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Statistical power and sample size calculations: A primer for pediatric surgeons,☆☆

https://doi.org/10.1016/j.jpedsurg.2019.05.007Get rights and content

Abstract

Background/Purpose

Determining the appropriate sample size is an integral component of any well-designed research study, grant application, or scientific manuscript. Surgeons intuitively understand the concept of statistical power, but have limited knowledge in how to go about performing the calculations correctly. Our goal is to provide a strategy for pediatric surgeons to use when planning a study to determine the sample sizes required for detecting a clinically meaningful effect, which is important for interpreting and validating their results.

Methods

We present a general 5-step approach for performing a sample size justification and statistical power analysis, and illustrate this approach using several surgical research examples. The 5 steps are: 1) Define the primary outcome of interest, 2) Define the magnitude of the effect or effect size and power desired, 3) Determine the appropriate statistics and statistical test that will be considered, 4) Perform the calculations to estimate the required sample size using software or a reference table, 5) Write the formal power and sample size statement for the manuscript, grant application, or project proposal.

Conclusions

Understanding sample size considerations and statistical power in the surgical research community will improve the quality of published articles. This primer can be used by pediatric surgeons in the process of determining the appropriate sample sizes for detecting a clinically meaningful effect with sufficient statistical power. Virtually all research studies in pediatric surgery should include a justification of sample size based on a power calculation as this leads to more meaningful inferences from the data and analysis.

Type of study

Review article.

Level of evidence

N/A.

Section snippets

Methods

We present a practical 5-step approach for the pediatric surgeon to determine the appropriate sample sizes needed to detect clinically and scientifically meaningful results based on sufficient statistical power. These steps can be applied to any prospective or retrospective research study scenario regardless of the nature of the data. The 5 steps are:

  • 1.

    Define the outcome variable of interest and comparison groups and formulate the clinical or scientific hypothesis. The outcome of interest may be

Discussion

We have demonstrated how to perform statistical power and sample size calculations with our 5-step approach using examples in pediatric surgery. Regardless of the research study, the nature of the outcome or exposure data, or the study design, our 5-step approach can be applied to perform power calculations correctly to determine the minimum required sample sizes to ensure sufficient power and meaningful interpretation of the study data.

It may be challenging to obtain sample sizes as large as

Conclusions

A properly performed and described power and sample size calculation improves the quality of manuscripts submitted to surgical journals, IRB proposals, and grant applications. The clinical hypotheses and the nature of the data guide the appropriate statistical test to be used in sample size calculations. Our article can be utilized as a practical guide for pediatric surgeons when designing a clinical study or laboratory investigation to ensure that the sample sizes will provide the sufficient

References (21)

  • E.C. Hedberg et al.

    The power of a paired t-test with a covariate

    Soc Sci Res

    (2015)
  • R. Bender et al.

    Adjusting for multiple testing—when and how?

    J Clin Epidemiol

    (2001)
  • A.D. Althouse

    Adjust for multiple comparisons? It's not that simple

    Ann Thorac Surg

    (2016)
  • S.M. Gore et al.

    How large a sample?

  • D. Moher et al.

    Statistical power, sample size, and their reporting in randomized controlled trials

    JAMA

    (1994)
  • S. Adie et al.

    CONSORT compliance in surgical randomized trials: are we there yet? A systematic review

    Ann Surg

    (2013)
  • U. Ahmed Ali et al.

    Trends in worldwide volume and methodological quality of surgical randomized controlled trials

    Ann Surg

    (2013)
  • R. Rosenthal et al.

    Comparison of randomized controlled trial registry entries and content of reports in surgery journals

    Ann Surg

    (2013)
  • E. Erdfelder et al.

    GPOWER: a general power analysis program

    Behav Res Methods Instrum Comput

    (1996)
  • F. Faul et al.

    G*Power 3: a flexible statistical power analysis for the social, behavioral, and biomedical sciences

    Behav Res Methods

    (2007)
There are more references available in the full text version of this article.

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Declarations of interest: none.

☆☆

Financial disclosures: none.

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