some results.
First without error, then with 30% error. For each case, screenshot of input, then output of the C# prototype.
No error
With 30% error CV (no bias)
As Grzebyk et al. showed, the “mistake” caused by not considering measurement error in the analysis corresponds to an overestimation of variability, seen in GSD and P95 estimates.
This C# prototype looks quite mature !
And the result is as expected: for N=18 the influence of a Variation Coefficient (%)=30 on Exceedance (%) is minimal
This example from the AIHA 2015 strategy Annex V (Paul Hewett) page 442 refers to Heptane with an OELV of 400,which will give more illustrative exceedance and critical percentage  overexposure risk (%) values than the current 100%
How can I get this application running on W11 system? Or do you have a link?
I would like to see what happens with samples of size N= 3, 4 and 5
Regards
Theo
Hello Theo, the link I provided above : https://github.com/webexpo/webexpo_cs_proto
Should provide enough information to run the prototype easily (I did it myself yesterday) : downlowd the repository on your PC, then run one of the mentionned files (there is a windows security warning to bypass)
Jérôme
Thanks Jerome, I will give it a try!
The Readme instruction is quite different from what you should do
So donot :
 Download the code (by clicking the green “Clone or download” button)
 Open the folder containing the code
But  Click the green “Code” button, in the upper right corner
 Click Download ZIP
Excellent, thanks for the practical instructions
FYI the mathematical models underlying the prototype are described in the IRSST report, and an extensive validation effort (additional to what was described in the report but no yet published) was conducted to verify that all platforms (C#, R, JAGS, Javascript) yield the same results for the same data.