### Abstract

In this paper, the optimal sample sizes at the cluster and person levels for each of two treatment arms are obtained for cluster randomized trials where the cost-effectiveness of treatments on a continuous scale is studied. The optimal sample sizes maximize the efficiency or power for a given budget or minimize the budget for a given efficiency or power. Optimal sample sizes require information on the intra-cluster correlations (ICCs) for effects and costs, the correlations between costs and effects at individual and cluster levels, the ratio of the variance of effects translated into costs to the variance of the costs (the variance ratio), sampling and measuring costs, and the budget. When planning, a study information on the model parameters usually is not available. To overcome this local optimality problem, the current paper also presents maximin sample sizes. The maximin sample sizes turn out to be rather robust against misspecifying the correlation between costs and effects at the cluster and individual levels but may lose much efficiency when misspecifying the variance ratio. The robustness of the maximin sample sizes against misspecifying the ICCs depends on the variance ratio. The maximin sample sizes are robust under misspecification of the ICC for costs for realistic values of the variance ratio greater than one but not robust under misspecification of the ICC for effects. Finally, we show how to calculate optimal or maximin sample sizes that yield sufficient power for a test on the cost-effectiveness of an intervention.

Original language | English |
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Pages (from-to) | 2538-2553 |

Number of pages | 16 |

Journal | Statistics in Medicine |

Volume | 33 |

Issue number | 15 |

DOIs | |

Publication status | Published - 10 Jul 2014 |

### Keywords

- cluster randomized trials
- cost-effectiveness analysis
- maximin design
- optimal design
- sample size calculation
- MULTILEVEL MODELS
- STATISTICAL POWER
- CLINICAL-TRIALS
- OPTIMAL-DESIGN
- NET-BENEFIT
- UNCERTAINTY
- FRAMEWORK

## Cite this

*Statistics in Medicine*,

*33*(15), 2538-2553. https://doi.org/10.1002/sim.6112