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**Objective: **The objective of the study was to evaluate the utility of a Petrifilm-based on-farm culture system when used to make selective antimicrobial treatment decisions on low somatic cell count cows (<200,000 cells/mL) at drying off. A total of 729 cows from 16 commercial dairy herds with a low bulk tank somatic cell count (<250,000 cells/mL) were randomly assigned to receive either blanket dry cow therapy (DCT) or Petrifilm-based selective DCT.

**Year:** 2014

**Source:** Journal of Dairy Science

**Link: **http://www.sciencedirect.com/science/article/pii/S0022030213007352

**Clinical Area:** Veterinary

Sample Size Section in Paper/Protocol: |

“Sample size calculations were based on a published prevalence of IMI at calving |

Summary of Necessary Parameter Estimates for Sample Size Calculation:

Parameter |
Value |

Significance Level (2-Sided) | 0.05 |

Expected Prevalence Difference | -0.05 |

Control Group Prevalence | 0.2 |

ICC | 0.2 |

Sample Size per Cluster/Cow | 4 |

Cluster/Cows per Group | 454 |

**Step 1: **

Select the** CRT Two Proportions Inequality Completely Randomised** table.

This can be done **using the radio buttons** or alternatively, you can** use the search bar** at the end of the Select Test Design & Goal window.

**Step 2:**Enter the parameter values for power calculation taken from the study protocol.

**Step 3:**Once the parameter values are entered from Step 2,

This analysis gives a power of 81.529 / 81.53 as per the targeted power of 80% |

**The slight increase in power could be due to rounding or different assumptionsregarding the test statistic used e.g. continuity correction, chi-squared statistic,sine adjustment.**

**Note:** When using nQuery Advanced both the Cluster Sample Size Ratio and Power (%) will be auto-calculated once all the parameter values from Step 2 are entered.

**Step 4:**Once the calculation is completed, nQuery Advanced provides an output statement summarizing the results. It States:

Output Statement: |

“In a cluster randomized trial comparing two binary variables, a sample size of 454 clusters with 4 individuals per cluster in the treatment group and a sample size of 454 clusters per group with 4 individuals per cluster in the control group achieves 81.53% power to detect a difference between two proportions when the Differences under null hypothesis and alternative hypotheses are 0 and 0.05 respectively, the control group proportion is 0.2, the intracluster correlation is 0.2 and when using a two-Sided test at the 0.05 significance level using the Unpooled score statistic.” |

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