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PROMIS Global Health item nonresponse: is it better to impute missing item responses before computing T-scores?

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Abstract

Purpose

Item response theory (IRT) scoring provides T-scores for physical and mental health subscales on the Patient-Reported Outcomes Measurement Information System Global Health questionnaire (PROMIS-GH) even when relevant items are skipped. We compared different item- and score-level imputation methods for estimating T-scores to the current scoring method.

Methods

Missing PROMIS-GH items were simulated using a dataset of complete PROMIS-GH scales collected at a single tertiary care center. Four methods were used to estimate T-scores with missing item scores: (1) IRT-based scoring of available items (IRTavail), (2) item-level imputation using predictive mean matching (PMM), (3) item-level imputation using proportional odds logistic regression (POLR), and (4) T-score-level imputation (IMPdirect). Performance was assessed using root mean squared error (RMSE) and mean absolute error (MAE) of T-scores and comparing estimated regression coefficients from the four methods to the complete data model. Different proportions of missingness and sample sizes were examined.

Results

IRTavail had lowest RMSE and MAE for mental health T-scores while PMM had lowest RMSE and MAE for physical health T-scores. For both physical and mental health T-scores, regression coefficients estimated from imputation methods were closer to those of the complete data model.

Conclusions

The available item scoring method produced more accurate PROMIS-GH mental but less accurate physical T-scores, compared to imputation methods. Using item-level imputation strategies may result in regression coefficient estimates closer to those of the complete data model when nonresponse rate is high. The choice of method may depend on the application, sample size, and amount of missingness.

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Funding

No external funding was received.

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Authors and Affiliations

  1. Department of Quantitative Health Sciences, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, JJN3-1, USA

    Nicolas R. Thompson, Ryan D. Honomichl & Brittany R. Lapin

  2. Center for Outcomes Research and Evaluation, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA

    Nicolas R. Thompson, Irene L. Katzan, Ryan D. Honomichl & Brittany R. Lapin

Authors
  1. Nicolas R. Thompson
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  2. Irene L. Katzan
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  3. Ryan D. Honomichl
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  4. Brittany R. Lapin
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Contributions

NRT, BRL: conceived and designed the study. NRT: analyzed the data and wrote first draft. All authors: critically revised the manuscript. All authors: reviewed and approved the final manuscript.

Corresponding author

Correspondence to Nicolas R. Thompson.

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Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

This study was approved by the Cleveland Clinic Institutional Review Board (IRB # 07-591). Because the study involved retrospective analysis of data acquired as part of standard of care, informed consent was waived. For this type of study formal consent is not required.

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Thompson, N.R., Katzan, I.L., Honomichl, R.D. et al. PROMIS Global Health item nonresponse: is it better to impute missing item responses before computing T-scores?. Qual Life Res 29, 537–546 (2020). https://doi.org/10.1007/s11136-019-02327-1

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  • DOI: https://doi.org/10.1007/s11136-019-02327-1

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