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An efficient test for the analysis of dichotomized variables when the reliability is known
Author(s) -
Borm George F.,
Munneke Marten,
Lemmers Oscar,
van Zuijlen Martien
Publication year - 2006
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.2705
Subject(s) - statistics , outcome (game theory) , logistic regression , sample size determination , variable (mathematics) , reliability (semiconductor) , mathematics , range (aeronautics) , power (physics) , mathematical analysis , physics , mathematical economics , quantum mechanics , materials science , composite material
Abstract A difference in an outcome variable between the treatment groups in a trial does not necessarily mean that there is a difference in the number of patients who experience relevant improvement on that variable. When the relevant improvement corresponds with an outcome or change in outcome that exceeds a certain threshold, the outcome variable can be dichotomized. A responder is a patient whose outcome exceeds the threshold. Comparisons can be made between the number of responders in the two treatment groups using logistic regression, or some other method to evaluate binary outcomes. An important disadvantage of this approach is the loss of power. In general, it is more efficient to test the difference between the mean values. We developed a statistical test that compares response rates for a dichotomized variable. It requires that an estimate of the reliability of the outcome variable is available. Simulations showed that the test was valid and robust over a wide range of distributions and sample sizes. The power was greater than the power of a χ 2 test, which would enable substantial reduction in the sample size. Copyright © 2006 John Wiley & Sons, Ltd.