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Assessing the impact of selection bias on test decisions in trials with a time‐to‐event outcome
Author(s) -
Rückbeil Marcia Viviane,
Hilgers RalfDieter,
Heussen Nicole
Publication year - 2017
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.7299
Subject(s) - censoring (clinical trials) , selection bias , outcome (game theory) , type i and type ii errors , statistics , selection (genetic algorithm) , econometrics , event (particle physics) , computer science , randomization , mathematics , clinical trial , medicine , artificial intelligence , physics , mathematical economics , pathology , quantum mechanics
If past treatment assignments are unmasked, selection bias may arise even in randomized controlled trials. The impact of such bias can be measured by considering the type I error probability. In case of a normally distributed outcome, there already exists a model accounting for selection bias that permits calculating the corresponding type I error probabilities. To model selection bias for trials with a time‐to‐event outcome, we introduce a new biasing policy for exponentially distributed data. Using this biasing policy, we derive an exact formula to compute type I error probabilities whenever an F ‐test is performed and no observations are censored. Two exemplary settings, with and without random censoring, are considered in order to illustrate how our results can be applied to compare distinct randomization procedures with respect to their performance in the presence of selection bias. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.