When it comes to recording race, it’s important to understand design criteria.
Allowing more possible choices is not always better in clinical trials. The more data you have, the more degrees of freedom you have in the data and the more spurious correlations you are going to pick up.
If you add a new category that only appears in one or two people in your trial, you pay the cost but you are not going to learn anything from it.
This is one of the few things we were taught at university in our statistics for bioinformatics course (which was run by someone who looks over the statistics of clinical trials) that aren’t often made in discussion of statistics I see online.
Minorities like Black people and Native American have lower trust in the medical system because the system historically treated them poorly.
Creating rules for representation if Black and Native Americans in clinical trials has the purpose of winning the trust of those communities.
As far as I understand, FDA regulators do read free text fields. While free text feels don’t allow for quantitative analysis the allow for qualitative analysis and new hypothesis generation.
I may have distracted from the point by using the race field as my example, my point was primarily to show how deviating from controlled terminology is a waste of time and money.
Allowing more possible choices is not always better in clinical trials. The more data you have, the more degrees of freedom you have in the data and the more spurious correlations you are going to pick up.
Controlled terminology outline what standard terms are available to be used for a particular field. Studies are not required to put all available terms in the dropdown. For instance, there are 100+ entries in the controlled terminology for “UNIT”. Usually one only needs to make available the ones applicable to whatever is being measured rather than all the allowed options.
In some regards my perspective was biased here by being exclusively focused on quantitative analysis.
When it comes to recording race, it’s important to understand design criteria.
Allowing more possible choices is not always better in clinical trials. The more data you have, the more degrees of freedom you have in the data and the more spurious correlations you are going to pick up.
If you add a new category that only appears in one or two people in your trial, you pay the cost but you are not going to learn anything from it.
This is one of the few things we were taught at university in our statistics for bioinformatics course (which was run by someone who looks over the statistics of clinical trials) that aren’t often made in discussion of statistics I see online.
Minorities like Black people and Native American have lower trust in the medical system because the system historically treated them poorly.
Creating rules for representation if Black and Native Americans in clinical trials has the purpose of winning the trust of those communities.
As far as I understand, FDA regulators do read free text fields. While free text feels don’t allow for quantitative analysis the allow for qualitative analysis and new hypothesis generation.
I may have distracted from the point by using the race field as my example, my point was primarily to show how deviating from controlled terminology is a waste of time and money.
Controlled terminology outline what standard terms are available to be used for a particular field. Studies are not required to put all available terms in the dropdown. For instance, there are 100+ entries in the controlled terminology for “UNIT”. Usually one only needs to make available the ones applicable to whatever is being measured rather than all the allowed options.
In some regards my perspective was biased here by being exclusively focused on quantitative analysis.