Abstract

Despite of an extensive statistical literature showing that discretizing continuous variables results in substantial loss of information, categorization of continuous variables has been a common practice in clinical research and in cancer dose finding (phase I) clinical trials. The objective of this study is to quantify the loss of information incurred by using a discrete set of doses to estimate the maximum tolerated dose (MTD) in phase I trials, instead of a continuous dose support. Escalation With Overdose Control and Continuous Reassessment Method were used because they are model-based designs where dose can be specified either as continuous or as a set of discrete levels. Five equally spaced sets of doses with different interval lengths and three sample sizes with sixteen scenarios were evaluated to compare the operating characteristics between continuous and discrete dose designs by Monte Carlo simulation. Loss of information was quantified by safety and efficiency measures. We conclude that if there is insufficient knowledge about the true MTD value, as commonly happens in phase I clinical trials, a continuous dose scheme minimizes information loss. If one is required to implement a design using discrete doses, then a scheme with 9 to 11 doses may yield similar results to the continuous dose scheme.

Highlights

  • Measurements of continuous variables are made in all fields of medicine

  • Several authors [2,3,4,5,6,7] have pursued methodologies to provide optimal criteria of discretization for continuous variables based on test statistics

  • The results are evaluated based on median and quantiles over 16 scenarios (4 True maximum tolerated dose (MTD) × 4 True Distributions)

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Summary

Introduction

Measurements of continuous variables are made in all fields of medicine. In medical research such continuous variables are often converted into categorical variables by grouping values into two or more categories in order to have easier interpretations. Cox [1] presented the first optimization criterion for discretizing a continuous variable showing the minimum loss of information as a function of the number of categories. Several authors [2,3,4,5,6,7] have pursued methodologies to provide optimal criteria of discretization for continuous variables based on test statistics. Extensive statistical literature [8,9,10,11,12,13,14] has advised against categorization due the loss of power and precision of the estimated quantities.

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