Nasonex Treatment Analysis Nasonex is a nasal spray used to...

Nasonex Treatment Analysis Nasonex is a nasal spray used to treat allergies. In clinical trials, 1671 subjects were given a placebo, and 2 of them developed upper respiratory tract infections. Another 2103 patients were treated with Nasonex and 6 of them developed upper respiratory tract infections. Assume that Nasonex has no effect on upper respiratory tract infections so that the rate of those infections also applies to Nasonex users. Using the placebo rate of $2 / 1671,$ simulate groups of 2103 subjects given the Nasonex treatment, and determine whether a result of 6 upper respiratory tract infections could easily occur. What does that suggest about Nasonex as a cause of upper respiratory tract infections?

Key Concepts

Simulation

Simulation involves generating numerous random samples based on a specified distribution or model to understand the variability and likelihood of different outcomes. In clinical research, simulations help assess whether an observed result, like the number of infections in a treatment group, could reasonably occur by chance under the null hypothesis.

Null Hypothesis

The null hypothesis is the foundational assumption in hypothesis testing that there is no effect or no difference between two populations—in this case, that Nasonex does not affect the rate of upper respiratory tract infections. This concept underpins the evaluation of experimental data by setting a baseline expectation that any deviations observed are due to random chance rather than a true treatment effect.

Binomial Distribution

The binomial distribution is a probability distribution that models the number of successes in a fixed number of independent trials with the same probability of success. In the context of clinical trials, it is used to simulate and analyze the occurrence of events (such as infections) based on a given probability derived from a control or placebo group.

Statistical Significance

Statistical significance is a measure used to determine whether the observed outcome in an experiment is unlikely to have occurred by random chance alone. It involves comparing the actual data to what would be expected under the null hypothesis, and if the probability (p-value) of observing such an extreme result is very low, it may suggest that the treatment has a genuine effect.

Transcript

00:02 In this lesson, we would simulate this experiment.

00:07 So in the classical side, we have two out of 1671.

00:16 Then in the neson, the pazza, the subjects were given the lesson next, we had six, a proportion of various upper restrict three in function six out of two one zero three okay so we are using the rate of two over one six seven one so if we compare that to two one zero three we have about three out of two one zero three so this is what would do would have the random between using the xl the run between using the cell and we have one up to two one zero three okay so what we do is that those that are from one to three would be categorized as no infection uh those that are from one to three would be infections and those that are from four to two one zero three would be infections and those that are from four to 210 will be for no infection.

01:42 Okay, so we'll do that for 2103 times.

01:46 Then we'll count the number of infections that we've had.

01:50 Okay.

01:51 So let's drag through so we get that number.

02:07 A lot of stuff in there.

02:53 Okay, so that will be 2104 because one is ticking for the title.

03:00 Okay.

03:25 Okay, so let's look at the description.

03:28 This is what we do for the description.

03:35 Those that i use the if.

03:41 So if the number in section, if the number in section is less than, it's less than four.

04:22 If it's less than four, then we have infection, else we have no infection so this also goes through then we get the infections and all that the last step that will would have is to count to count the number of infections and let's us how we'll do we do use the count if method from excel then we divided by to 1 zero three to know the proportion...

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