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<h2 class="hd hd-2 unit-title">Balanced Two-Arm RCT</h2>
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<h2 class="hd hd-2 unit-title">Exercise: Balanced Two-Arm RCT</h2>
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Exercise: Balanced Two-Arm RCT
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Consider a balanced, two-arm randomized clinical trial (RCT) of a new treatment designed to reduce the risk of myocardial infarction (heart attack). Over the course of the study, 30% fewer patients died in the treatment group relative to the control group, and this difference was statistically significant at the 5% level. We can say with certainty that the treatment is effective.
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<h2 class="hd hd-2 unit-title">Exercise: Cost vs. Type I & Type II Errors</h2>
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Exercise: Cost vs. Type I &amp; Type II Errors
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Consider a small randomized clinical trial of a new treatment designed to reduce the risk of myocardial infarction (heart attack). Over the course of the study, 20% fewer patients died in the treatment group relative to the control group, but this difference was not statistically significant. If we are not constrained by costs, then we should test the new treatment in a larger clinical trial.
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<h2 class="hd hd-2 unit-title">Changing the Threshold c</h2>
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<h3 class="hd hd-2">Changing the Threshold c</h3>
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<h2 class="hd hd-2 unit-title">Formal Statistical Analysis</h2>
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<h2 class="hd hd-2 unit-title">Interactive: RCT Statistics</h2>
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<p style="text-align: left;">The graph below calculates the statistical power of a balanced, two-arm randomized clinical trial. What happens when the number of patients in the trial is increased? Notice the trade-off that exists between statistical power (type 2 error) and the statistical significance level (type 1 error).</p>
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<h2 class="hd hd-2 unit-title">Hypothesis Testing Review</h2>
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<h2 class="hd hd-2 unit-title">Alternative Hypothesis vs. Power</h2>
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<h3 class="hd hd-2">Example: Spark’s Luxturna</h3>
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