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Power, sample size and sampling costs for clustered data

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  • Tokola, K.
  • Larocque, D.
  • Nevalainen, J.
  • Oja, H.

Abstract

The data collected in epidemiological or clinical studies are frequently clustered. In such settings, appropriate variance adjustments must be made in order to estimate the sufficient sample size correctly. This paper works through the sample size calculations for clustered data. Importantly, our explicit variance expressions also enable us to optimize the design with respect to the number of clusters and number of subjects; the objective could be either to maximize the power or to minimize the costs with given costs on the clusters and on the individuals. In our approach, units on different levels and treatment groups can have different costs, but the members of the same cluster are assumed to belong to the same treatment group. Design considerations in the health coaching project TERVA are used as motivating examples. R-functions for carrying out the computations presented are provided.

Suggested Citation

  • Tokola, K. & Larocque, D. & Nevalainen, J. & Oja, H., 2011. "Power, sample size and sampling costs for clustered data," Statistics & Probability Letters, Elsevier, vol. 81(7), pages 852-860, July.
    • Handle: RePEc:eee:stapro:v:81:y:2011:i:7:p:852-860
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    References listed on IDEAS

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    1. Ronald E. Gangnon, 2004. "Sample-size formula for clustered survival data using weighted log-rank statistics," Biometrika, Biometrika Trust, vol. 91(2), pages 263-275, June.
    2. Moonseong Heo & Andrew C. Leon, 2008. "Statistical Power and Sample Size Requirements for Three Level Hierarchical Cluster Randomized Trials," Biometrics, The International Biometric Society, vol. 64(4), pages 1256-1262, December.
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    Cited by:

    1. Kari Tokola & Andreas Lundell & Jaakko Nevalainen & Hannu Oja, 2014. "Design and cost optimization for hierarchical data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(2), pages 130-148, May.
    2. Md Abu Manju & Math J. J. M. Candel & Gerard J. P. van Breukelen, 2019. "SamP2CeT: an interactive computer program for sample size and power calculation for two-level cost-effectiveness trials," Computational Statistics, Springer, vol. 34(1), pages 47-70, March.

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