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Targeted Therapy With the T-Cell–Engaging Antibody Blinatumomab of Chemotherapy-Refractory Minimal Residual Disease in B-Lineage Acute Lymphoblastic Leukemia Patients Results in High Response Rate and Prolonged Leukemia-Free Survival

**Objective: **To determine the efficacy of blinatumomab in MRD-positive B-lineage ALL.

**Year:** 2011

**Source:** Journal of Clinical Oncology

**Link:** https://ascopubs.org/doi/full/10.1200/JCO.2010.32.7270

**Clinical Area:** Oncology

Sample Size Section in Paper/Protocol: |

“The primary end point of this study was the MRD response rate, as defined by the incidence of MRD negativity within four cycles of treatment with blinatumomab. Simon’s MinMax two-stage design with parameters P0 = 0.05, P1 = 0.3, ⍺= 0.05, and 𝛽 = 0.2 resulted in two stages of seven patients each. The sample size was expanded to 21 patients to enroll more patients with BCR-ABL translocation.” |

Summary of Necessary Parameter Estimates for Sample Size Calculation:

Parameter |
Value |

Significance Level, ⍺ | 0.05 |

Maximum Ineffective Proportion, 𝜋 0 | 0.05 |

Minimum Efficacy Proportion, 𝜋 1 | 0.03 |

Power | 80% |

Design Criterion | Minimax |

**Step 1: **Select the

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

**Step 2:**Enter the parameter values for sample size calculation taken from the study design. The required sample size will automatically calculate once values for ⍺, 𝜋 0 , 𝜋 1 and Power are entered. (Note that the “Optimum Search Value” and “Lower N Limit” parameters are only required under the “Optimum” design criterion.)

The analysis results in a total sample size of 14, with 7 patients at stage one and a further 7 added for stage two. The actual significance level is 0.027 and the power of the test is 81.01%. At the final analysis, if two or less responses are observed in total, then the drug is rejected. The overall probability of early termination is 69.8%

**Step 3:**Once the calculation is completed, nQuery provides an output statement summarizing the results.

** It ****states:**

“Under the minimax design criterion, a sample size of 14 is required to test a null hypothesis of H₀: π ≤ 0.05 versus an alternative hypothesis of H₁: π ≥ 0.3 with a one-sided significance level of 0.027 and 81.01% power, where π is the true proportion of successes. This design results in an expected sample size of 9.112 and a probability of early termination of 0.698.

If the number of responses is less than or equal to 0 out of 7 subjects in the first stage then the trial will be stopped. If the trial proceeds to the second stage, 14 subjects in total will be studied. If 2 or less responses are observed, then the drug or treatment is rejected.”

**Step 4:**We can compare the Minimax design with the Optimum and Single Stage designs by completing two additional columns in nQuery with the same ⍺, 𝜋 0 , 𝜋 1 and power parameters and selecting the appropriate Design Criterion.

In this case, the optimum design results in a total sample size of 18, while the single-stage and

minimax designs result in a total sample size of 14.

**Step 5:**nQuery advanced also provides plotting options. To access the plotting tools, highlight the

completed columns that you wish to work with, go to the menu and select:

Plot > User-Selected Rows.

In this case, we will display how the total sample size for each design criterion is affected when the maximum ineffective proportion varies between 0.01 and 0.1.

The **Edit** button at the top of the output allows users to customise the appearance of the plot.

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