Stopping Rules and Data Monitoring in Clinical Trials
Stopping rules—rules dictating when to stop accumulating data and start analyzing it for the purposes inferring from the experiment—divide Bayesians, Likelihoodists and classical statistical approaches to inference. Although the relationship between Bayesian philosophy of science and stopping rules can be complex (cf. Steel 2003), in general, Bayesians regard stopping rules as irrelevant to what inference should be drawn from the data. This position clashes with classical statistical accounts. For orthodox statistics, stopping rules do matter to what inference should be drawn from the data. “The dispute over stopping rule is far from being a marginal quibble, but is instead a striking illustration of the divergence of fundamental aims and standards separating Bayesians and advocates of orthodox statistical methods” (Steel 2004, 195).
KeywordsInterim Analysis Data Monitor Committee Conditional Power Likelihood Principle Toxoplasmic Encephalitis
I am grateful to Paul Bartha for his supervision, helpful discussion and feedback. I am also grateful to two anonymous reviewers for their comments and criticisms, and the audience at EPSA 2009 in Amsterdam. Earlier version of this work was presented at the PSX in the Center for Philosophy of Science at the University of Pittsburgh.
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