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Purpose <p>This proof-of-principle study investigated a novel Computer Adaptive Testing (CAT) method termed Latent-class and Sum score based Computerized Adaptive Testing (LSCAT), developed for screening purposes. LSCAT was assessed for its ability to accurately predict depression symptoms during health-related quality of life (HR-QoL) screenings.</p> Methods <p>LSCAT’s performance was compared with two benchmark CAT methods, Stochastic Curtailment (SC) and Decision Tree based Computer Adaptive Testing (DTCAT), using data from the Patient Health Questionnaire-9 (PHQ-9).</p> Results <p>LSCAT consistently outperformed both SC and DTCAT in terms of predictive accuracy, achieving the lowest rates of Type I error. Furthermore, LSCAT’s Type II error rates were at least as low as those of SC and significantly lower than those of DTCAT across all simulation scenarios.</p> Conclusion <p>These results suggest that LSCAT is a promising method for developing valid and efficient screening tools in HR-QoL research and practice.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月26日

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  • Anastasios Psychogyiopoulos,
  • Niels Smits,
  • L. Andries van der Ark

摘要

Purpose

This proof-of-principle study investigated a novel Computer Adaptive Testing (CAT) method termed Latent-class and Sum score based Computerized Adaptive Testing (LSCAT), developed for screening purposes. LSCAT was assessed for its ability to accurately predict depression symptoms during health-related quality of life (HR-QoL) screenings.

Methods

LSCAT’s performance was compared with two benchmark CAT methods, Stochastic Curtailment (SC) and Decision Tree based Computer Adaptive Testing (DTCAT), using data from the Patient Health Questionnaire-9 (PHQ-9).

Results

LSCAT consistently outperformed both SC and DTCAT in terms of predictive accuracy, achieving the lowest rates of Type I error. Furthermore, LSCAT’s Type II error rates were at least as low as those of SC and significantly lower than those of DTCAT across all simulation scenarios.

Conclusion

These results suggest that LSCAT is a promising method for developing valid and efficient screening tools in HR-QoL research and practice.