Advertisement

Statistical Power and Sample Size: Some Fundamentals for Clinician Researchers

  • J. Michael Oakes

Abstract

This chapter aims to arm clinical researchers with the necessary conceptual and practical tools (1) to understand what sample size or power analysis is, (2) to conduct such analyses for basic low-risk studies, and (3) to recognize when it is necessary to seek expert advice and input. I hope it is obvious that this chapter aims to serve as a general guide to the issues; specific details and mathematical presentations may be found in the cited literature. Additionally, it should be obvious that this discussion of statistical power is focused, appropriately, on quantitative investigations into real or hypothetical effects of treatments or interventions. It does not address qualitative study designs. The ultimate goal here is to help practicing clinical researcher get started with power analyses.

Keywords

Null Hypothesis Power Analysis Clinical Researcher Hypothetical Effect Sample Size Group 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Baussell RB, Li Y-F. Power Analysis for Experimental Research: A Practical Guide for the Biological, Medical and Social Sciences. New York: Cambridge; 2002 (p ix).Google Scholar
  2. 2.
    Herman A, Notzer N, Libman Z, Braunstein R, Steinberg DM. Statistical education for medical students–concepts are what remain when the details are forgotten. Stat Med. Oct 15, 2007; 26(23):4344–4351.PubMedCrossRefGoogle Scholar
  3. 3.
    Berry DA. Introduction to Bayesian methods III: use and interpretation of Bayesian tools in design and analysis. Clin Trials. 2005; 2(4):295–300; discussion 301–294, 364–278.PubMedCrossRefGoogle Scholar
  4. 4.
    Berry DA. Bayesian statistics. Med Decis Making. Sep–Oct 2006; 26(5):429–430.PubMedCrossRefGoogle Scholar
  5. 5.
    Browne RH. Using the sample range as a basis for calculating sample size in power calculations. Am Statistician. 2001; 55:293–298.CrossRefGoogle Scholar
  6. 6.
    Bloom HS. Minimum detectable effects: a simple way to report the statistical power of experimental designs. Evaluat Rev. Oct 1995; 10(5):547–556.CrossRefGoogle Scholar
  7. 7.
    Greenland S. Power, sample size and smallest detectable effect determination for multivariate studies. Stat Med. Apr–June 1985; 4(2):117–127.PubMedCrossRefGoogle Scholar
  8. 8.
    Poole C. Low P-values or narrow confidence intervals: which are more durable? Epidemiology. May 2001; 12(3):291–294.PubMedCrossRefGoogle Scholar
  9. 9.
    Savitz DA, Tolo KA, Poole C. Statistical significance testing in the American Journal of Epidemiology, 1970–1990. Am J Epidemiol. May 15, 1994; 139(10):1047–1052.PubMedGoogle Scholar
  10. 10.
    Sterne JA. Teaching hypothesis tests–time for significant change? Stat Med. Apr 15, 2002; 21(7):985–994; discussion 995–999, 1001.PubMedCrossRefGoogle Scholar
  11. 11.
    Greenland S. On sample-size and power calculations for studies using confidence intervals. Am J Epidemiol. July 1988; 128(1):231–237.PubMedGoogle Scholar
  12. 12.
    Hintz J. PASS 2008, NCSSLLC. www.ncss.com.
  13. 13.
    Hoenig JM, Heisey D. The abuse of power: the pervasive fallacy of power calculations for data analysis. Am Stat. 2001; 55:19–24.CrossRefGoogle Scholar
  14. 14.
    Chow S-C, Shao J, Wang H. Sample Size Calculations in Clinical Research. New York: Marcel Dekker; 2003.Google Scholar
  15. 15.
    Lipsey M. Design Sensitivity: Statistical Power for Experimental Research. Newbury Park, CA: Sage; 1990.Google Scholar
  16. 16.
    Maxwell SE, Kelly K, Rausch JR. Sample size planning for statistical power and accuracy in parameter estimation. Ann Rev Psychol. 2008; 59:537–563.CrossRefGoogle Scholar
  17. 17.
    Oakes JM, Feldman HA. Statistical power for nonequivalent pretest-posttest designs. The impact of change-score versus ANCOVA models. Eval Rev. Feb 2001; 25(1):3–28.PubMedCrossRefGoogle Scholar
  18. 18.
    Feldman HA, McKinlay SM. Cohort versus cross-sectional design in large field trials: precision, sample size, and a unifying model. Stat Med. Jan 15, 1994; 13(1):61–78.PubMedCrossRefGoogle Scholar
  19. 19.
    Armstrong B. A simple estimator of minimum detectable relative risk, sample size, or power in cohort studies. Am J Epidemiol. Aug 1987; 126(2):356–358.PubMedGoogle Scholar
  20. 20.
    Greenland S. Tests for interaction in epidemiologic studies: a review and a study of power. Stat Med. Apr–June 1983; 2(2):243–251.PubMedCrossRefGoogle Scholar
  21. 21.
    Self SG, Mauritsen RH. Power/sample size calculations for generalized linear models. Biometrics. 1988; 44:79–86.CrossRefGoogle Scholar

Copyright information

© Springer Science + Business Media B.V 2008

Authors and Affiliations

  • J. Michael Oakes
    • 1
  1. 1.School of Public HealthUniversity of MinnesotaMinneapolis

Personalised recommendations