Large-Simple Clinical Trial

A Large-Simple Clinical Trial is a clinical trial that involves a large number of participants, but requires less precision in clinical endpoints' estimation.

• Context:
  • It is (typically) used to study treatment effects that will affect a large portion of the population (e.g. heart disease treatment).
  • It can (often) be a multi-site clinical trial.
  • It can (typically) have a broad eligibility criteria.
  • It (typically) requires minimal data collection.
  • ...
• Example(s):
  • …
• Counter-Example(s):
  • Adaptive Clinical Trial,
  • Crossover Clinical Trial,
  • Factorial Clinical Trial,
  • Limited Clinical Investigation Program,
  • Parallel Clinical Trial.
• See: Clinical Trial Phase, Washout Period, Clinical Trial Dropout, Clinical Trial Group Allocation, Clinical Trial Randomization Unit, Clinical Trial Arm, Placebo-Controlled Clinical Trial, Equivalency Clinical Trial, Superiority Clinical Trial, Non-Inferiority Clinical Trial.

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References

2022

• (Coursera, 2021) ⇒ "Design and Interpretation of Clinical Trials" (lecture notes, adaptation).
  • QUOTE: Large-simple designs are exactly as they sound. They are large, and they are simple. They are characterized by a very large number of patients, usually numbering in the tens of thousands. They are recruited from many, many centers. For instance, in ISIS-3 study we saw that they had over 900 centers, and they require minimal data collection on each participant.

    The rationale for a large, simple trial is that it takes large sample sizes to detect modest benefit. We don't find treatments that have effects that are so large that we can see the effect by only observing a small number of people. If we are looking for a treatment that provides a small survival benefit for those with heart disease, for instance, we need a large sample size to detect it. Why would we be interested in a small clinical effect? Well, heart disease is a very common condition, so small advancements in treatment that improve the survival time by even a small amount can correspond to a large public health benefit(...)

    In a large, simple design, we tolerate less precision in estimation. With such a heterogeneous group of participants and study personnel, we'll have a wide variety of people enrolled. We'll have less control over the training and standardization of administration of treatment and of outcome assessment, so we have to expect that there will be more error or increased variance in the estimation of the outcome measures. And we counter this increase in error with a large sample size.

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