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In the evaluation step, we use the K-Nearest Neighbor (KNN) algorithm [47] to assign a class score to a test trend q.
For a test trend, the class score is given by the fraction of promoted trends among the (K=5) nearest neighbors.
Via simulation, the rate of correct and incorrect MED identification are compared for Dunnett's test, trend test and MCP-Mod for a variety of underlying dose response profiles including both monotone and non-monotone dose responses and are compared under a large number of trial design settings.
* 2-sided chi-square test trend significant p <.001.
*Fisher's exact test, Trend = Chi-squared test for trend.
† 2-sided chi-square test trend significant p <.05 * 2-sided chi-square test trend significant p <.001.
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So test trends will never unfailingly indicate proficiency changes.
Logistic regression was used to test trends.
We can also test trends across time.
The Cochran-Armitage trend test was used to test trends across survey periods.
Post-test trend analysis revealed a statistically significant linear trend for skill growth across time, i.e., course year (p = 0.0022).
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