TY - JOUR
T1 - Type I error and power in noninferiority/equivalence trials with correlated multiple endpoints
T2 - An example from vaccine development trials
AU - Kong, Lan
AU - Kohberger, Robert C.
AU - Koch, Gary G.
N1 - Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
PY - 2004
Y1 - 2004
N2 - Clinical trials necessary for the development of new treatment often require testing of multiple endpoints for equivalence or noninferiority relative to an existing effective standard therapy. An example is a vaccine study with multiple antibody measurements in sera of subjects receiving a combination vaccine such as a pneumococcal vaccine, which contains many different serotypes of the pneumococcal organism. This article describes testing methods for the demonstration of simultaneous marginal equivalence or noninferiority of two treatments on each component of the response vector that follows a multivariate normal distribution. Systematic simulation studies are conducted to evaluate the performance of the testing method and to examine under what conditions the power is substantially different if the multiple endpoints are assumed to be independent when they are actually strongly correlated. Data from an illustrative example are used to describe how the study power can be evaluated in the design of the trials.
AB - Clinical trials necessary for the development of new treatment often require testing of multiple endpoints for equivalence or noninferiority relative to an existing effective standard therapy. An example is a vaccine study with multiple antibody measurements in sera of subjects receiving a combination vaccine such as a pneumococcal vaccine, which contains many different serotypes of the pneumococcal organism. This article describes testing methods for the demonstration of simultaneous marginal equivalence or noninferiority of two treatments on each component of the response vector that follows a multivariate normal distribution. Systematic simulation studies are conducted to evaluate the performance of the testing method and to examine under what conditions the power is substantially different if the multiple endpoints are assumed to be independent when they are actually strongly correlated. Data from an illustrative example are used to describe how the study power can be evaluated in the design of the trials.
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U2 - 10.1081/BIP-200035454
DO - 10.1081/BIP-200035454
M3 - Review article
C2 - 15587971
AN - SCOPUS:9244234992
SN - 1054-3406
VL - 14
SP - 893
EP - 907
JO - Journal of Biopharmaceutical Statistics
JF - Journal of Biopharmaceutical Statistics
IS - 4
ER -