Understanding MCP-MOD dose finding as a method based on linear regression

Graves-PCD: protocol for a randomised, dose-finding, adaptive trial of the plasma cell-depleting agent daratumumab in severe Graves' disease

INTRODUCTION

Severe Graves' disease is a life-changing condition with poor outcomes from currently available treatments. It is caused by directly pathogenic thyroid-stimulating hormone receptor-stimulating antibodies (TRAb), which are secreted from plasma cells. The human anti-CD38 monoclonal antibody daratumumab was developed to target plasma cells which express high levels of CD38, and is currently licensed for treatment of the plasma cell malignancy, myeloma. However, it can also deplete benign plasma cells with the potential to reduce TRAb and alter the natural history of severe Graves' disease. This study aims to establish proof of concept that daratumumab has efficacy in patients with severe Graves' disease and will provide important data to inform a choice of dosing regimen for subsequent trials.

METHODS AND ANALYSIS

The Graves-PCD trial aims to determine if daratumumab modulates the humoral immune response in patients with severe Graves' disease, and if so, over what time period, and to find an optimal dose. It is a single-blinded, randomised, dose-finding, adaptive trial using four different doses of daratumumab or placebo in 30 adult patients. Part 1 of the trial is dose-finding and, following an interim analysis, in part 2, the remaining patients will be randomised between the chosen dose(s) from the interim analysis or placebo. The primary outcome is the percentage change in serum TRAb from baseline to 12 weeks.

ETHICS AND DISSEMINATION

The trial received a favourable ethical opinion from London-Hampstead Research Ethics Committee (reference 21/LO/0449). The results of this trial will be disseminated at international meetings, in the peer-reviewed literature and through partner patient group newsletters and presentations at patient education events.

TRIAL REGISTRATION NUMBER

ISRCTN81162400.

Adaptive dose-response studies to establish proof-of-concept in learning-phase clinical trials

In learning-phase clinical trials in drug development, adaptive designs can be efficient and highly informative when used appropriately. In this article, we extend the multiple comparison procedures with modeling techniques (MCP-Mod) procedure with generalized multiple contrast tests (GMCTs) to two-stage adaptive designs for establishing proof-of-concept. The results of an interim analysis of first-stage data are used to adapt the candidate dose-response models and the dosages studied in the second stage. GMCTs are used in both stages to obtain stage-wise p -values, which are then combined to determine an overall p -value. An alternative approach is also considered that combines the t -statistics across stages, employing the conditional rejection probability principle to preserve the Type I error probability. Simulation studies demonstrate that the adaptive designs are advantageous compared to the corresponding tests in a nonadaptive design if the selection of the candidate set of dose-response models is not well informed by evidence from preclinical and early-phase studies.

The Tukey trend test: Multiplicity adjustment using multiple marginal models

In dose-response analysis, it is a challenge to choose appropriate linear or curvilinear shapes when considering multiple, differently scaled endpoints. It has been proposed to fit several marginal regression models that try sets of different transformations of the dose levels as explanatory variables for each endpoint. However, the multiple testing problem underlying this approach, involving correlated parameter estimates for the dose effect between and within endpoints, could only be adjusted heuristically. An asymptotic correction for multiple testing can be derived from the score functions of the marginal regression models. Based on a multivariate t-distribution, the correction provides a one-step adjustment of p-values that accounts for the correlation between estimates from different marginal models. The advantages of the proposed methodology are demonstrated through three example datasets, involving generalized linear models with differently scaled endpoints, differing covariates, and a mixed effect model and through simulation results. The methodology is implemented in an R package.

Generalized multiple contrast tests in dose-response studies

In the process of developing drugs, proof-of-concept studies can be helpful in determining whether there is any evidence of a dose-response relationship. A global test for this purpose that has gained popularity is a component of the multiple comparisons procedure with modeling techniques (MCP-Mod), which involves the specification of a candidate set of several plausible dose-response models. For each model, a test is performed for significance of an optimally chosen contrast among the sample means. An overall P-value is obtained from the distribution of the maximum of the contrast statistics. This is equivalent to basing the test on the minimum of the P-values arising from these contrast statistics and, hence, can be viewed as a method for combining dependent P-values. We generalize this idea to the use of different statistics for combining the dependent P-values, such as Fisher's combination method or the inverse normal combination method. Simulation studies show that the generalized multiple contrast tests (GMCTs) based on the Fisher and inverse normal methods are generally more powerful than the MCP-Mod procedure based on the minimum of the P-values except for cases where the true dose-response model is, in a sense, near the extremes of the candidate set of dose-response models. The proposed GMCTs can also be used for model selection and dosage selection by employing a closed testing procedure.