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Related Concept Videos

Sample Size Calculation01:19

Sample Size Calculation

Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
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  3. Sample Size Calculation For The Roci Design.
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  3. Sample Size Calculation For The Roci Design.

Related Experiment Video

Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

Sample Size Calculation for the ROCI Design.

Henry Bern1, James Carpenter1, Mahesh Parmar1

  • 1Institute of Clinical Trials and Methodology, MRC Clinical Trials Unit at UCL, London, UK.

Statistics in Medicine
|May 25, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a faster method for calculating sample sizes for Response Over Continuous Intervention (ROCI) trials. The new approach simplifies sample size estimation for treatment de-escalation studies.

Keywords:
Response Over Continuous Intervention (ROCI)bootstrapdelta methodflexible modelingfractional polynomialsintervention optimizationnormal approximationsample size calculation

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Pharmacometrics

Background:

  • Response Over Continuous Intervention (ROCI) designs offer advantages for optimizing continuous treatment interventions.
  • ROCI designs are applicable to de-escalation trials aiming to reduce treatment intensity.
  • Current sample size calculations for ROCI designs rely on computationally intensive simulations.

Purpose of the Study:

  • To propose a normal approximation approach for sample size calculation in ROCI designs.
  • To evaluate methods for estimating nuisance parameters crucial for sample size determination.
  • To reduce the computational burden associated with sample size estimation in ROCI trials.

Main Methods:

  • Developed a normal approximation method for sample size calculation.
  • Assessed various techniques for estimating nuisance parameters, considering reliability and computational cost.
  • Utilized fractional polynomial regression to model intervention-response curves within the ROCI framework.

    • Main Results:

    • The normal approximation approach is well-founded for ROCI designs.
    • This methodology significantly reduces computational demands compared to simulation-based methods.
    • Reliable estimation of distribution parameters necessitates methods addressing model selection uncertainty.

    Conclusions:

    • Normal approximation offers an efficient and valid method for sample size calculation in ROCI trials.
    • The proposed framework streamlines sample size estimation, making ROCI designs more accessible.
    • The approach is adaptable to ROCI designs employing diverse flexible modeling strategies.