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There was a significant effect of probability of trial type prediction for five trial types (NALR, DALR, VDRL, NARL and DARL) in episodic replays (ANOVA, P < 0.01).
To perform an analysis for replays, I analyzed the probability of trial type prediction across all replays occurring at the junction on a time-window basis.
To simulate reporting bias affecting trials in a given meta-analysis, we considered a selection model that links the probability of trial selection to both trial size and intensity of treatment effect [ 9, 31, 32].
Secondly, to check whether matches between predicted and actual trial types were coincidental, I analyzed the probability of trial type prediction across all replays occurring at the junction on a time-window basis.
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To further check whether the predicted and actual trial types were coincidently matched, the probability of trial-type prediction across all replays occurring at the junction was analyzed on a time-window basis.
There was a significant effect of the probability of trial-type prediction for five trial types (NALR, DALR, VDRL, NARL, and DARL) in episodic replays (ANOVA, p < 0.01; Figure 7 figure supplement 3).
We further evaluated the performance of RI and other algorithms on binomial trees T where the number of children of each node follows a binomial distribution such that ξ is the number of trials and β is the success probability of each trial.
Let Y be the number of successes (replicates that yield a given node) out of n trials (replicates), and p the success probability of each trial.
Demonstrating dosing and delivery paradigms and safety of therapy in the canine disease models will provide key supportive data and improve the probability of clinical trial success.
How should the probability of a trial's given alpha error be adjusted to account for statistical significance?
Second, we introduced a selection model, which models the probability of a trial being selected and is taken into account with inverse weighting in the NMA.
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