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Using a Dose-Finding Benchmark to Quantify the Loss Incurred by Dichotomisation in Phase II Dose-Ranging Studies

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<mark>Journal publication date</mark>1/11/2020
<mark>Journal</mark>Biometrical Journal
Issue number7
Number of pages13
Pages (from-to)1717-1729
Publication StatusPublished
Early online date11/06/20
<mark>Original language</mark>English


While there is recognition that more informative clinical endpoints can support better decision-making in clinical trials, it remains a common practice to categorise endpoints originally measured on a continuous scale. The primary motivation for this categorisation (and most commonly dichotomisation) is the simplicity of the analysis. There is, however, a long argument that this simplicity can come at a high cost. Specifically, larger sample sizes are needed to achieve the same level of accuracy when using a dichotomised outcome instead of the original continuous endpoint. The degree of “loss of information” has been studied in the contexts of parallel-group designs and two-stage Phase II trials. Limited attention, however, has been given to the quantification of the associated losses in dose ranging trials. In this work, we propose an approach to estimate the associated losses in Phase II dose ranging trials that is free of the actual dose ranging design
used and depends on the clinical setting only. The approach uses the notion of a non-parametric optimal benchmark for dose finding trials, an evaluation tool that facilitates the assessment of a dose finding design by providing an upper bound on its performance under a given scenario in terms of the probability of the target dose selection. After demonstrating how the benchmark can be applied to Phase II dose ranging trials,
we use it to quantify the dichotomisation losses. Using parameters from real clinical trials in various therapeutic areas, it is found that the ratio of sample sizes needed to obtain the same precision using continuous and binary (dichotomized) endpoints varies between 70%-75% under the majority of scenarios but can drop to 50% in some cases.