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Asymptotic power calculations: description, examples, computer code.

B W Brown1, J Lovato, K Russell

  • 1Department of Biomathematics, Box 237, The University of Texas, M. D. Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA. bwb@mdanderson.org

Statistics in Medicine
|November 2, 1999
PubMed
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This study presents two asymptotic methods for calculating sample size and statistical power in hypothesis testing. These methods utilize chi-squared distributions and offer extensions for various parameter constraints, with software available.

Area of Science:

  • Statistics
  • Biostatistics
  • Hypothesis Testing

Background:

  • Accurate sample size and power calculations are crucial for robust hypothesis testing.
  • Existing methods may have limitations in handling complex parameter constraints.
  • Asymptotic approximations offer a computationally efficient approach to these calculations.

Purpose of the Study:

  • To introduce and describe two novel asymptotic methods for sample size and power calculations.
  • To demonstrate the flexibility of these methods in accommodating different parameter constraint types.
  • To provide practical guidance and software for their application in statistical analysis.

Main Methods:

  • The study employs asymptotic approximations based on the chi-squared distribution under null and alternative hypotheses.

Related Experiment Videos

  • Two distinct methods are presented, differing in their approximation of the non-centrality parameter.
  • Methods are extended to handle constraints setting parameters to constant values and equality of parameters.

    • Main Results:

    • The proposed asymptotic methods provide a framework for sample size and power calculations.
    • Examples illustrate the methods' applicability across various scenarios, including those with limited initial information.
    • One method, the 'information method,' is shown to potentially yield incorrect results in specific cases.

    Conclusions:

    • The described asymptotic methods offer a versatile approach to sample size and power determination in hypothesis testing.
    • The study highlights the importance of selecting appropriate approximation methods and provides practical examples.
    • A computer implementation in S-plus is announced, facilitating the use of these statistical tools.