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Sample size calculations for controlled clinical trials using generalized estimating equations (GEE).

G Dahmen1, J Rochon, I R König

  • 1Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Ratzeburger Allee 160, Haus 4, 23538 Lübeck, Germany. dahmen@imbs.uni-luebeck.de

Methods of Information in Medicine
|February 11, 2005
PubMed
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This study introduces a flexible SAS macro, GEESIZE, for calculating sample sizes in clinical trials with correlated data. It supports both generalized estimating equations (GEE) and independence estimating equations (IEE), enhancing trial design accuracy.

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Longitudinal Data Analysis

Background:

  • Generalized Estimating Equations (GEE) are popular for correlated data in clinical trials, often requiring smaller sample sizes than traditional methods.
  • Independence Estimating Equations (IEE) are recommended for primary analysis in controlled trials over GEE with estimated correlations.
  • Existing sample size and power calculation methods for correlated data are often too specific and lack generalizability for robust clinical trial design.

Purpose of the Study:

  • To enhance the GEESIZE SAS macro for more general sample size and power calculations in clinical trials with correlated response data.
  • To incorporate the independence working correlation matrix option for Independence Estimating Equations (IEE) analysis.
  • To reformulate hypotheses for improved coding, including intercept terms, enhancing the macro's applicability.

Related Experiment Videos

Main Methods:

  • Extended the existing GEESIZE SAS macro to include an independence working correlation matrix for IEE.
  • Modified hypothesis coding to accommodate intercept terms, moving beyond traditional analysis of variance coding.
  • Validated the extended GEESIZE macro by comparing calculated sample sizes against models with available closed-form solutions.

Main Results:

  • Demonstrated the validity of the enhanced GEESIZE macro through comparisons with established statistical models.
  • Illustrated the macro's practical application by using it to plan sample size for a hypertension treatment trial.
  • Confirmed the flexibility and utility of the GEESIZE macro in various clinical trial scenarios.

Conclusions:

  • The enhanced GEESIZE SAS macro provides a general and valuable tool for sample size determination in clinical trials involving correlated data.
  • The macro's ability to handle both GEE and IEE, along with flexible hypothesis coding, improves the precision of clinical trial planning.
  • This freely available tool supports more accurate and efficient design of clinical trials with complex data structures.