Complex Clinical Trial Design Research Articles

A longitudinal model for the Mayo Clinical Score and its sub-components in patients with ulcerative colitis

Clinical trials in patients with ulcerative colitis (UC) face the challenge of high and variable placebo response rates. The Mayo Clinical Score (MCS) is used widely as the primary endpoint in clinical trials to describe the clinical status of patients with UC. The MCS is comprised of four subscores, each scored 0, 1, 2 and 3: rectal bleeding (RB), stool frequency (SF), physician’s global assessment (PGA), and endoscopy (ENDO) subscore. Excluding the PGA subscore gives the modified MCS. Quantitative insight on the placebo response, and its impact on the components of the MCS over time, can better inform clinical trial design and interpretation. Longitudinal modeling of the MCS, and the modified MCS, can be challenging due to complex clinical trial design, population heterogeneity, and limited assessments for the ENDO subscore. The current study pooled patient-level placebo/standard of care (SoC) arm data from five clinical trials in the TransCelerate database to develop a longitudinal placebo response model that describes the MCS over time in patients with UC. MCS subscores were modeled using proportional odds models, and the removal of patients from the placebo/SoC arm, or “dropout”, was modeled using logistic regression models. The subscore and dropout models were linked to allow for the prediction of the MCS and the modified MCS. Stepwise covariate modeling identified prior exposure to TNF-α antagonists as a statistically significant predictor on the RB + SF subscore. Patients with prior exposure to TNF-α antagonists had higher post-baseline RB + SF subscores than naive patients.

The Evidence REVEAL Study: Exploring the Use of Real-World Evidence and Complex Clinical Trial Design by the European Pharmaceutical Industry.

The rapid evolution of science and technology allows innovative approaches to generate new types of evidence about the effectiveness of medical product development so as to speed up patients’ access to better diagnostics and treatment. Our study explored how two emerging approaches, the use of real‐world evidence (RWE) and complex clinical trial (CCT) design, are currently being used by the pharmaceutical industry to support premarketing authorization of medical product development and reviewed the international landscape for regulatory acceptance of such novel approaches. Combining evidence from a literature review, company survey, and interviews with international regulators and experts, we found that 80% of Europe‐based pharmaceutical companies have used RWE and 50% have used CCTs, in some capacity. Further, we present case examples of how companies are using these approaches and how international regulators are preparing for such developments. To conclude, we provide a set of recommendations for European industry and regulators to consider so that these novel approaches achieve their full potential within the EU regulatory system.

In Vitro Enzyme Measurement to Test Pharmacological Chaperone Responsiveness in Fabry and Pompe Disease.

The use of personalized medicine to treat rare monogenic diseases like lysosomal storage disorders (LSDs) is challenged by complex clinical trial designs, high costs, and low patient numbers. Hundreds of mutant alleles are implicated in most of the LSDs. The diseases are typically classified into 2 to 3 different clinical types according to severity. Moreover, molecular characterization of the genotype can help predict clinical outcomes and inform patient care. Therefore, we developed a simple cell culture assay based on HEK293H cells heterologously over-expressing the mutations identified in Fabry and Pompe disease. A similar assay has recently been introduced as a preclinical test to identify amenable mutations for Pharmacological Chaperone Therapy (PCT) in Fabry disease. This manuscript describes an amended cell culture assay which enables rapid phenotypic assessment of allelic variants in Fabry and Pompe disease to identify eligible patients for PCT and may aid in the development of novel pharmacochaperones.

Multiple comparisons in complex clinical trial designs

Multiple comparisons have drawn a great deal of attention in evaluation of statistical evidence in clinical trials for regulatory applications. As the clinical trial methodology is increasingly more complex to properly take into consideration many practical factors, the multiple testing paradigm widely employed for regulatory applications may not suffice to interpret the results of an individual trial and of multiple trials. In a large outcome trial, an increasing need of studying more than one dose complicates a proper application of multiple comparison procedures. Additional challenges surface when a special endpoint, such as mortality, may need to be tested with multiple clinical trials combined, especially under group sequential designs. Another interesting question is how to study mortality or morbidity endpoints together with symptomatic endpoints in an efficient way, where the former type of endpoints are often studied in only one single trial but the latter type of endpoints are usually studied in at least two independent trials. This article is devoted to discussion of insufficiency of such a widely used paradigm applying only per-trial based multiple comparison procedures and to expand the utility of the procedures to such complex trial designs. A number of viable expanded strategies are stipulated.

A Mixture Gatekeeping Procedure Based on the Hommel Test for Clinical Trial Applications

When conducting clinical trials with hierarchically ordered objectives, it is essential to use multiplicity adjustment methods that control the familywise error rate in the strong sense while taking into account the logical relations among the null hypotheses. This paper proposes a gatekeeping procedure based on the Hommel (1988) test, which offers power advantages compared to other p value-based tests proposed in the literature. A general description of the procedure is given and details are presented on how it can be applied to complex clinical trial designs. Two clinical trial examples are given to illustrate the methodology developed in the paper.

Projection of Power and Events in Clinical Trials with a Time-to-event Outcome

In 2005, Barthel, Royston, and Babiker presented a menu-driven Stata program under the generic name of ART (assessment of resources for trials) to calculate sample size and power for complex clinical trial designs with a time-to-event or binary outcome. In this article, we describe a Stata tool called ARTPEP, which is intended to project the power and events of a trial with a time-to-event outcome into the future given patient accrual figures so far and assumptions about event rates and other defining parameters. ARTPEP has been designed to work closely with the ART program and has an associated dialog box. We illustrate the use of ARTPEP with data from a phase III trial in esophageal cancer.

A Menu-driven Facility for Complex Sample Size Calculation in Randomized Controlled Trials with a Survival or a Binary Outcome: Update

Royston and Babiker (2002) presented a menu-driven Stata program for the calculation of sample size or power for complex clinical trial designs under a survival time or binary outcome. In the present article, the package is updated to Stata 8 under the new name ART. Furthermore, the program has been extended to incorporate noninferiority designs and provides more detailed output. This package is the only realistic sample size tool for survival studies available in Stata.

“It's harder than we thought it would be”: A comparative case study of expert–novice experimentation strategies

AbstractScientific inquiry is a complex skill. Aspiring physicians need to learn these skills so that they can be educated consumers of medical research as well as being collaborators in different kinds of clinical trials. But school science often fails to provide the kind of authentic tasks needed to help students develop appropriate reasoning skills and epistemological beliefs. In this study, we compared a group of expert cancer researchers with four groups of fourth year medical students (the “novice” groups) engaged in the task of designing a clinical trial to test a new cancer drug using a computer‐based modeling tool, the Oncology Thinking Cap. Although the experts and novices reached similar endpoints, their reasoning processes differed considerably. For the experts, this was a task that required learning about the drug they were testing. The novices needed to learn about designing clinical trials, particularly about how variables interacted with each other, as well as learning about the drug. One of the major lessons learned by the novice student groups was just how complex clinical trial design really is. © 2002 Wiley Periodicals, Inc. Sci Ed 86:219–243, 2002; DOI 10.1002/sce.10002