The challenge of measuring intra-individual change in fatigue during cancer treatment

Carol M. Moinpour*, Gary W. Donaldson, Kimberly M. Davis, Arnold L. Potosky, Roxanne E. Jensen, Julie R. Gralow, Anthony L. Back, Jimmy J. Hwang, Jihye Yoon, Debra L. Bernard, Deena R. Loeffler, Nan E. Rothrock, Ron D. Hays, Bryce B. Reeve, Ashley Wilder Smith, Elizabeth A. Hahn, David Cella

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

Purpose: To evaluate how well three different patient-reported outcomes (PROs) measure individual change. Methods: Two hundred and fourteen patients (from two sites) initiating first or new chemotherapy for any stage of breast or gastrointestinal cancer participated. The 13-item FACIT Fatigue scale, a 7-item PROMIS® Fatigue Short Form (PROMIS 7a), and the PROMIS® Fatigue computer adaptive test (CAT) were administered monthly online for 6 months. Reliability of measured change was defined, under a population mixed effects model, as the ratio of estimated systematic variance in rate of change to the estimated total variance of measured individual differences in rate of change. Precision of individual measured change, the standard error of measurement of change, was given by the square root of the rate-of-change sampling variance. Linear and quadratic models were examined up to 3 and up to 6 months. Results: A linear model for measured change showed the following by 6 and 3 months, respectively: PROMIS CAT (0.363 and 0.342); PROMIS SF (0.408 and 0.533); FACIT (0.459 and 0.473). Quadratic models offered no noteworthy improvement over linear models. Both reliability and precision results demonstrate the need to improve the measurement of intra-individual change. Conclusions: These results illustrate the challenge of reliably measuring individual change in fatigue with a level of confidence required for intervention. Optimizing clinically useful measurement of intra-individual differences over time continues to pose a challenge for PROs.

Original languageEnglish (US)
Pages (from-to)259-271
Number of pages13
JournalQuality of Life Research
Volume26
Issue number2
DOIs
StatePublished - Feb 1 2017

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Fatigue
Linear Models
Individuality
Neoplasms
Gastrointestinal Neoplasms
Therapeutics
Reproducibility of Results
Breast Neoplasms
Drug Therapy
Population
Patient Reported Outcome Measures

Keywords

  • Cancer
  • Fatigue
  • Intra-individual change
  • Measured change
  • Patient-reported outcomes

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health

Cite this

Moinpour, C. M., Donaldson, G. W., Davis, K. M., Potosky, A. L., Jensen, R. E., Gralow, J. R., ... Cella, D. (2017). The challenge of measuring intra-individual change in fatigue during cancer treatment. Quality of Life Research, 26(2), 259-271. https://doi.org/10.1007/s11136-016-1372-9
Moinpour, Carol M. ; Donaldson, Gary W. ; Davis, Kimberly M. ; Potosky, Arnold L. ; Jensen, Roxanne E. ; Gralow, Julie R. ; Back, Anthony L. ; Hwang, Jimmy J. ; Yoon, Jihye ; Bernard, Debra L. ; Loeffler, Deena R. ; Rothrock, Nan E. ; Hays, Ron D. ; Reeve, Bryce B. ; Smith, Ashley Wilder ; Hahn, Elizabeth A. ; Cella, David. / The challenge of measuring intra-individual change in fatigue during cancer treatment. In: Quality of Life Research. 2017 ; Vol. 26, No. 2. pp. 259-271.
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Moinpour, CM, Donaldson, GW, Davis, KM, Potosky, AL, Jensen, RE, Gralow, JR, Back, AL, Hwang, JJ, Yoon, J, Bernard, DL, Loeffler, DR, Rothrock, NE, Hays, RD, Reeve, BB, Smith, AW, Hahn, EA & Cella, D 2017, 'The challenge of measuring intra-individual change in fatigue during cancer treatment', Quality of Life Research, vol. 26, no. 2, pp. 259-271. https://doi.org/10.1007/s11136-016-1372-9

The challenge of measuring intra-individual change in fatigue during cancer treatment. / Moinpour, Carol M.; Donaldson, Gary W.; Davis, Kimberly M.; Potosky, Arnold L.; Jensen, Roxanne E.; Gralow, Julie R.; Back, Anthony L.; Hwang, Jimmy J.; Yoon, Jihye; Bernard, Debra L.; Loeffler, Deena R.; Rothrock, Nan E.; Hays, Ron D.; Reeve, Bryce B.; Smith, Ashley Wilder; Hahn, Elizabeth A.; Cella, David.

In: Quality of Life Research, Vol. 26, No. 2, 01.02.2017, p. 259-271.

Research output: Contribution to journalArticle

TY - JOUR

T1 - The challenge of measuring intra-individual change in fatigue during cancer treatment

AU - Moinpour, Carol M.

AU - Donaldson, Gary W.

AU - Davis, Kimberly M.

AU - Potosky, Arnold L.

AU - Jensen, Roxanne E.

AU - Gralow, Julie R.

AU - Back, Anthony L.

AU - Hwang, Jimmy J.

AU - Yoon, Jihye

AU - Bernard, Debra L.

AU - Loeffler, Deena R.

AU - Rothrock, Nan E.

AU - Hays, Ron D.

AU - Reeve, Bryce B.

AU - Smith, Ashley Wilder

AU - Hahn, Elizabeth A.

AU - Cella, David

PY - 2017/2/1

Y1 - 2017/2/1

N2 - Purpose: To evaluate how well three different patient-reported outcomes (PROs) measure individual change. Methods: Two hundred and fourteen patients (from two sites) initiating first or new chemotherapy for any stage of breast or gastrointestinal cancer participated. The 13-item FACIT Fatigue scale, a 7-item PROMIS® Fatigue Short Form (PROMIS 7a), and the PROMIS® Fatigue computer adaptive test (CAT) were administered monthly online for 6 months. Reliability of measured change was defined, under a population mixed effects model, as the ratio of estimated systematic variance in rate of change to the estimated total variance of measured individual differences in rate of change. Precision of individual measured change, the standard error of measurement of change, was given by the square root of the rate-of-change sampling variance. Linear and quadratic models were examined up to 3 and up to 6 months. Results: A linear model for measured change showed the following by 6 and 3 months, respectively: PROMIS CAT (0.363 and 0.342); PROMIS SF (0.408 and 0.533); FACIT (0.459 and 0.473). Quadratic models offered no noteworthy improvement over linear models. Both reliability and precision results demonstrate the need to improve the measurement of intra-individual change. Conclusions: These results illustrate the challenge of reliably measuring individual change in fatigue with a level of confidence required for intervention. Optimizing clinically useful measurement of intra-individual differences over time continues to pose a challenge for PROs.

AB - Purpose: To evaluate how well three different patient-reported outcomes (PROs) measure individual change. Methods: Two hundred and fourteen patients (from two sites) initiating first or new chemotherapy for any stage of breast or gastrointestinal cancer participated. The 13-item FACIT Fatigue scale, a 7-item PROMIS® Fatigue Short Form (PROMIS 7a), and the PROMIS® Fatigue computer adaptive test (CAT) were administered monthly online for 6 months. Reliability of measured change was defined, under a population mixed effects model, as the ratio of estimated systematic variance in rate of change to the estimated total variance of measured individual differences in rate of change. Precision of individual measured change, the standard error of measurement of change, was given by the square root of the rate-of-change sampling variance. Linear and quadratic models were examined up to 3 and up to 6 months. Results: A linear model for measured change showed the following by 6 and 3 months, respectively: PROMIS CAT (0.363 and 0.342); PROMIS SF (0.408 and 0.533); FACIT (0.459 and 0.473). Quadratic models offered no noteworthy improvement over linear models. Both reliability and precision results demonstrate the need to improve the measurement of intra-individual change. Conclusions: These results illustrate the challenge of reliably measuring individual change in fatigue with a level of confidence required for intervention. Optimizing clinically useful measurement of intra-individual differences over time continues to pose a challenge for PROs.

KW - Cancer

KW - Fatigue

KW - Intra-individual change

KW - Measured change

KW - Patient-reported outcomes

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Moinpour CM, Donaldson GW, Davis KM, Potosky AL, Jensen RE, Gralow JR et al. The challenge of measuring intra-individual change in fatigue during cancer treatment. Quality of Life Research. 2017 Feb 1;26(2):259-271. https://doi.org/10.1007/s11136-016-1372-9