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Acetaminophen and Asthma Does frequent use of acetaminophen lead to asthma- related complications among children? Excerpts from the abstract of a study published in The New England Journal of Medicine about this are given (Sheehan et al. 2016 ). Read them and then answer the questions that follow. Methods: In a randomized, double-blind, parallel-group trial, we enrolled 300 children (age range, \(12-59\) months) with mild persistent asthmas and assigned them to receive either acetaminophen or ibuprofen when needed for the alleviation of fever or pain over the course of 48 weeks. The primary outcome was the number of asthma exacerbations that let to treatment with systemic glucocorticoids. Results: The number of asthmas exacerbations did not differ significantly between the two groups, with a mean of \(0.81\) per participant with acetaminophen and \(0.87\) per participant in the ibuprofen group \((p=0.67)\) a. Identify the treatment variable and the response variable. b. Was this a controlled experiment or an observational study? c. How does the p-value support the conclusion of the study? d. Did this study use random sampling, random assignment, or both?

Step by step solution

01

Identification of Variables

The treatment variable in this study is the use of either acetaminophen or ibuprofen by the children. It's what is being tested to see if it has an effect on the response variable. The response variable, in turn, is the number of asthma exacerbations that lead to treatment with systemic glucocorticoids. This is the outcome that is being measured.

02

Determining the Type of Study

This was a controlled experiment. The key indication is the use of randomization in assigning participants to either the acetaminophen or ibuprofen groups. Unlike an observational study, an experiment involves controlled manipulations by the experimenters in order to better isolate the effects of the treatment variable on the response variable.

03

Interpreting the P-Value

The p-value is a measure of the likelihood that the observed results could have happened by mere chance under the assumption of no real effect. Here, the p-value (p=0.67) is large - far larger than the traditional cut-off of 0.05 - which indicates a lack of strong evidence against the null hypothesis (the hypothesis stating that there's no difference between the two groups). In other words, the p-value supports the study's conclusion that the number of asthma exacerbations did not differ significantly between the two groups.

04

Distinguishing Sampling and Assignment Methods

This study used both random sampling and random assignment. The children were enrolled in the study (which suggests random sampling) and then assigned to receive either acetaminophen or ibuprofen (indicating random assignment). Random sampling helps to ensure that the sample is representative of the population, while random assignment serves to equally distribute potential confounding variables among the treatment groups.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Random Assignment

Random assignment is a fundamental principle used in controlled experiments like the one conducted on acetaminophen and asthma. It involves assigning participants to different groups purely by chance, which helps ensure that each participant has an equal opportunity of being placed in any group. This method aims to distribute potential confounding factors evenly between treatment groups.
In this study, children were randomly assigned to receive either acetaminophen or ibuprofen. This process helps isolate the effect of the treatment variable (which was the type of medicine) on the response variable (number of asthma exacerbations). By using random assignment, researchers minimize bias and make it more likely that differences between groups are due to the treatment itself rather than pre-existing differences among participants.

Treatment Variable

The treatment variable is the factor that researchers manipulate in an experiment to observe its effect on the outcome. In the acetaminophen study, the treatment variable was the type of medication administered to children – either acetaminophen or ibuprofen.
It is important to clearly define the treatment variable because it is the independent variable that researchers believe may cause changes in the dependent variable, or outcome. By comparing asthma exacerbations between the acetaminophen and ibuprofen groups, researchers aim to identify if one treatment is more effective or shows different impacts.
Careful selection and control of the treatment variable help in drawing valid conclusions about its effects.

Response Variable

In any experiment, the response variable is the main outcome that researchers are interested in measuring. It reflects the result of the experiment and is influenced by the treatment variable. For the acetaminophen study, the response variable was the number of asthma exacerbations that required treatment with systemic glucocorticoids.
This variable serves as the benchmark to assess the efficacy or impact of the treatment variable. Researchers do not control the response variable; rather, they measure it to determine the result of the manipulation of the treatment variable. Understanding the response variable is crucial for interpreting the results and drawing conclusions from the study.

P-Value Interpretation

The p-value is a statistical measure that helps determine the significance of the results in an experiment. It provides a probability estimate that the observed results could have occurred under the null hypothesis, which typically states that there is no effect or difference present.
In the study in question, the p-value was 0.67, which is considerably larger than the conventional threshold of 0.05.
Interpretation of this means there is a high probability that the results could occur due to random chance, under the assumption that there is no actual difference between the use of acetaminophen and ibuprofen on asthma exacerbations.
Thus, a high p-value like 0.67 suggests that the study provides weak evidence against the null hypothesis, supporting the conclusion that no significant difference was found between the two treatment groups.