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On the properties of the toxicity index and its statistical efficiency
Author(s) -
Razaee Zahra S.,
Amini Arash A.,
Diniz Márcio A.,
Tighiouart Mourad,
Yothers Greg,
Rogatko André
Publication year - 2020
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.8858
Subject(s) - toxicity , sample size determination , statistics , poisson distribution , rank (graph theory) , statistical power , limit (mathematics) , ranking (information retrieval) , measure (data warehouse) , mathematics , poisson regression , confidence interval , population , computer science , medicine , data mining , artificial intelligence , mathematical analysis , environmental health , combinatorics
Cancer clinical trials typically generate detailed patient toxicity data. The most common measure used to summarize patient toxicity is the maximum grade among all toxicities and it does not fully represent the toxicity burden experienced by patients. In this article, we study the mathematical and statistical properties of the toxicity index (TI), in an effort to address this deficiency. We introduce a total ordering, (T‐rank), that allows us to fully rank the patients according to how frequently they exhibit toxicities, and show that TI is the only measure that preserves the T‐rank among its competitors. Moreover, we propose a Poisson‐Limit model for sparse toxicity data. Under this model, we develop a general two‐sample test, which can be applied to any summary measure for detecting differences among two population of toxicity data. We derive the asymptotic power function of this class as well as the asymptotic relative efficiency (ARE) of the members of the class. We evaluate the ARE formula empirically and show that if the data are drawn from a random Poisson‐Limit model, the TI is more efficient, with high probability, than the maximum and the average summary measures. Finally, we evaluate our method on clinical trial toxicity data and show that TI has a higher power in detecting the differences in toxicity profile among treatments. The results of this article can be applied beyond toxicity modeling, to any problem where one observes a sparse array of scores on subjects and a ranking based on extreme scores is desirable.