|
Abstract:
|
Treatment comparisons in randomized clinical trials usually involve several endpoints. Sometimes it is of interest to determine whether there is a treatment-associated improvement in disease status based on multiple endpoints, particularly if a treatment is expected to have the same directional effect on all of the endpoints. This gives rise to a multivariate one-sided hypothesis. Under the multivariate normality assumption, Perlman (1969) derived the likelihood-ratio test in the one-sample case; however, its null distribution depends on the unknown covariance matrix and it is biased. Wang and McDermott (1998) derived a conditional likelihood ratio test (CLRT), conditioning on a sufficient statistic for the covariance matrix, resulting in a uniformly more powerful test. Recently, Wang extended the CLRT to the two-sample case. Since the problem of missing data is ubiquitous in practical applications, we propose an extension of the complete-data CLRT to incorporate missing data with a missing at random (MAR) mechanism. We will illustrate the operating characteristics of the two-sample CLRT through simulation studies in the complete data and missing data cases.
|
