Randomized exposure-controlled trials; impact of randomization and analysis strategies

Abstract

Aims: In the literature, five potential benefits of randomizing clinical trials on concentration levels, rather than dose, have been proposed: (i) statistical study power will increase; (ii) study power will be less sensitive to high variability in the pharmacokinetics (PK); (iii) the power of establishing an exposure-response relationship will be robust to correlations between PK and pharmacodynamics (PD); (iv) estimates of the exposure-response relationship are likely to be less biased; and (v) studies will provide a better control of exposure in situations with toxicity issues. The main aim of this study was to investigate if these five statements are valid when the trial results are evaluated using a model-based analysis.

Methods: Quantitative relationships between drug dose, concentration, biomarker and clinical end-point were defined using pharmacometric models. Three randomization schemes for exposure-controlled trials, dose-controlled (RDCT), concentration-controlled (RCCT) and biomarker-controlled (RBCT), were simulated and analysed according to the models.

Results: (i) The RCCT and RBCT had lower statistical power than RDCT in a model-based analysis; (ii) with a model-based analysis the power for an RDCT increased with increasing PK variability; (iii) the statistical power in a model-based analysis was robust to correlations between CL and EC(50) or E(max); (iv) under all conditions the bias was negligible (<3%); and (v) for studies with equal power RCCT could produce either more or fewer adverse events compared with an RDCT.

Conclusion: Alternative randomization schemes may not have the proposed advantages if a model-based analysis is employed.

Figure 1

The model for the mechanistic pathway of drug response consists of: a pharmacokinetic (PK) model for the dose–exposure relationship, a pharmacokinetic biomarker model for the exposure–biomarker relationship and biomarker clinical end-point model for the biomarker–clinical end-point relationship and a pharmacokinetic toxicity (Tox) model for the exposure–toxicity relationship.

Figure 2

The effect of randomization and independent variable on statistical power under the default simulation setup with both wide and low dose ranges. The independent variable group indicates a traditional statistical analysis.

Figure 3

Effect of high variability in CL with a randomized dose-controlled trial (RDCT) design. The randomized concentration-controlled trial (RCCT) and randomized biomarker-controlled trial (RBCT) designs are not affected by the variability in CL. The independent variable group indicates a traditional statistical analysis.

Figure 4

The difference in number of adverse events vs. the size of therapeutic interval (TI = TC₅₀/EC₅₀) between a randomized dose-controlled trial (RDCT) with model-based analysis (MBA) with biomarker as independent variable and a randomized concentration-controlled trial (RCCT) with group-wise analysis. The graphs are conditioned on variability in (a) TC₅₀ (, 0 varCL; , 0.2 varCL; , 0.3 varCL; , 0.5 varCL; , 0 varTC50) and (b) CL (, 0 varTC50; , 0.3 varTC50; , 0.5 varTC50; , 0.75 varTC50; , 1 varTC50). The variability is noted as the CV.