Scores: Mapping Condition Specific to Quality of Life
Mapping scores from one tool to another tool is definitely possible IF the two tools are measuring the same thing. For example, the FOTO team did the research required to provide DASH and ODI equivalent scores for customers who need to have these scores for payment purposes. FOTO measurements are condition or body part specific measurements. FOTO measurements rely on computer adaptive testing as the testing mode whereas the DASH and ODI are legacy tools that use paper and pencil method (EVEN if a computer is involved). The reason DASH and ODI are considered legacy tools is because they were the first tools ever created and relied on paper and pencil method. Even if a computer is involved, they are still considered paper and pencil tools because the computer just facilitates providing all the questions to the person, just like when doing a paper and pencil questionnaire. Every question has to have a response or be considered when scoring the tool.
It doesn't make intuitive sense to try to map a condition specific score to a health related quality of life score to determine quality of life adjusted years. The two tools a condition specific tool and a health related quality of life tool are measuring two different things. I didn't realize that research would be required to show that this isn't a great thing to do.
Here's a quick view of the abstract.
Can Mapping Algorithms Based on Raw Scores Overestimate QALYs Gained by Treatment? A Comparison of Mappings Between the Roland-Morris Disability Questionnaire and the EQ-5D-3L Based on Raw and Differenced Score Data.
Mapping algorithms are increasingly being used to predict health-utility values based on responses or scores from non-preference-based measures, thereby informing economic evaluations.
We explored whether predictions in the EuroQol 5-dimension 3-level instrument (EQ-5D-3L) health-utility gains from mapping algorithms might differ if estimated using differenced versus raw scores, using the Roland-Morris Disability Questionnaire (RMQ), a widely used health status measure for low back pain, as an example.
We estimated algorithms mapping within-person changes in RMQ scores to changes in EQ-5D-3L health utilities using data from two clinical trials with repeated observations. We also used logistic regression models to estimate response mapping algorithms from these data to predict within-person changes in responses to each EQ-5D-3L dimension from changes in RMQ scores. Predicted health-utility gains from these mappings were compared with predictions based on raw RMQ data.
Using differenced scores reduced the predicted health-utility gain from a unit decrease in RMQ score from 0.037 (standard error [SE] 0.001) to 0.020 (SE 0.002). Analysis of response mapping data suggests that the use of differenced data reduces the predicted impact of reducing RMQ scores across EQ-5D-3L dimensions and that patients can experience health-utility gains on the EQ-5D-3L 'usual activity' dimension independent from improvements captured by the RMQ.
Mappings based on raw RMQ data overestimate the EQ-5D-3L health utility gains from interventions that reduce RMQ scores. Where possible, mapping algorithms should reflect within-person changes in health outcome and be estimated from datasets containing repeated observations if they are to be used to estimate incremental health-utility gains.