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A flexible multi‐domain test with adaptive weights and its application to clinical trials
Author(s) -
Zhao Yang,
Yu Qifeng,
Lake Stephen L.
Publication year - 2019
Publication title -
pharmaceutical statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 38
eISSN - 1539-1612
pISSN - 1539-1604
DOI - 10.1002/pst.1993
Subject(s) - resampling , test statistic , type i and type ii errors , computer science , statistics , clinical trial , statistical hypothesis testing , test (biology) , data mining , mathematics , machine learning , algorithm , medicine , pathology , paleontology , biology
Summary The design of a clinical trial is often complicated by the multi‐systemic nature of the disease; a single endpoint often cannot capture the spectrum of potential therapeutic benefits. Multi‐domain outcomes which take into account patient heterogeneity of disease presentation through measurements of multiple symptom/functional domains are an attractive alternative to a single endpoint. A multi‐domain test with adaptive weights is proposed to synthesize the evidence of treatment efficacy over numerous disease domains. The test is a weighted sum of domain‐specific test statistics with weights selected adaptively via a data‐driven algorithm. The null distribution of the test statistic is constructed empirically through resampling and does not require estimation of the covariance structure of domain‐specific test statistics. Simulations show that the proposed test controls the type I error rate, and has increased power over other methods such as the O'Brien and Wei‐Lachin tests in scenarios reflective of clinical trial settings. Data from a clinical trial in a rare lysosomal storage disorder were used to illustrate the properties of the proposed test. As a strategy of combining marginal test statistics, the proposed test is flexible and readily applicable to a variety of clinical trial scenarios.