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Purpose <p>To describe the data analytic strategy used to develop new quality-of-life measures for the Limb Injury Measurement Battery for Quality of Life (LIMB-QOL).</p> Methods <p>Several item pools were created and administered to a large sample of individuals with a history of major extremity injury or limb loss (n = 603). Item analyses adhered to modern psychometric standards (e.g., PROMIS®, COSMIN) and aimed to create several item response theory-based (IRT) item banks based on the graded response model. Items were removed iteratively based on pre-defined criteria and IRT model assumptions were met for the final item pools (monotonicity, unidimensionality, local item independence); differential item functioning, test–retest reliability, and convergent validity were then evaluated. Computer adaptive test and short-form versions of final item banks were created and examined using data simulation.</p> Results <p>Item analyses led to the development of 8 new item banks and two fixed-length scales. These 10 new LIMB-QOL measures demonstrated initial evidence of reliability (α range = 0.94–0.98, test–retest ICC range: 0.68–0.91) and convergent validity for use in individuals with a history of major extremity injury or limb loss, and abbreviated formats of the full item banks exhibited comparable performance.</p> Conclusion <p>The new LIMB-QOL measures demonstrated strong psychometric properties and can be used to collect patient-reported assessments of quality of life following major extremity injury and limb loss. The analytic strategy described herein exemplifies how the PROMIS methodology can be utilized to design IRT-based patient-reported outcome measures to fill measurement gaps for specific clinical populations.</p>

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A methodological overview of new measure development for the Limb Injury Measurement Battery for Quality of Life (LIMB-QOL)

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月4日

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  • Aaron J. Boulton,
  • Seung W. Choi,
  • Callie E. Tyner,
  • Jerry Slotkin,
  • David S. Tulsky

摘要

Purpose

To describe the data analytic strategy used to develop new quality-of-life measures for the Limb Injury Measurement Battery for Quality of Life (LIMB-QOL).

Methods

Several item pools were created and administered to a large sample of individuals with a history of major extremity injury or limb loss (n = 603). Item analyses adhered to modern psychometric standards (e.g., PROMIS®, COSMIN) and aimed to create several item response theory-based (IRT) item banks based on the graded response model. Items were removed iteratively based on pre-defined criteria and IRT model assumptions were met for the final item pools (monotonicity, unidimensionality, local item independence); differential item functioning, test–retest reliability, and convergent validity were then evaluated. Computer adaptive test and short-form versions of final item banks were created and examined using data simulation.

Results

Item analyses led to the development of 8 new item banks and two fixed-length scales. These 10 new LIMB-QOL measures demonstrated initial evidence of reliability (α range = 0.94–0.98, test–retest ICC range: 0.68–0.91) and convergent validity for use in individuals with a history of major extremity injury or limb loss, and abbreviated formats of the full item banks exhibited comparable performance.

Conclusion

The new LIMB-QOL measures demonstrated strong psychometric properties and can be used to collect patient-reported assessments of quality of life following major extremity injury and limb loss. The analytic strategy described herein exemplifies how the PROMIS methodology can be utilized to design IRT-based patient-reported outcome measures to fill measurement gaps for specific clinical populations.