Crippling New Treatments
- 101 Downloads
Conventional phase I clinical trials, in which a dose is chosen using adaptive decision rules based on toxicity but ignoring efficacy, are fundamentally flawed. This chapter will provide several illustrations of this important fact. The worst class of phase I “toxicity only” designs are so-called \(3+3\) algorithms, which are widely used but have terrible properties. Regardless of methodology, the conventionally small sample sizes of phase I trials provide very unreliable inferences about the relationship between dose and the risk of toxicity. More generally, the paradigm of first doing dose-finding in a phase I trial based on toxicity, and then doing efficacy evaluation in a phase II trial, is fundamentally flawed. This chapter will provide numerical illustrations of all of these problems. It will be explained, and illustrated by example, why the class of phase I–II trials, which are based on both efficacy and toxicity, provide a greatly superior general alternative to the conventional phase I \(\rightarrow \) phase II paradigm. The EffTox design of Thall and Cook (2004) and Thall et al. (2014a) will be reviewed and compared to a \(3+3\) algorithm and the continual reassessment method of O’Quigley et al. (1990) by computer simulation. It will be argued that, because conventional methods do a very poor job of identifying a safe and effective dose, it is very likely that, at the start of clinical evaluation, they cripple many new treatments by choosing a suboptimal dose that is unsafe, ineffective, or both.