A novel CAT method for QoL screening: proof-of-principle study with comparisons to standard methods.

Area of Science:

• Psychometric methods
• Health services research
• Mental health screening

Background:

• Health-related quality of life (HR-QoL) screenings are crucial for identifying individuals with depression.
• Current screening methods may lack optimal accuracy and efficiency.
• Computer Adaptive Testing (CAT) offers a potential improvement for efficient and accurate assessment.

Purpose of the Study:

• To investigate a novel CAT method, Latent-class and Sum score based Computerized Adaptive Testing (LSCAT), for depression symptom screening.
• To evaluate LSCAT's accuracy in predicting depression symptoms within HR-QoL assessments.
• To establish LSCAT as a viable tool for mental health screening.

Main Methods:

• LSCAT was developed and tested as a proof-of-principle CAT method.
• Performance comparison involved two benchmark CAT methods: Stochastic Curtailment (SC) and Decision Tree based Computer Adaptive Testing (DTCAT).
• Data from the Patient Health Questionnaire-9 (PHQ-9) were utilized for simulations.

Main Results:

• LSCAT demonstrated superior predictive accuracy compared to both SC and DTCAT.
• LSCAT achieved the lowest rates of Type I error (false positives).
• LSCAT exhibited Type II error rates (false negatives) that were equal to or lower than SC and significantly lower than DTCAT.

Conclusions:

• LSCAT shows significant promise as a valid and efficient screening tool.
• The findings support the use of LSCAT in HR-QoL research and clinical practice.
• LSCAT represents an advancement in psychometric screening methodologies for mental health.