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The distributed trial design involves assigning psoriasis patients across a range of feasible and practical drug dose levels.
This work shows that, for a small decrease in the ability to make a correct G/NG decision, a distributed trial design provides D R learning that can provide advantage in Phase 2. A limitation of this work is that safety analysis was not included into decision making.
Distributed trial designs.
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Reichenbach's proposal is reminiscent of the law of large numbers, that the empirical distribution of a sequence of independent and identically distributed trials converges in probability to the true distribution.
But the law of large numbers depends on independent, identically distributed trials.
(Note: These examples employ "identically distributed" trials — repeated tosses of a coin — as an illustration. But Convergence Theorem 2 applies much more generally.
For any sequence of independent, identically distributed trials, it is possible for the average utility payoff per trial to diverge arbitrarily far from the expected utility of an individual trial.
The counterexamples to RCT offered in §§4.3 4.4 suggest strongly that the appeal of RCT depends on our curious tendency to take independent identically distributed trials, like the Bernoulli process of fair coin tossing, to be paradigms of chance processes.
For example, if a sequence is genuinely random, we should expect that in the long run it would tend to have features we associate with the outputs of (independent, identically distributed trials of) a chancy process.
This sequence, if long enough, is not KML-random, but it is unpredictable.[24] The left-to-right direction of CTU looks relatively secure when we attend just to independent and identically distributed trials.
Randomisation was at the level of the individual, hence potential confounders were equally distributed across trial groups despite the small number of participating practices.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com