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Semiparametric dose finding methods
Author(s) -
Clertant M.,
O’Quigley J.
Publication year - 2017
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/rssb.12229
Subject(s) - parametric statistics , class (philosophy) , interval (graph theory) , bayesian probability , semiparametric model , matching (statistics) , mathematics , computer science , econometrics , statistics , artificial intelligence , combinatorics
Summary We describe a new class of dose finding methods to be used in early phase clinical trials. Under some added parametric conditions the class reduces to the family of continual reassessment method (CRM) designs. Under some relaxation of the underlying structure the method is equivalent to the cumulative cohort design, the modified toxicity probability interval method or Bayesian optimal interval design classes of methods, which are non‐parametric in nature whereas the CRM class can be viewed as being strongly parametric. The class proposed is characterized as being semiparametric since it corresponds to the CRM with a nuisance parameter. Performance is good, matching that of the CRM class and improving on it in some cases. The structure allows theoretical questions to be more easily investigated and to understand better how different classes of methods relate to one another.