Tradeoff-based optimization criteria in clinical trials with multiple objectives and adaptive designs

An enhanced mixture method for constructing gatekeeping procedures in clinical trials

It is increasingly common to encounter complex multiplicity problems with several multiplicity components in confirmatory Phase III clinical trials. These components are often based on several endpoints (primary and secondary endpoints) and several dose-control comparisons. When constructing a multiplicity adjustment in these settings, it is important to control the Type I error rate over all multiplicity components. An important class of multiple testing procedures, known as gatekeeping procedures, was derived using the mixture method that enables clinical trial sponsors to set up efficient multiplicity adjustments that account for clinically relevant logical relationships among the hypotheses of interest. An enhanced version of this mixture method is introduced in this paper to construct more powerful gatekeeping procedures for a specific type of logical relationships that rely on transitive serial restrictions. Restrictions of this kind are very common in Phase III clinical trials and the proposed method is applicable to a broad class of multiplicity problems. Several examples are provided to illustrate the new method and results of simulation trials are presented to compare the performance of gatekeeping procedures derived using this method and other available methods.

Penalty-based approaches to evaluating multiplicity adjustments in clinical trials: Traditional multiplicity problems

Given the importance of addressing multiplicity issues in confirmatory clinical trials, several recent publications focused on the general goal of identifying most appropriate methods for multiplicity adjustment in each individual setting. This goal can be accomplished using the Clinical Scenario Evaluation approach. This approach encourages trial sponsors to perform comprehensive assessments of applicable analysis strategies such as multiplicity adjustments under all plausible sets of statistical assumptions using relevant evaluation criteria. This two-part paper applies a novel class of criteria, known as criteria based on multiplicity penalties, to the problem of evaluating the performance of several candidate multiplicity adjustments. The ultimate goal of this evaluation is to identify efficient and robust adjustments for each individual trial and optimally select parameters of these adjustments. Part I deals with traditional problems with a single source of multiplicity. Two case studies based on recently conducted Phase III trials are used to illustrate penalty-based approaches to evaluating candidate multiple testing methods and constructing optimization algorithms.

Using Contour Plots to Assess the Sensitivity of Clinical Trial Design Assumptions

BACKGROUND

Sample size calculations are an important part of the design of any clinical trial. These calculations ensure a sufficient number of patients to detect a clinically meaningful difference between 2 treatments with high probability (ie, power). Perhaps less-often discussed, the sample size exercise is important so that resources are not wasted studying too many observations to test a particular hypothesis. Since patients may be randomized to doses of a novel treatment with a limited safety profile, or a placebo which provides no therapeutic benefit, sample size calculations in clinical trials come with an ethical burden not experienced in many subject-matter areas.

METHODS

The sample size of a clinical trial should be determined using as much data as is available, over a range of assumptions, and with input from clinical colleagues. Graphical techniques are often utilized to summarize power and sample size calculations. In this manuscript, we propose the use of contour plots to better assess, report, and communicate the sensitivity of clinical trial design assumptions.

RESULTS

Through several examples, we illustrate that contour plots are applicable to binary, continuous, and time-to-event endpoints for a variety of study design scenarios.

CONCLUSIONS

Contour plots are a useful tool for the study team in designing clinical trials, and they can be included in study documents to better communicate the rationale for sample size for clinicians and regulators. Contour plots provide greater transparency as to the uncertainty of the currently available information, and can be useful in deciding whether to consider adaptive designs.