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Authors:
Committee on Strategies for Small-Number-Participant Clinical Research Trials, Board on Health Sciences Policy

ABSTRACT
Scientific research has a long history of using well-established, well documented,
and validated methods for the design, conduct, and analysis of clinical
trials. A study design that is considered appropriate includes sufficient sample
size (n) and statistical power and proper control of bias to allow a meaningful
interpretation of the results. Whenever feasible, clinical trials should be designed
and performed so that they have adequate statistical power. However,
when the clinical context does not provide a sufficient number of research
participants for a trial with adequate statistical power but the research question
has great clinical significance, research can still proceed under certain conditions.
Small clinical trials might be warranted for the study of rare diseases,
unique study populations (e.g., astronauts), individually tailored therapies, in
environments that are isolated, in emergency situations, and in instances of
public health urgency. Properly designed trials with small sample sizes may
provide substantial evidence of efficacy and are especially appropriate in particular
situations. However, the conclusions derived from such studies may
require careful consideration of the assumptions and inferences, given the small
number of paticipants.
Bearing in mind the statistical power, precision, and validity limitations of
trials with small sample sizes, there are innovative design and analysis approaches
that can improve the quality of such trials. A number of trial designs
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especially lend themselves to use in studies with small sample sizes, including
one subject (n-of-1) designs, sequential designs, “within-subject” designs, decision
analysis-based designs, ranking and selection designs, adaptive designs,
and risk-based allocation designs. Data analysis for trials with small numbers
of participants in particular must be focused. In general, certain types of analyses
are more amenable to studies with small numbers of participants, including
sequential analysis, hierarchical analysis, Bayesian analysis, decision analysis,
statistical prediction, meta-analysis, and risk-based allocation.
Because of the constraints of conducting research with small sample sizes,
the committee makes recommendations in several areas: defining the research
question, tailoring the study design by giving careful consideration to alternative
methods, clarifying sample characteristics and methods for the reporting of
results of clinical trials with small sample sizes, performing corroborative analyses
to evaluate the consistency and robustness of the results of clinical trials
with small sample sizes, and exercising caution in the interpretation of the
results before attempting to extrapolate or generalize the findings of clinical
trials with small sample sizes. The committee also recommends that more research
be conducted on the development and evaluation of alternative experimental
designs and analysis methods for trials with small sample sizes.

Copyright © National Academy of Sciences.

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