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Confidence bounds for nonlinear dose-response relationships.

C Baayen1,2, P Hougaard1

  • 1Biometrics Division, H. Lundbeck A/S, Ottiliavej 9, 2500 Valby, Denmark.

Statistics in Medicine
|June 27, 2015
PubMed
Summary
This summary is machine-generated.

We introduce a profile likelihood method for calculating confidence intervals for drug dose-response curves. This approach offers improved accuracy and efficiency over traditional Wald and bootstrap methods, especially when monotonicity is considered.

Keywords:
confidence intervalsdose-findingnonlinear modelspercentile bootstrapprofile likelihood

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Area of Science:

  • Pharmacometrics
  • Biostatistics
  • Clinical Trial Design

Background:

  • Characterizing drug dose-response relationships is crucial in clinical trials.
  • Parametric, monotone functions are often used to model these relationships.
  • Accurate estimation of curve uncertainty is essential for reliable interpretation.

Purpose of the Study:

  • To develop a novel method for computing confidence intervals for nonlinear dose-response curves.
  • To address limitations of existing methods like Wald and bootstrap intervals.
  • To incorporate monotonicity constraints into confidence interval calculations.

Main Methods:

  • Proposed a profile likelihood approach for confidence interval estimation.
  • Compared performance against Wald and bootstrap confidence intervals.
  • Utilized Emax and sigmoid Emax models for simulations and data illustration.

Main Results:

  • Profile likelihood intervals demonstrated superior coverage properties compared to Wald intervals.
  • The proposed method showed improved precision and computational efficiency over bootstrap methods.
  • Monotonicity constraints were effectively integrated into the profile likelihood approach.

Conclusions:

  • The profile likelihood method provides a robust and efficient tool for quantifying uncertainty in dose-response modeling.
  • This approach offers advantages over existing methods, particularly in scenarios requiring monotonicity.
  • Accurate confidence intervals are vital for informed decision-making in drug development.