Sample size calculation for comparing time-averaged responses in K-group repeated binary outcomes

Jijia Wang, Song Zhang, Chul Ahn

Research output: Contribution to journalArticle

Abstract

In clinical trials with repeated measurements, the time-averaged difference (TAD) may provide a more powerful evaluation of treatment efficacy than the rate of changes over time when the treatment effect has rapid onset and repeated measurements continue across an extended period after a maximum effect is achieved (Overall and Doyle, Controlled Clinical Trials, 15, 100-123, 1994). The sample size formula has been investigated by many researchers for the evaluation of TAD in two treatment groups. For the evaluation of TAD in multi-arm trials, Zhang and Ahn (Computational Statistics & Data Analysis, 58, 283-291, 2013) and Lou et al. (Communications in Statistics-Theory and Methods, 46, 11204-11213, 2017b) developed the sample size formulas for continuous outcomes and count outcomes, respectively. In this paper, we derive a sample size formula to evaluate the TAD of the repeated binary outcomes in multi-arm trials using the generalized estimating equation approach. This proposed sample size formula accounts for various correlation structures and missing patterns (including a mixture of independent missing and monotone missing patterns) that are frequently encountered by practitioners in clinical trials. We conduct simulation studies to assess the performance of the proposed sample size formula under a wide range of design parameters. The results show that the empirical powers and the empirical Type I errors are close to nominal levels. We illustrate our proposed method using a clinical trial example.

Original languageEnglish (US)
Pages (from-to)321-328
Number of pages8
JournalCommunications for Statistical Applications and Methods
Volume25
Issue number3
DOIs
StatePublished - May 1 2018

Fingerprint

Sample Size Calculation
Binary Outcomes
K-group
Response Time
Sample Size
Clinical Trials
Statistics
Repeated Measurements
Evaluation
Computational Statistics
Communication
Generalized Estimating Equations
Rate of change
Type I error
Correlation Structure
Treatment Effects
Parameter Design
Categorical or nominal
Efficacy
Data analysis

Keywords

  • Multi-arm trials
  • Sample size formula
  • Time-averaged difference

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Finance
  • Applied Mathematics
  • Modeling and Simulation
  • Statistics and Probability

Cite this

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