Abstract
BackgroundThe overwhelming majority of dose-escalation clinical trials use methods that seek a maximum tolerable dose, including rule-based methods like the 3+3, and model-based methods like CRM and EWOC. These methods assume that the incidences of efficacy and toxicity always increase as dose is increased. This assumption is widely accepted with cytotoxic therapies. In recent decades, however, the search for novel cancer treatments has broadened, increasingly focusing on inhibitors and antibodies. The rationale that higher doses are always associated with superior efficacy is less clear for these types of therapies.MethodsWe extracted dose-level efficacy and toxicity outcomes from 115 manuscripts reporting dose-finding clinical trials in cancer between 2008 and 2014. We analysed the outcomes from each manuscript using flexible non-linear regression models to investigate the evidence supporting the monotonic efficacy and toxicity assumptions.ResultsWe found that the monotonic toxicity assumption was well-supported across most treatment classes and disease areas. In contrast, we found very little evidence supporting the monotonic efficacy assumption.ConclusionsOur conclusion is that dose-escalation trials routinely use methods whose assumptions are violated by the outcomes observed. As a consequence, dose-finding trials risk recommending unjustifiably high doses that may be harmful to patients. We recommend that trialists consider experimental designs that allow toxicity and efficacy outcomes to jointly determine the doses given to patients and recommended for further study.
Highlights
The overwhelming majority of dose-escalation clinical trials use methods that seek a maximum tolerable dose, including rule-based methods like the 3+3, and model-based methods like continual reassessment method (CRM) and escalation with overdose control (EWOC)
We see that the phenomena we have described are broadly observed across most disease types
The observation remained that dose-limiting toxicity (DLT) curves commonly increased with dose whilst objective response (OR) curves were mostly invariant in dose
Summary
We sought to identify a broad sample of manuscripts reporting recent dose-finding clinical trials in oncology. Concerning outcomes, we extracted the dose-levels administered, the number of patients evaluated at each, and the number of DLT and objective response events recorded at each. These outcomes are explained and justified . The relative strength of the Emax model is that it allows the event probability to plateau at a value less than 1 It contains as a special case the scenario reflected by logit models where the event probability tends to 1 as dose is increased. We used both maximum likelihood and Bayesian approaches to fit Emax models. Data processing was aided using tidyverse [148] packages, posterior samples were extracted using tidybayes [149], and plots were produced using ggplot2 [150]
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