Although PROMIS (Patient-Reported Outcomes Measurement Information System) has created several Computerized Adaptive Testing (CAT) systems, at this point, most of them target general conditions. In contrast, the literature has repeatedly demonstrated that condition-specific assessment tools tend to be more sensitive. This characteristic translates into a higher probability of detecting real differences between treatments, as well as smaller sample sizes in clinical trials. We clean and prepare your dataset. This phase of the project ensures that your dataset is in a format that is ready for analysis. [Read More]
Computerized Adaptive Testing (CAT) to facilitate data collection and outcomes assessment
We developed a new infographic to explore Computerized Adaptive testing in terms of its requirements, requirements, and how it works in very generic terms.
Computerized Adaptive Testing (CAT)
Data Science Methods
Computerized Adaptive Testing or CAT for short, is a method to assess patients’ self-report concepts (also known as constructs or latent variables). CAT systems present several advantages over traditional self-report scales. Below we outline the comparison between the two, how CAT systems work, and what you would need if you would like to create new CAT systems for clinical areas where they still don’t exist. Shortcomings of traditional scales and where CAT excels Most clinical research and quality safety improvement projects involve the use of self-report scales to measure constructs such as quality of life, depression, ability to perform activities of daily living, among others. [Read More]
Health utilities: addressing the condition specific vs. general assessment issue through Computerized Adaptive Testing.
Ben is planning a chronic pain trial where his team intends to measure health utilities for a subsequent cost-effectiveness evaluation. Although he is familiar with these concepts, he is now torn between generic and disease-specific utility measures. There is some evidence in his field that condition-specific measurements are more sensitive. However, by making his assessment condition-specific, the utility measures lose their ability to generalize to patients with other conditions. [Read More]