### Abstract

One of the criteria for demonstrating efficacy in a single multicenter trial is that the centers are consistent with respect to the direction and significance of results. The purpose of this research is to discuss the use of the noncentrality parameter A of an F distribution as a means of testing for the consistency of treatment effects across centers. We state the testing problem as H_{0}: {δ > δ_{0}} versus H_{1}: {A_{0}}, where δ_{0}is prespecified, so that H_{0}represents inconsistency and H_{1}consistency. Thus, strong evidence from the sample data is required in order to conclude that the treatment effects are consistent across centers, We discuss reasonable choices forδ_{0} and develop the a-level, uniformly most powerful and unbiased test, which is equivalent to rejecting H_{0}if the 100(1 - a)% uniformly most accurate and unbiased upper confidence limit for A is less than or equal to δ_{0}. We examine other tests based on upper confidence limits, such as those calculated from linear estimators of A and those calculated from a likelihood approach. We investigate the performance of the tests in a small simulation study and present an example from a four-center clinical trial.

Original language | English (US) |
---|---|

Pages (from-to) | 67-80 |

Number of pages | 14 |

Journal | Journal of Biopharmaceutical Statistics |

Volume | 1 |

Issue number | 1 |

DOIs | |

State | Published - Jan 1 1991 |

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### All Science Journal Classification (ASJC) codes

- Statistics and Probability
- Pharmacology
- Pharmacology (medical)

### Cite this

*Journal of Biopharmaceutical Statistics*,

*1*(1), 67-80. https://doi.org/10.1080/10543409108835006

}

*Journal of Biopharmaceutical Statistics*, vol. 1, no. 1, pp. 67-80. https://doi.org/10.1080/10543409108835006

**Testing for consistency in a single multicenter trial.** / Chinchilli, Vernon; Bortey, Enoch B.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Testing for consistency in a single multicenter trial

AU - Chinchilli, Vernon

AU - Bortey, Enoch B.

PY - 1991/1/1

Y1 - 1991/1/1

N2 - One of the criteria for demonstrating efficacy in a single multicenter trial is that the centers are consistent with respect to the direction and significance of results. The purpose of this research is to discuss the use of the noncentrality parameter A of an F distribution as a means of testing for the consistency of treatment effects across centers. We state the testing problem as H0: {δ > δ0} versus H1: {A0}, where δ0is prespecified, so that H0represents inconsistency and H1consistency. Thus, strong evidence from the sample data is required in order to conclude that the treatment effects are consistent across centers, We discuss reasonable choices forδ0 and develop the a-level, uniformly most powerful and unbiased test, which is equivalent to rejecting H0if the 100(1 - a)% uniformly most accurate and unbiased upper confidence limit for A is less than or equal to δ0. We examine other tests based on upper confidence limits, such as those calculated from linear estimators of A and those calculated from a likelihood approach. We investigate the performance of the tests in a small simulation study and present an example from a four-center clinical trial.

AB - One of the criteria for demonstrating efficacy in a single multicenter trial is that the centers are consistent with respect to the direction and significance of results. The purpose of this research is to discuss the use of the noncentrality parameter A of an F distribution as a means of testing for the consistency of treatment effects across centers. We state the testing problem as H0: {δ > δ0} versus H1: {A0}, where δ0is prespecified, so that H0represents inconsistency and H1consistency. Thus, strong evidence from the sample data is required in order to conclude that the treatment effects are consistent across centers, We discuss reasonable choices forδ0 and develop the a-level, uniformly most powerful and unbiased test, which is equivalent to rejecting H0if the 100(1 - a)% uniformly most accurate and unbiased upper confidence limit for A is less than or equal to δ0. We examine other tests based on upper confidence limits, such as those calculated from linear estimators of A and those calculated from a likelihood approach. We investigate the performance of the tests in a small simulation study and present an example from a four-center clinical trial.

UR - http://www.scopus.com/inward/record.url?scp=0026362127&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0026362127&partnerID=8YFLogxK

U2 - 10.1080/10543409108835006

DO - 10.1080/10543409108835006

M3 - Article

C2 - 1844692

AN - SCOPUS:0026362127

VL - 1

SP - 67

EP - 80

JO - Journal of Biopharmaceutical Statistics

JF - Journal of Biopharmaceutical Statistics

SN - 1054-3406

IS - 1

ER -