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Employing a latent variable framework to improve efficiency in composite endpoint analysis.

Martina McMenamin1, Jessica K Barrett1, Anna Berglind2

  • 147959MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.

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|November 25, 2020
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Summary
This summary is machine-generated.

A new latent variable model improves analysis of composite endpoints in clinical trials for systemic lupus erythematosus (SLE). This method offers significant efficiency gains and reduces sample size requirements compared to standard logistic regression.

Keywords:
Composite endpointlatent variable modelresponder analysissystemic lupus erythematosus

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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Rheumatology

Background:

  • Composite endpoints are frequently used in clinical trials for chronic conditions like systemic lupus erythematosus (SLE).
  • Standard analysis of composite endpoints, such as logistic regression, can lead to substantial information loss.
  • SLE composite endpoints often combine measures of different scales (continuous, ordinal, binary).

Purpose of the Study:

  • To propose a novel latent variable model for analyzing complex composite endpoints in SLE clinical trials.
  • To address the information loss associated with traditional logistic regression methods.
  • To improve the precision and efficiency of treatment effect estimation.

Main Methods:

  • Development of a latent variable model assuming discrete outcomes manifest from underlying continuous measures.
  • Joint modeling of the individual components within the composite endpoint.
  • Simulation studies to compare the proposed method against standard analysis.
  • Application of the model to data from the Phase IIb MUSE trial in SLE patients.

Main Results:

  • The latent variable model demonstrated significant efficiency gains over standard logistic regression.
  • The magnitude of efficiency gains was dependent on the specific components contributing to the response.
  • A bootstrap procedure was employed to correct for bias when joint normality assumptions were violated.
  • The model estimated the treatment effect 2.5 times more precisely in the MUSE trial.

Conclusions:

  • The proposed latent variable model provides a more efficient and informative approach to analyzing composite endpoints in SLE.
  • This method can lead to substantial reductions in required sample size, estimated at 60% in the MUSE trial.
  • The findings suggest broader applicability of latent variable models for complex composite endpoints in clinical research.