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Non-inferiority test based on transformations for non-normal distributions

  • ,
  • Arpita Chatterjee
    ,
  • Samiran Ghosh(corresponding author)
*Corresponding author for this work
Scholary Output:
Contribution to journal
Article
Peer-review

Abstract

Non-inferiority trials are becoming very popular for comparative effectiveness research. These trials are required to show that the effect of an experimental treatment is not worse than that of a reference treatment by more than a specified margin. Hence non-inferiority trials are of great importance, when superiority cannot be claimed. A three-arm non-inferiority trial consists of a placebo, a reference treatment, and an experimental treatment is considered. However unlike the traditional choices, it is assumed that the distributions of the end points corresponding to these treatments are unknown and suggested test procedures for a three-arm non-inferiority trial based on monotone transformations in conjunction with a normal approximation. The resulting test procedures are flexible and robust. Theoretical properties of the proposed methods are also investigated. The performance of the suggested test procedures is compared to their counterparts using simulations. In terms of type I error and power, the proposed methods perform better than their counterparts in most cases. The usefulness of the proposed methods is further illustrated through an example.

Publication Information

Output type

Scholary Output:
Contribution to journal
Article
Peer-review

Original language

English (US)

Pages from-to (Number of pages)

Pages 73-87 (15 pages)

Journal (Volume, Issue Number)

Computational Statistics and Data Analysis (Volume 113)

Publication milestones

  • Published - 09/2017

Publication status

Published - 09/2017

ISSN

0167-9473

Publication IDs

  • Scopus: 85006010318

Publication metrics

Metrics

Scopus
citations
Fractional count
1
Fractional count
0.33
Fractional count
2
Fractional count
0.67
Fractional count
1
Fractional count
1
SciVal
FWCI
0.29
SciVal
Author count
3
SciVal
citations
2
SciVal
Paper percentile
44

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Citation count
7
Usage
20
Captures
7

Funding Details

The research of last author is partly supported by PCORI Grant Number ME-1409-21410 and NIH Grant Number P30-ES020957. We would like to thank the Editor, Associate Editor and two anonymous referees for their careful reading and constructive suggestions which improved the readability of the paper.