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Simulation study comparing analytical methods for single-item longitudinal patient-reported outcomes data.

Vinicius F Calsavara1, Márcio A Diniz2, Mourad Tighiouart2

  • 1Cedars-Sinai Medical Center, Samuel Oschin Comprehensive Cancer Institute, Los Angeles, CA, USA. vinicius.calsavara@cshs.org.

Quality of Life Research : an International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation
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PubMed
Summary
This summary is machine-generated.

For analyzing longitudinal patient-reported outcomes (PROs), probabilistic index models (PIM) with baseline symptom adjustment offer a robust approach. This method balances performance with practical applicability in clinical research.

Keywords:
Adverse eventCumulative logit mixed modelPRO-CTCAEPatient-reported outcomeProbabilistic index modelType I and II errors

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Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Health Outcomes Research

Background:

  • Reproducible analysis of longitudinal patient-reported outcome (PRO) data is crucial for clinical research.
  • Ordinal PRO data presents unique analytical challenges.
  • Comparing statistical methods for analyzing PRO data is essential for advancing research methodologies.

Purpose of the Study:

  • To compare the performance of semiparametric probabilistic index models (PIM) against parametric cumulative logit mixed models (CLMM) for longitudinal ordinal PRO data.
  • To evaluate the power of PIM and CLMM in detecting differences in PRO adverse events (AE) between control and intervention groups.

Main Methods:

  • A simulation study was conducted comparing PIM and CLMM.
  • PRO data were simulated using copula multinomial models under various scenarios.
  • The study utilized existing and novel summary scores for PROs and included comparisons with clinical trial data.

Main Results:

  • Cumulative logit mixed models (CLMM) demonstrated substantially greater power than probabilistic index models (PIM) when using a baseline-adjusted method.
  • CLMM also showed a small advantage in power when the baseline symptom was included as a covariate.
  • The study identified differences in PRO adverse events between control and intervention groups.

Conclusions:

  • While CLMM exhibited superior performance, its underlying assumptions are difficult to verify in real-world settings.
  • Probabilistic index models (PIM) with baseline symptom as a covariate are recommended for analyzing longitudinal ordinal PRO data due to their practical advantages.
  • The findings guide the selection of appropriate analytical methods for PRO data in clinical research.