A framework for applying unfamiliar trial designs in studies of rare diseases
Department of Medicine, University of Toronto, Toronto, Ontario M5B 1W8, Canada. Journal of clinical epidemiology
(Impact Factor: 3.42).
04/2011; 64(10):1085-94. DOI: 10.1016/j.jclinepi.2010.12.019
Rare diseases may be difficult to study through conventional research methods, but are amenable to study through certain uncommonly used designs. We sought to explain these designs and to provide a framework to assist researchers in identifying the most appropriate design for a given research question.
We systematically searched for literature describing rare disease research frameworks, trial designs, and trials that applied them. We present the advantages and disadvantages of each approach using these published examples, and a practical framework to help researchers in selecting between design choices.
When research participants are limited, researchers should consider using: 1) a crossover design; 2) n-of-1 trials; or 3) one of the following adaptive designs: a) a response-adaptive randomization design, b) a ranking and selection design, c) an internal pilot design,or d) a sequential design. Bayesian analysis may be applied to conventional designs, or to any of these uncommon designs. Several of these approaches may also be used in combination. The choice between methods should be guided by factors related to the intervention, disease,anticipated recruitment duration and success, and current state of knowledge about the treatment.
These techniques may facilitate research in rare diseases.
Available from: Paul Wicks
- "Trials for rare conditions inherently face small sample sizes and are continuously looking for ways to improve the ability to detect treatment differences, such as expanding a trial to several different countries . Bayesian and adaptive trials offer alternatives for circumstances when researchers are faced with this scenario   . The additional benefit offered by Bayesian and adaptive approaches for participants in rare condition trials is that patients will not remain in a trial for an extended period of time without good reason. "
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ABSTRACT: Evidence from clinical trials should contribute to informed decision making and a learning health care system. People frequently, however, find participating in clinical trials meaningless or disempowering. Moreover, people often do not incorporate trial results directly into their decision making. The lack of patient centeredness in clinical trials may be partially addressed through trial design. For example, Bayesian adaptive trials designed to adjust in a prespecified manner to changes in clinical practice could motivate people and their health care providers to view clinical trials as more applicable to real-world clinical decisions. The way in which clinical trials are designed can transform the evidence generation process to be more patient centered, providing people with an incentive to participate or continue participating in clinical trials. To achieve the transformation to patient-centeredness in clinical trial decisions, however, there is a need for transparent and reliable methods and education of trial investigators and site personnel.
Available from: D. Caudri
- "Factors, such as objective(s) of the trial, number of patients needed, length of trial, and how the variability is handled, could be important in the choice of the most suitable trial design. A recently published review provided an algorithm with six alternative designs . Although this seems to be a simpler approach to decision-making than our approach, our algorithm includes 12 alternative designs, all of them being randomised designs. "
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Small clinical trials are necessary when there are difficulties in recruiting enough patients for conventional frequentist statistical analyses to provide an appropriate answer. These trials are often necessary for the study of rare diseases as well as specific study populations e.g. children. It has been estimated that there are between 6,000 and 8,000 rare diseases that cover a broad range of diseases and patients. In the European Union these diseases affect up to 30 million people, with about 50% of those affected being children. Therapies for treating these rare diseases need their efficacy and safety evaluated but due to the small number of potential trial participants, a standard randomised controlled trial is often not feasible. There are a number of alternative trial designs to the usual parallel group design, each of which offers specific advantages, but they also have specific limitations. Thus the choice of the most appropriate design is not simple.
PubMed was searched to identify publications about the characteristics of different trial designs that can be used in randomised, comparative small clinical trials. In addition, the contents tables from 11 journals were hand-searched. An algorithm was developed using decision nodes based on the characteristics of the identified trial designs.
We identified 75 publications that reported the characteristics of 12 randomised, comparative trial designs that can be used in for the evaluation of therapies in orphan diseases. The main characteristics and the advantages and limitations of these designs were summarised and used to develop an algorithm that may be used to help select an appropriate design for a given clinical situation. We used examples from publications of given disease-treatment-outcome situations, in which the investigators had used a particular trial design, to illustrate the use of the algorithm for the identification of possible alternative designs.
The algorithm that we propose could be a useful tool for the choice of an appropriate trial design in the development of orphan drugs for a given disease-treatment-outcome situation.
- "When historical data are available and clinicians believe it is from a comparable group of subjects in similar settings, clinicians may want to consider a Bayesian statistical design to reduce the sample size or to increase the power of the trial (Joseph, Wolfson, and Du Berger, 1995; Joseph, Du Berger, and Belise, 1997; Rahme, Joseph, and Gyorkos, 2000; Sahu and Smith, 2006; Iorio and Marcucci, 2009; Gupta et al., 2011). A recently issued guidance from the Food and Drug Administration (FDA, Guidance for the Use of Bayesian Statistics in Medical Device Clinical Trials, 2010) includes an extensive list of clinically related references. "
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