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Comparison of raw and regression approaches to capturing change on patient-reported outcome measures

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Abstract

Purpose

Patient-reported outcome (PRO) analyses often involve calculating raw change scores, but limitations of this approach are well documented. Regression estimators can incorporate information about measurement error and potential covariates, potentially improving change estimates. Yet, adoption of these regression-based change estimators is rare in clinical PRO research.

Methods

Both simulated and PROMIS® pain interference items were used to calculate change employing three methods: raw change scores and regression estimators proposed by Lord and Novick (LN) and Cronbach and Furby (CF). In the simulated data, estimators’ ability to recover true change was compared. Standard errors of measurement (SEM) and estimation (SEE) with associated 95% confidence limits were also used to identify criteria for significant improvement. These methods were then applied to real-world data from the PROMIS® study.

Results

In the simulation, both regression estimators reduced variability compared to raw change scores by almost half. Compared to CF, the LN regression better recovered true simulated differences. Analysis of the PROMIS® data showed similar themes, and change score distributions from the regression estimators showed less dispersion. Using distribution-based approaches to calculate thresholds for significant within-patient change, smaller changes could be detected using both regression estimators.

Conclusions

These results suggest that calculating change using regression estimates may result in more increased measurement sensitivity. Using these scores in lieu of raw differences can help better identify individuals who experience real underlying change in PROs in the course of a trial, and enhance the established methods for identifying thresholds for meaningful within-patient change in PROs.

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Data availability

The PROMIS® 1 Wave 2 Pain Depression dataset can be requested here: https://doi.org/10.7910/DVN/ZDIITC.

Abbreviations

CAT:

Computerized adaptive testing

CF:

Cronbach & Furby (complete estimator)

CTT:

Classical test theory

EAP:

Expected a-priori

GRM:

Graded response model

IRT:

Item-response theory

LN:

Lord & Novick

MVN:

Multivariate normal distribution

PRO:

Patient-reported outcome

PROMIS®:

Patient-reported outcome measurement information system

SE:

Standard Error

SEM:

Standard error of measurement

SEP:

Standard error of prediction

SS:

Sum score

TS:

T-score

References

  1. U.S. Food and Drug Administration (2019) Patient-focused Drug Development Guidance Public Workshop - Discussion document: Incorporating clinical outcome assessments into endpoints for regulatory decision-making. Retrieved, from https://www.fda.gov/media/132505/download

  2. Coon, C. D., & Cook, K. F. (2018). Moving from significance to real-world meaning: Methods for interpreting change in clinical outcome assessment scores. Quality of Life Research, 27, 33–40.

    Article  PubMed  Google Scholar 

  3. US Food and Drug Administration. (2018). Patient-Focused Drug Development Guidance Public Workshop: Methods to identify what is important to patients select, develop or modify fit-for-purpose clinical outcomes assessments.

  4. Kim-Kang, G., & Weiss, D. J. (2008). Adaptive measurement of individual change. Zeitschrift für Psychologie / Journal of Psychology, 216, 49–58.

    Article  Google Scholar 

  5. Lord, F. M. (1958). Further problems in the measurement of growth. Educational and Psychological Measurement, 18, 437–451.

    Article  Google Scholar 

  6. Lord, F. M. (1956). The measurement of growth. ETS Res Bull Ser, 1956, i–22.

    Google Scholar 

  7. McNemar, Q. (1958). On growth measurement. Educational and Psychological Measurement, 18, 47–55.

    Article  Google Scholar 

  8. Cronbach, L. J., & Furby, L. (1970). How we should measure change–or should we? Psychological Bulletin, 74, 68–80.

    Article  Google Scholar 

  9. Lord, F. M., & Novick, M. R. (1968). Statistical theories of mental test scores. Addison-Wesley Pub Co, Reading.

    Google Scholar 

  10. Cascio, W. F., & Kurtines, W. M. (1977). A practical method for identifying significant change scores. Educational and Psychological Measurement, 37, 889–895. https://doi.org/10.1177/001316447703700411

    Article  Google Scholar 

  11. Cella, D., Riley, W., Stone, A., Rothrock, N., Reeve, B., Yount, S., Amtmann, D., Bode, R., Buysse, D., Choi, S., Cook, K., Devellis, R., Dewalt, D., Fries, J. F., Gershon, R., Hahn, E. A., Lai, J. S., Pilkonis, P., Revicki, D., … Hays, R. (2010). The patient-reported outcomes measurement information system (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005–2008. Journal of Clinical Epidemiology, 63, 1179–1194. https://doi.org/10.1016/j.jclinepi.2010.04.011

    Article  PubMed  PubMed Central  Google Scholar 

  12. Segawa, E., Schalet, B., & Cella, D. (2020). A comparison of computer adaptive tests (CATs) and short forms in terms of accuracy and number of items administrated using PROMIS profile. Quality of Life Research, 29, 213–221.

    Article  PubMed  Google Scholar 

  13. Dagmar Amtmann, 2016, "PROMIS 1 Wave 2 Pain", https://doi.org/10.7910/DVN/ESOAH5, Harvard Dataverse, V1, UNF:6:TYzYcoNorGguhqSjkVdL2Q== [fileUNF]

  14. Amtmann, D., Cook, K. F., Jensen, M. P., Chen, W.-H., Choi, S., Revicki, D., Cella, D., Rothrock, N., Keefe, F., Callahan, L., & Lai, J.-S. (2010). Development of a PROMIS item bank to measure pain interference. Pain, 150, 173–182.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Samejima, F. (1994). Estimation of reliability coefficients using the test information function and its modifications. Applied Psychological Measurement, 18, 229–244.

    Article  Google Scholar 

  16. R Core Team. (2020). A Language and Environment for Statistical Computing. R Found. Stat. Comput. Retrieved, from https://www.R--project.org

  17. Chalmers, R. P. (2012). mirt: A Multidimensional Item Response Theory Package for the R Environment. Journal of Statistical Software, 48, 1–29. https://doi.org/10.18637/jss.v048.i06

    Article  Google Scholar 

  18. der Elst, W., Molenberghs, G., Hilgers, RD., Verbeke, G., Heussen, N. (2019). CorrMixed: Estimate Correlations Between Repeatedly Measured Endpoints (Eg, Reliability) Based on Linear Mixed-Effects Models. R package version 1.0

  19. Revelle, W. (2021). psych: Procedures for Personality and Psychological Research, Northwestern University, Evanston, Illinois, USA, Retrieved, from https://CRAN.R-project.org/package=psychVersion=2.1.9

  20. Cohen, J. (1988). Statistical power analysis for the behavioral science (2nd ed.). Taylor & Francis Group.

    Google Scholar 

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Funding

The current project did not have explicit extramural funding sources. All authors are employees of their respective institutions.

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Authors and Affiliations

  1. Patient Centered Research, Evidera, 7101 Wisconsin Ave, Suite 1400, Bethesda, MD, 20814, USA

    David A. Andrae

  2. Endpoint Outcomes, 11 Beacon St., Suite 910, Boston, MA, 02108, USA

    Brandon Foster

  3. Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, 625 N Michigan Ave., 21st Floor, Chicago, IL, USA

    J. Devin Peipert

Authors
  1. David A. Andrae
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  2. Brandon Foster
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  3. J. Devin Peipert
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Contributions

All authors contributed to the conceptualization, drafting, and review of the manuscript. DAA: conducted the analyses. JDP: supplied the PROMIS® dataset. All authors approved the final manuscript.

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Correspondence to David A. Andrae.

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Andrae, D.A., Foster, B. & Peipert, J.D. Comparison of raw and regression approaches to capturing change on patient-reported outcome measures. Qual Life Res 32, 1381–1390 (2023). https://doi.org/10.1007/s11136-022-03196-x

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