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Related Concept Videos

Study Design in Statistics01:15

Study Design in Statistics

A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
Experimental Designs01:16

Experimental Designs

An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...
Study Designs in Epidemiology01:20

Study Designs in Epidemiology

Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
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Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs01:20

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

Bioequivalence experimental study designs are crucial methodologies used in evaluating and comparing the bioavailability of different drug products. These designs are categorized into various types: completely randomized, randomized block, repeated measures, cross and carry-over, and Latin square designs.Completely randomized designs involve randomly allocating treatments to all subjects participating in the experiment. This allocation is achieved by assigning unique random numbers to subjects...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...

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Updated: Jun 9, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Design of Trials with Composite Endpoints with the R Package CompAREdesign.

Jordi Cortés-Martínez1, Marta Bofill-Roig1,2, Guadalupe Gómez-Melis1

  • 1Research group in Biostatistics and Bioinformatics, GRBIO, Department of Statistics and Operations Research and Institute for Research and Innovation in Health (IRIS), Universitat Politècnica de Catalunya (BarcelonaTech), Jordi Girona 1-3, 08034 Barcelona, Spain.

Statistics in Biosciences
|June 8, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces CompAREdesign, an R package for designing clinical trials with composite endpoints. It addresses challenges like the proportional hazards assumption violation, offering novel methods for sample size and effect size calculations.

Keywords:
Clinical trialComposite endpointsR packageSurvival analysis

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Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Statistical Software Development

Background:

  • Composite endpoints are common in time-to-event clinical trials.
  • Proportional hazards assumption violations complicate trial design and sample size calculations.
  • Existing statistical software often lacks specific tools for composite endpoint trial design.

Purpose of the Study:

  • Introduce the R package CompAREdesign for clinical trial design.
  • Provide novel methodologies for sample size and effect size computation for composite endpoints.
  • Facilitate the design of trials with binary composite endpoints and offer simulation capabilities.

Main Methods:

  • Development of the R package CompAREdesign.
  • Incorporation of novel methodologies for composite endpoint trial design.
  • Implementation of functions for sample size, effect size calculation, and trial simulation.

Main Results:

  • CompAREdesign offers solutions for designing clinical trials with composite endpoints.
  • The package calculates key design elements based on component endpoint information.
  • It supports the design of trials with binary composite endpoints and includes simulation tools.

Conclusions:

  • CompAREdesign is the first R package specifically for the design phase of clinical trials with composite endpoints.
  • The package addresses critical design challenges, including proportional hazards assumption violations.
  • CompAREdesign is available on CRAN, providing a valuable tool for researchers.