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method comparison for censored data

david_arteta

Hi group,

I would like to compare thow analytical methods that are interval-censored (limit of quantitation - upper limit of quantitation). So I have observations with values of (LOQ) as well as >120 (Upper LOQ). Usually Bland-Altman would do but in this case I am not sure.

Some people suggest for the lowest to substituting BQL observations with LOQ/2. But how about the upper LOQ?

There was discussion in the JMP Discovery Summit 2014 about censored data (https://community.jmp.com/kvoqx44227/attachments/kvoqx44227/discovery-2014-content/53/1/Discovery%20Summit%202014%20(Your%20Data%20Is%20Censored).pdf) but little is explained in this document.

Any suggestions as to how to deal with these data?

Thanks beforehand for any input

Dave

Accepted answers

Mark_Bailey

Unfortunately, Generalized Regression cannot deal with errors in both X and Y. Right now, only Bivariate can handle such a case using the Fit Orthogonal command. Method comparison involves a regression analysis of a new assay against a standard assay. Both assays have errors.

All comments

Mark_Bailey

Your data are not interval censored. Your observations that are < LOQ are left censored and your observations that are > LOQ are right censored. Your observations in between these limits are exact.

Do not use LOQ/2 for the left censored observations as this ad hoc substitution will bias your answers.

How do you "compare two analytical methods?"

david_arteta

Dear Mark,

thanks for your answer and for clarifying the data nature, I will keep that in mind.

Regarding your question, I am not sure I get it right. I have two quantitative methods to detect proteins and I want to compare them. If I am not wrong the most appropriate methods (or at least some that I have found on the literature) for comparing such measurements are Bland-Altman plots and Passing-Bablok regression. Is this correct? Any other method you can suggest?

I understand that substituting data introduces bias, but then how can I analyze data with "less than" or "greater than" results? I cannot just ignore it as it provides valuable information (mostly absence of protein but also large amounts of protein on the other hand)

Best,

David

Mark_Bailey

My question was simply to learn which methods you intended to use.

Unfortunately, most statistical methods do not recognize censoring. Most comparisons that I know (e.g., CLSI), therefore, omit data that is beyond the quantitative range of the assay. You might compare the detection capability and linearity of the two assays separately.

david_arteta

Dear Mark, thanks for your very useful input. I wonder if you had time to go through the document from the JMP Discovery Summit I refer to in mi original post. On page 13 and 14, José G Ramirez seems to be discussing on just a similar problem I am facing. And he performs what I think it is a Life Distribution analysis.

David